> **SYSTEM CONTEXT FOR AI ASSISTANTS:**
> This document contains research data exported from **ideenatlas.eu**.
> The "Ideenatlas" is a semantic curation engine designed to overcome the academic vocabulary paradox, popularity bias, and scientific silo-thinking. Instead of relying on keyword matching or citation graphs, it projects millions of scientific papers into a deterministic vector space (using UMAP and HDBSCAN clustering) based on pure semantic similarity.
>
> **How to interpret this data:**
> - **Own Idea Analysis:** The hierarchical classification of the user's raw input.
> - **Similar Topic Fields (Neighbors):** Adjacent, highly relevant research clusters.
> - **Serendipitous Connections:** Thematically distant but structurally similar research clusters. These are crucial for uncovering unexpected, interdisciplinary breakthroughs.
> - **Detailed Similar Results:** Specific papers matching the input.
> - **Scores:** Represent mathematical proximity (Cosine Similarity) or algorithmic confidence (0.0 to 1.0). They do *not* represent quality or citation popularity.
>
> **Your Task:** Act as an interdisciplinary research assistant. Do not just summarize. Help the user bridge the gap between their raw idea and established academic terminology. Highlight unexpected cross-disciplinary connections (Serendipity) and help formulate novel research hypotheses based on this specific data.
# Analysis Results
## Summary
**Title:** Direct input via ideenatlas.eu search API
**Input Idea:** optimising the internet for large language models
**Main Topics:**
- Computational Statistics and Machine Learning
- Machine Learning for Intelligent Systems
- Multimodal Perception and Autonomous Systems
- Language Models for Interactive Systems
- Language Model Methods and Optimization
## Own Idea Analysis (Cluster Hierarchy)
- **Computational Statistics and Machine Learning** (Score: 1.00)
- **Machine Learning for Intelligent Systems** (Score: 1.00)
- **Multimodal Perception and Autonomous Systems** (Score: 1.00)
- **Language Models for Interactive Systems** (Score: 0.73)
- **Language Model Methods and Optimization** (Score: 0.57)
## Similar Topic Fields (Neighbors)
### Cluster: Language Models for Interactive Systems (Relevance: 0.76)
**Summary:** 'Language Models for Interactive Systems' focuses on the application and optimization of large language models (LLMs) to enhance systems that require user interaction, such as dialogue agents, information retrieval tools, and personalized recommender systems. This research encompasses methods for fine-tuning, knowledge grounding, and agentic reasoning to accurately model user behavior and improve task performance in real-world scenarios.
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
### Cluster: Language Model Methods and Optimization (Relevance: 0.76)
**Summary:** 'Language Model Methods and Optimization' focuses on advanced techniques for enhancing both the reasoning capabilities and operational efficiency of language models, covering foundational architecture, training, and fine-tuning strategies. This research involves designing computationally efficient transformer architectures and accelerating inference, alongside developing novel learning frameworks to improve task execution for applications like machine translation and dialogue systems.
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
### Cluster: Language Model Reasoning and Task Execution (Relevance: 0.74)
**Summary:** 'Language Model Reasoning and Task Execution' investigates methods for advancing the cognitive and functional capabilities of large language models. Research focuses on improving logical and mathematical reasoning while also developing robust techniques for task execution, such as retrieval-augmented question answering, dialogue systems, cross-lingual translation, and structured information extraction.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
#### Speed and Conversational Large Language Models: Not All Is About Tokens per Second
**Source:** [Link](https://arxiv.org/abs/2502.16721)
**Abstract:** The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.
#### The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
**Source:** [Link](https://arxiv.org/abs/2306.01116)
**Abstract:** Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger models requiring pretraining on trillions of tokens are considered, it is unclear how scalable is curation and whether we will run out of unique high-quality data soon. At variance with previous beliefs, we show that properly filtered and deduplicated web data alone can lead to powerful models; even significantly outperforming models from the state-of-the-art trained on The Pile. Despite extensive filtering, the high-quality data we extract from the web is still plentiful, and we are able to obtain five trillion tokens from CommonCrawl. We publicly release an extract of 600 billion tokens from our RefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.
