6). An Open-source LM Specialized in Evaluating Other LMs – open-source Prometheus 2 (7B & 8x7B), state-of-the-art open evaluator LLMs that closely mirror human and GPT-4 judgments; they support both direct assessments and pair-wise ranking formats grouped with user-defined
@dair_ai
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Self-Play Preference Optimization for Language Model Alignment
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7). Self-Play Preference Optimization – proposes a self-play-based method for aligning language models; this optimization procedure treats the problem as a constant-sum two-player game to identify the Nash equilibrium policy.
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Survey on Retrieval-Augmented Language Models: Evolution and Applications
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5). A Survey on Retrieval-Augmented Language Models – covers the most important recent developments in RAG and RAU systems; it includes evolution, taxonomy, and an analysis of applications.
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Med-Gemini Multimodal Model Surpasses GPT-4 on Medical Benchmarks
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3). Med-Gemini – presents a family of multimodal models specialized in medicines and based on the strong multimodal and long-context reasoning capabilities of Gemini; achieves state-of-the-art performance on 10/14 benchmarks surpassing GPT-4 models.
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Training LLMs to Retrieve Information When Uncertain
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4). When to Retrieve? – presents an approach to train LLMs to effectively utilize information retrieval; it first proposes a training approach to teach an LLM to generate a special token, , when it's not confident about the answer to a question.
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Multi-token Prediction Approach Improves LLM Speed
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2). Better and Faster LLMs via Multi-token Prediction – proposes a multi-token prediction approach that performs language modeling by training the predict the following n tokens using n independent output heads…
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State Space Models Survey: Analysis and Applications
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10/ A Survey on State Space Models – a survey paper on state space models (SSMs) with experimental comparison and analysis; it reviews current SSMs, improvements compared to alternatives, challenges, and their applications.
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LLM In-Context Recall Depends on Prompt Formulation
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9/ LLM In-Context Recall is Prompt Dependent – finds that a model's recall performance is significantly affected by small changes in the prompt; the interplay between prompt content and training data can degrade the response quality.
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RAG Reduces Hallucination in LLM Structured Outputs
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7/ Reducing Hallucination in Structured Outputs via RAG – shows that RAG can enable deploying powerful LLM-powered systems in limited-resource settings while mitigating issues like hallucination and increasing the reliability of outputs.
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Emerging AI Agent Architectures: Reasoning, Planning, and Tool Calling
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8/ Emerging AI Agent Architectures – a summary of emerging AI agent architectures; it focuses the discussion on capabilities like reasoning, planning, and tool calling which are all needed to build complex AI-powered agentic workflows and systems.