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Daily podcast about the published articles in the LLM field.
25 OCT 2024 · 📜 LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
The source is a research paper that proposes a new approach called LongRAG for enhancing the performance of Retrieval-Augmented Generation (RAG) systems in Long-Context Question Answering (LCQA) tasks. LongRAG addresses two major issues that limit the effectiveness of traditional RAG systems: the "lost in the middle" problem, where relevant information within long contexts is often missed, and the challenge of identifying precise factual details amid noise. This new paradigm uses a dual-perspective approach that effectively integrates global long-context information with specific factual details. The researchers demonstrate that LongRAG significantly outperforms other LCQA methods and traditional RAG systems, including those using large language models, on three multi-hop datasets.
📎 https://arxiv.org/abs/2410.18050
24 OCT 2024 · ⛓️ A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
The paper explores Chain-of-Thought (CoT) prompting, a method to enhance the reasoning skills of large language models (LLMs). It introduces Coherent CoT, where reasoning from previous steps is integrated during predictions, leading to better error correction and accuracy compared to a step-by-step approach. The study shows that errors in intermediate reasoning steps have a more significant impact on the final outcome than mistakes in the final response. Based on this, the authors propose an error-aware CoT prompting method, which includes both correct and incorrect reasoning in demonstrations, allowing LLMs to improve reasoning by learning from earlier mistakes.
🔗 https://arxiv.org/abs/2410.16540
23 OCT 2024 · 📚 A Survey on Data Synthesis and Augmentation for Large Language Models
This research paper examines the use of synthetic and augmented data to enhance the capabilities of Large Language Models (LLMs). The authors argue that the rapid growth of LLMs is outpacing the availability of high-quality data, creating a data exhaustion crisis. To address this challenge, the paper analyzes different data generation methods, including data augmentation and data synthesis, and explores their applications throughout the lifecycle of LLMs, including data preparation, pre-training, fine-tuning, instruction-tuning, and preference alignment. The paper also discusses the challenges associated with these techniques, such as data quality and bias, and proposes future research directions for the field.
📎 https://arxiv.org/abs/2410.12896
22 OCT 2024 · 🤔 Revealing the Barriers of Language Agents in Planning
This research paper examines the challenges faced by language agents in planning tasks. The authors explore the reasons behind the shortcomings of these agents, particularly their limited understanding of constraints and their diminishing ability to focus on goals as the planning horizon lengthens. They investigate two common strategies for improving planning performance: episodic memory updating and parametric memory updating. The study concludes that these strategies, while offering some improvements, primarily function as shortcut learning mechanisms, falling short of achieving human-level planning abilities.
📎 https://arxiv.org/abs/2410.12409
21 OCT 2024 · 🔀 Intelligence at the Edge of Chaos
This research investigates how intelligent behavior emerges in artificial systems by studying the connection between the complexity of rule-based systems and the abilities of models trained to predict these rules. The researchers used elementary cellular automata (ECA), simple one-dimensional systems with varying complexity, to train large language models (LLMs). Their results show that models trained on more complex ECAs demonstrate greater intelligence, excelling in reasoning and chess move prediction tasks. A key finding is the importance of training at a "sweet spot" of complexity—known as the "edge of chaos"—where systems are structured yet difficult to predict, fostering intelligent behavior. Additionally, models trained on complex rules develop sophisticated solutions by incorporating information from previous states, which improves their ability to generalize and perform well on various tasks.
📎 https://arxiv.org/abs/2410.02536v2
20 OCT 2024 · 🗓 Inference Scaling for Long-Context Retrieval Augmented Generation
This research paper explores the effectiveness of inference scaling for retrieval augmented generation (RAG), a technique that enhances large language models (LLMs) by incorporating external knowledge. The authors introduce two strategies, demonstration-based RAG (DRAG) and iterative demonstration-based RAG (IterDRAG), for effectively scaling inference computation. They demonstrate that increasing inference computation, when optimally allocated, leads to nearly linear gains in RAG performance. Furthermore, they develop a computation allocation model to predict the optimal test-time compute allocation for various tasks and scenarios, showcasing its effectiveness in achieving performance gains and aligning with experimental results.
📎 https://arxiv.org/abs/2410.04343
19 OCT 2024 · 🤝 Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
This paper presents a new method called MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) using swarm intelligence. The researchers propose viewing each LLM expert as a "particle" in a swarm and use particle swarm optimization (PSO) to collaboratively search the weight space for optimized models. This approach allows LLMs to adapt to a variety of objectives, including single tasks, multi-task domains, reward models, and human interests, without requiring large amounts of training data. Extensive experiments demonstrate that MODEL SWARMS outperforms existing model composition baselines and enables the discovery of previously unseen capabilities in LLMs.
📎 https://arxiv.org/abs/2410.11163
18 OCT 2024 · 🤖 Agent-as-a-Judge: Evaluate Agents with Agents
The paper detail a new framework for evaluating agentic systems called Agent-as-a-Judge, which uses other agentic systems to assess their performance. To test this framework, the authors created DevAI, a benchmark dataset consisting of 55 realistic automated AI development tasks. They compared Agent-as-a-Judge to LLM-as-a-Judge and Human-as-a-Judge on DevAI, finding that Agent-as-a-Judge outperforms both, aligning closely with human evaluations. The authors also discuss the benefits of Agent-as-a-Judge for providing intermediate feedback and creating a flywheel effect, where both the judge and evaluated agents improve through an iterative process.
📎 https://arxiv.org/abs/2410.10934v1
🤗 https://huggingface.co/DEVAI-benchmark
18 OCT 2024 · ⚖️ First-Person Fairness in Chatbots
This paper from OpenAI examines potential bias in chatbot systems like ChatGPT, specifically focusing on how a user's name, which can be associated with demographic attributes, influences the chatbot's responses. The authors propose a privacy-preserving method to measure user name bias across a large dataset of real-world chatbot interactions. They identify several instances of bias, demonstrating that chatbot responses can show a tendency towards creating protagonists whose gender matches the user's likely gender and that users with female-associated names receive responses with friendlier and simpler language more often. The study also finds that post-training interventions like reinforcement learning can significantly mitigate harmful stereotypes.
📎 https://cdn.openai.com/papers/first-person-fairness-in-chatbots.pdf
🌐 https://openai.com/index/evaluating-fairness-in-chatgpt/
18 OCT 2024 · 🤔 Thinking LLMs: General Instruction Following with Thought Generation
This research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its thoughts based on the quality of the resulting responses. The study demonstrates that Thinking LLMs can outperform standard LLMs on various general instruction-following tasks, including those not typically associated with reasoning, such as marketing and health. The research highlights the potential for Thinking LLMs to expand the capabilities of these models beyond traditional reasoning and problem-solving domains.
📎 https://arxiv.org/abs/2410.10630
Daily podcast about the published articles in the LLM field.
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Author | Shahriar Shariati |
Organization | Shahriar Shariati |
Categories | Technology , Mathematics , Tech News |
Website | - |
shahriarshm81@gmail.com |
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