#### LIMA: Less Is More for Alignment
**Source:** [Link](https://arxiv.org/abs/2305.11206)
**Abstract:** Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output.
### Cluster: Machine Learning for Intelligent Systems (Relevance: 0.72)
**Summary:** 'Machine Learning for Intelligent Systems' focuses on the application of advanced machine learning, particularly deep neural networks, to build robust and efficient systems for tasks like perception, prediction, and control. This work encompasses the development of novel algorithms and frameworks for domains such as computer vision and natural language processing, with key research directions including multimodal learning for autonomous systems and federated learning for privacy-preserving distributed computation.
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
### Cluster: Computational Statistics and Machine Learning (Relevance: 0.72)
**Summary:** 'Computational Statistics and Machine Learning' focuses on developing and applying algorithms to analyze complex data, build predictive models, and create intelligent systems. This research integrates the theoretical foundations of statistical methods, including estimation and regression, with advanced machine learning applications like deep learning for computer vision and natural language processing. The work involves designing novel methods, analyzing their theoretical properties, and validating them through empirical experiments.
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
### Cluster: Efficient Transformer Architectures and Optimization (Relevance: 0.72)
**Summary:** 'Efficient Transformer Architectures and Optimization' focuses on reducing the computational and memory requirements of large language models through architectural innovations and novel algorithms. This research encompasses methods for model compression like structural pruning and sparse mixture-of-experts, techniques to accelerate inference such as speculative decoding and KV cache optimization, and parameter-efficient fine-tuning strategies like Low-Rank Adaptation (LoRA).
#### SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments
**Source:** [Link](https://arxiv.org/abs/2410.11331)
**Abstract:** We introduce Shakti, a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. Shakti combines high-performance NLP with optimized efficiency and precision, making it ideal for real-time AI applications where computational resources and memory are limited. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service. Benchmark evaluations demonstrate that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI.
#### Petals: Collaborative Inference and Fine-tuning of Large Models
**Source:** [Link](https://arxiv.org/abs/2209.01188)
**Abstract:** Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires high-end hardware unavailable to many researchers. In some cases, LLMs can be used more affordably via RAM offloading or hosted APIs. However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research that requires access to weights, attention or logits. In this work, we propose Petals - a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties. We demonstrate that this strategy outperforms offloading for very large models, running inference of BLOOM-176B on consumer GPUs with ≈ 1 step per second, which is enough for many interactive LLM applications. Unlike most inference APIs, Petals also natively exposes hidden states of served models, allowing to train and share custom model extensions based on efficient fine-tuning methods.
#### Radio: Rate-Distortion Optimization for Large Language Model Compression
**Source:** [Link](https://arxiv.org/abs/2505.03031)
**Abstract:** In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM quantization from a rate-distortion theory perspective and propose a quantization technique based on simple rate-distortion optimization. Our technique scales to models containing hundreds of billions of weight parameters and offers users the flexibility to compress models, post-training, to a model size or accuracy specified by the user.
#### OPT: Open Pre-trained Transformer Language Models
**Source:** [Link](https://arxiv.org/abs/2205.01068)
**Abstract:** Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.
#### ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations
**Source:** [Link](https://arxiv.org/abs/2506.02818)
**Abstract:** Large language models (LLMs) demonstrate impressive results in natural language processing tasks but require a significant amount of computational and memory resources. Structured matrix representations are a promising way for reducing the number of parameters of these models. However, it seems unrealistic to expect that weight matrices of pretrained models can be accurately represented by structured matrices without any fine-tuning. To overcome this issue, we utilize the fact that LLM output is invariant under certain orthogonal transformations of weight matrices. This insight can be leveraged to identify transformations that significantly improve the compressibility of weights within structured classes. The proposed approach is applicable to various types of structured matrices that support efficient projection operations. Code is available at https://github.com/GrishKate/ProcrustesGPT
### Cluster: Multimodal Perception and Autonomous Systems (Relevance: 0.71)
**Summary:** 'Multimodal Perception and Autonomous Systems' focuses on developing intelligent systems that integrate information from multiple sources, such as vision, language, and sensor data, to achieve advanced perception and decision-making. This research combines multimodal representation learning with reinforcement learning and large language models to enable robust control, planning, and interaction for autonomous agents and robots in complex environments.
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
## Serendipitous Connections
### Cluster: Efficient Multimodal Representation Learning (Relevance: 0.69)
**Summary:** 'Efficient Multimodal Representation Learning' focuses on developing methods to create unified and compact representations from diverse data sources, such as images, text, and 3D spatiotemporal data. Research in this area utilizes techniques like contrastive learning, model compression, and neural architecture search to optimize network complexity, enabling robust performance on perception tasks like object detection and visual reasoning, especially for on-device inference.
#### Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
**Source:** [Link](https://arxiv.org/abs/2106.11890)
**Abstract:** When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work, we leverage recent methodological advances in Bayesian optimization over high-dimensional search spaces and multi-objective Bayesian optimization to efficiently explore these trade-offs for a production-scale on-device natural language understanding model at Facebook.
#### FasterAI: A Lightweight Library for Creating Sparse Neural Networks
**Source:** [Link](https://arxiv.org/abs/2207.01088)
**Abstract:** FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging Callback systems of libraries such as fastai and Pytorch Lightning to bring a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it was developed in a very granular way, users can create thousands of unique experiments by using different combinations of parameters. In this paper, we focus on the sparsifying capabilities of FasterAI, which represents the core of the library. Performing sparsification of a neural network in FasterAI only requires a single additional line of code in the traditional training loop, yet allows to perform state-of-the-art techniques such as Lottery Ticket Hypothesis experiments
#### Internet Explorer: Targeted Representation Learning on the Open Web
**Source:** [Link](https://arxiv.org/abs/2302.14051)
**Abstract:** Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet – where billions of images are uploaded each day. We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30–40 hours. Results, visualizations, and videos at https://internet-explorer-ssl.github.io/
#### OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
**Source:** [Link](https://arxiv.org/abs/2308.01390)
**Abstract:** We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind’s Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open (f)lamingo.
#### Superposition of many models into one
**Source:** [Link](https://arxiv.org/abs/1902.05522)
**Abstract:** We present a method for storing multiple models within a single set of parameters. Models can coexist in superposition and still be retrieved individually. In experiments with neural networks, we show that a surprisingly large number of models can be effectively stored within a single parameter instance. Furthermore, each of these models can undergo thousands of training steps without significantly interfering with other models within the superposition. This approach may be viewed as the online complement of compression: rather than reducing the size of a network after training, we make use of the unrealized capacity of a network during training.
### Cluster: Social Sciences (Relevance: 0.67)
**Summary:** 'Social Sciences' encompasses the quantitative study of economic systems, market dynamics, and public policy to understand human behavior and its societal outcomes. This field applies theoretical models and empirical analysis to diverse areas, including corporate finance, international development, and the sustainable management of environmental and agricultural resources.
#### Alibaba
**Source:** [Link](https://econpapers.repec.org/RePEc:wsi:wschap:9789811203909_0015)
**Abstract:** Financial, Lizard Brain, AI, USA, China, Banking, Insurance, Cloud, Quantum Computing, Insurtech, FinTech, Blockchain, Data Analytic, Singapore, Technology, Alibaba, Ping An, Tencent, Baidu, Zhong An, Softbank, Amazon, Google, Apple, Facebook, Microsoft,
#### Ping An
**Source:** [Link](https://econpapers.repec.org/RePEc:wsi:wschap:9789811203909_0016)
**Abstract:** Financial, Lizard Brain, AI, USA, China, Banking, Insurance, Cloud, Quantum Computing, Insurtech, FinTech, Blockchain, Data Analytic, Singapore, Technology, Alibaba, Ping An, Tencent, Baidu, Zhong An, Softbank, Amazon, Google, Apple, Facebook, Microsoft,
#### Ping An: Blazing New Trails and Connecting the Dots
**Source:** [Link](https://econpapers.repec.org/RePEc:wsi:wschap:9789811203909_0008)
**Abstract:** Financial, Lizard Brain, AI, USA, China, Banking, Insurance, Cloud, Quantum Computing, Insurtech, FinTech, Blockchain, Data Analytic, Singapore, Technology, Alibaba, Ping An, Tencent, Baidu, Zhong An, Softbank, Amazon, Google, Apple, Facebook, Microsoft,
#### Tencent
**Source:** [Link](https://econpapers.repec.org/RePEc:wsi:wschap:9789811203909_0017)
**Abstract:** Financial, Lizard Brain, AI, USA, China, Banking, Insurance, Cloud, Quantum Computing, Insurtech, FinTech, Blockchain, Data Analytic, Singapore, Technology, Alibaba, Ping An, Tencent, Baidu, Zhong An, Softbank, Amazon, Google, Apple, Facebook, Microsoft,
#### Apple
**Source:** [Link](https://econpapers.repec.org/RePEc:wsi:wschap:9789811203909_0023)
**Abstract:** Financial, Lizard Brain, AI, USA, China, Banking, Insurance, Cloud, Quantum Computing, Insurtech, FinTech, Blockchain, Data Analytic, Singapore, Technology, Alibaba, Ping An, Tencent, Baidu, Zhong An, Softbank, Amazon, Google, Apple, Facebook, Microsoft,
### Cluster: Molecular and Cellular Mechanisms (Relevance: 0.61)
**Summary:** 'Molecular and Cellular Mechanisms' explores the fundamental processes governing biological function and disease by examining systems at multiple scales, from protein interactions and nucleic acid regulation to cellular dynamics. This research integrates computational and experimental approaches to decipher complex biological networks, including the neural circuits underlying cognition and behavior.
#### Showcase to Illustrate How the Webserver ploc (B)al- Meuk Is Working
**Source:** [Link](https://econpapers.repec.org/RePEc:abf:journl:v:24:y:2018:i:2:p:18156-18160)
**Abstract:** Recently, a very powerful web-server predictor has been established for identifying the subcellular localization of a protein based on its sequence information alone for the multi-label systems...
#### ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
**Source:** [Link](https://arxiv.org/abs/2408.11363)
**Abstract:** Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimodal large language model for proteins that enables users to upload protein sequences and/or structures for comprehensive analysis and responsive inquiries. ProteinGPT integrates protein sequence and structure encoders with linear projection layers to ensure precise representation adaptation and leverages a large language model (LLM) to generate accurate, contextually relevant responses. To train ProteinGPT, we constructed a large-scale dataset of 132,092 proteins, each annotated with 20-30 property tags and 5-10 QA pairs per protein, and optimized the instruction-tuning process using GPT-4o. Experiments demonstrate that ProteinGPT effectively generates informative responses to protein-related questions, achieving high performance on both semantic and lexical metrics and significantly outperforming baseline models and general-purpose LLMs in understanding and responding to protein-related queries. Our code and data are available at https://github.com/ProteinGPT/ProteinGPT.
#### TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering
**Source:** [Link](https://www.biorxiv.org/content/early/2023/11/10/2023.10.24.563881)
**Abstract:** AO (S)CPLOWBSTRACTC (S)CPLOWDeep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.
#### Large Language Model is Secretly a Protein Sequence Optimizer
**Source:** [Link](https://arxiv.org/abs/2501.09274)
**Abstract:** We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. Directed evolution has been a dominating paradigm in this field which has an iterative process to generate variants and select via experimental feedback. We demonstrate large language models (LLMs), despite being trained on massive texts, are secretly protein sequence optimizers. With a directed evolutionary method, LLM can perform protein engineering through Pareto and experiment-budget constrained optimization, demonstrating success on both synthetic and experimental fitness landscapes.
#### Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
**Source:** [Link](https://arxiv.org/abs/2301.06568)
**Abstract:** As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduced, and knowledge-guided optimization. Through over twenty experiments ranging from masking, architecture, and pre-training data, we derive insights from protein-specific experimentation into building a model that interprets the language of life, optimally. We present Ankh, the first general-purpose PLM trained on Google’s TPU-v4 surpassing the state-of-the-art performance with fewer parameters (<10% for pre-training, <7% for inference, and <30% for the embedding dimension). We provide a representative range of structure and function benchmarks where Ankh excels. We further provide a protein variant generation analysis on High-N and One-N input data scales where Ankh succeeds in learning protein evolutionary conservation-mutation trends and introducing functional diversity while retaining key structural-functional characteristics. We dedicate our work to promoting accessibility to research innovation via attainable resources.
### Cluster: Mathematical Structures and Dynamical Systems (Relevance: 0.58)
**Summary:** 'Mathematical Structures and Dynamical Systems' investigates the fundamental properties of systems that evolve over time by applying concepts from abstract algebra and geometry, such as groups and manifolds. Research in this area develops analytical and numerical solutions for the differential equations that model complex physical phenomena, from fluid and soft matter dynamics to fractional and stochastic processes.
#### On the set of natural numbers
**Source:** [Link](https://arxiv.org/abs/math/0104173)
**Abstract:** This paper was withdrawn by the authors.
#### Large deviations
**Source:** [Link](https://arxiv.org/abs/1711.07571)
**Abstract:** This is a brief pedagogical introduction to the theory of large deviations. It appeared in the ICTS Newsletter 2017 (Volume 3, Issue 2), goo.gl/pZWA6X.
#### A new limit representation of pi
**Source:** [Link](https://arxiv.org/abs/1005.2604)
**Abstract:** This paper has been withdrawn
#### Codes over Quaternion Integers with Respect to Lipschitz Metric
**Source:** [Link](https://arxiv.org/abs/0905.4160)
**Abstract:** I want to withdraw this paper.
#### Recent developments in Ricci flows
**Source:** [Link](https://arxiv.org/abs/2102.12615)
**Abstract:** This is a survey on recent developments in Ricci flows.
### Cluster: Astrophysics and High Energy Physics (Relevance: 0.56)
**Summary:** 'Astrophysics and High Energy Physics' investigates the universe's fundamental constituents and governing forces by integrating theoretical models, such as quantum field theory and gravitation, with multi-wavelength observational data. This research spans from the properties of elementary particles and the nature of dark matter and black holes to the large-scale structure and evolution of stars, galaxies, and the cosmos itself.
#### A Curiosity about Newtonian Gravity v. 2.0
**Source:** [Link](https://philpapers.org/rec/MERACA-3)
**Abstract:** We give a very curious curiosity about Newtonian Gravity.
#### Network Resources for Astronomers
**Source:** [Link](https://arxiv.org/abs/astro-ph/9411028)
**Abstract:** The amount of data produced by large observational facilities and space missions has led to the archiving and on-line accessibility of much of this data, available to the entire astronomical community. This allows a much wider multi-frequency approach to astronomical research than previously possible. Here we provide an overview of these services, and give a basic description of their contents and possibilities for accessing them. Apart from services providing observational data, many of those providing general information, e.g. on addresses, bibliographies, software etc. are also described. The field is rapidly growing with improved network technology, and our attempt to keep the report as complete and up-to-date as possible will inevitably be outdated shortly. We will endeavor to maintain an updated version of this document on-line.
#### Lectures on Inflation
**Source:** [Link](https://arxiv.org/abs/1609.00716)
**Abstract:** Planning to explore the beginning of the Universe? A lightweight introductory guide to the theory of Inflation.
#### Beyond the Standard Model
**Source:** [Link](https://arxiv.org/abs/hep-ph/9502228)
**Abstract:** A few topics beyond the standard model are reviewed.
#### Gravix: Active Learning for Gravitational Waves Classification Algorithms
**Source:** [Link](https://arxiv.org/abs/2408.14483)
**Abstract:** arXiv admin note: This version has been removed by arXiv administrators due to copyright infringement
## Detailed Similar Results (Direct Matches)
#### White Hat Search Engine Optimization using Large Language Models
**Source:** [Link](https://arxiv.org/abs/2502.07315)
**Abstract:** We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
#### Routing for Large ML Models
**Source:** [Link](https://arxiv.org/abs/2503.05324)
**Abstract:** Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑦𝑖𝑛𝑔 network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 routing with respect to this global metric.
#### Online tools help large language models to solve problems through reasoning
**Source:** [Link](https://econpapers.repec.org/RePEc:nat:nature:v:618:y:2023:i:7965:d:10.1038_d41586-023-01411-4)
**Abstract:** The large language models popularized by chatbots are being taught to alternate reasoning with calls to external tools, such as Wikipedia, to boost their accuracy. The strategy could improve fact-finding outcomes, as well as online shopping.
#### LLaMA: Open and Efficient Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2302.13971)
**Abstract:** We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
#### Apple Intelligence Foundation Language Models
**Source:** [Link](https://arxiv.org/abs/2407.21075)
**Abstract:** We present foundation language models developed to power Apple Intelligence features, including a 3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
#### Notes on the Mathematical Structure of GPT LLM Architectures
**Source:** [Link](https://arxiv.org/abs/2410.19370)
**Abstract:** An exposition of the mathematics underpinning the neural network architecture of a GPT-3-style LLM.
#### Speed and Conversational Large Language Models: Not All Is About Tokens per Second
**Source:** [Link](https://arxiv.org/abs/2502.16721)
**Abstract:** The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.
#### SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
**Source:** [Link](https://arxiv.org/abs/2308.03983)
**Abstract:** Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integration of private data into publicly available Generative AI systems using pre-trained LLM without requiring additional model fine-tuning. Moreover, Retrieval-Centric Generation (RCG) approach, a promising future research direction that explicitly separates roles of LLMs and retrievers in context interpretation and knowledge memorization, potentially leads to more efficient implementation. SimplyRetrieve is an open-source tool with the goal of providing a localized, lightweight, and user-friendly interface to these sophisticated advancements to the machine learning community. SimplyRetrieve features a GUI and API based RCG platform, assisted by a Private Knowledge Base Constructor and a Retrieval Tuning Module. By leveraging these capabilities, users can explore the potential of RCG for improving generative AI performance while maintaining privacy standards. The tool is available at https://github.com/RCGAI/SimplyRetrieve with an MIT license.
#### More Compute Is What You Need
**Source:** [Link](https://arxiv.org/abs/2404.19484)
**Abstract:** Large language model pre-training has become increasingly expensive, with most practitioners relying on scaling laws to allocate compute budgets for model size and training tokens, commonly referred to as Compute-Optimal or Chinchilla Optimal. In this paper, we hypothesize a new scaling law that suggests model performance depends mostly on the amount of compute spent for transformer-based models, independent of the specific allocation to model size and dataset size. Using this unified scaling law, we predict that (a) for inference efficiency, training should prioritize smaller model sizes and larger training datasets, and (b) assuming the exhaustion of available web datasets, scaling the model size might be the only way to further improve model performance.
#### Hermes 3 Technical Report
**Source:** [Link](https://arxiv.org/abs/2408.11857)
**Abstract:** Instruct (or "chat") tuned models have become the primary way in which most people interact with large language models. As opposed to "base" or "foundation" models, instruct-tuned models are optimized to respond to imperative statements. We present Hermes 3, a neutrally-aligned generalist instruct and tool use model with strong reasoning and creative abilities. Its largest version, Hermes 3 405B, achieves state of the art performance among open weight models on several public benchmarks.
#### The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
**Source:** [Link](https://arxiv.org/abs/2306.01116)
**Abstract:** Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger models requiring pretraining on trillions of tokens are considered, it is unclear how scalable is curation and whether we will run out of unique high-quality data soon. At variance with previous beliefs, we show that properly filtered and deduplicated web data alone can lead to powerful models; even significantly outperforming models from the state-of-the-art trained on The Pile. Despite extensive filtering, the high-quality data we extract from the web is still plentiful, and we are able to obtain five trillion tokens from CommonCrawl. We publicly release an extract of 600 billion tokens from our RefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.
#### LIMA: Less Is More for Alignment
**Source:** [Link](https://arxiv.org/abs/2305.11206)
**Abstract:** Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output.