9/ The Unreasonable Effectiveness of Easy Training Data for Hard Tasks – suggests that language models often generalize well from easy to hard data, i.e., easy-to-hard generalization.
LLMS
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MoE-Mamba: Scaling LLMs with State Space Models and Mixture of Experts
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10/ MoE-Mamba – an approach to efficiently scale LLMs by combining state space models (SSMs) with Mixture of Experts (MoE); MoE-Mamba, outperforms both Mamba and Transformer-MoE.
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LLM Evaluation Methodologies: Taxonomy and Approaches
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7/ Overview of LLMs for Evaluation – thoroughly surveys the methodologies and explores their strengths and limitations; provides a taxonomy of different approaches involving prompt engineering or calibrating open-source LLMs for evaluation.
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Patchscopes: Framework for Explaining LLM Internal Representations
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8/ Patchscopes – proposes a framework that leverages a model itself to explain its internal representations; it can be used to answer questions about an LLM’s computation and can even be used to fix latent multi-hop reasoning errors.
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ReFT: Enhancing LLM Reasoning Through Reinforced Fine-Tuning
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6/ Reasoning with Reinforced Fine-Tuning – an approach, ReFT, to enhance the generalizability of LLMs for reasoning; it starts with applying SFT and then applies online RL for further refinement while automatically sampling reasoning paths to learn from.
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Proxy-Tuning: Efficient Language Model Adaptation at Decoding
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5/ Tuning Language Models by Proxy – introduces proxy-tuning, a decoding-time algorithm that modifies logits of a target LLM with the logits’ difference between a small base model and a fine-tuned base model.
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Self-Rewarding Models: LLM Self-Alignment Training Method
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4/ Self-Rewarding Models – proposes a self-alignment method that uses the model itself for LLM-as-a-Judge prompting to provide its rewards during training; Iterative DPO is used for instruction following training using the preference pairs.
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AlphaCodium: Iterative LLM Flow for Improved Code Generation
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2/ AlphaCodium – a code-oriented iterative flow that improves LLMs on code generation; involves additional generated data (problem self-reflection & test reasoning) to aid the iterative process, and enrich public tests using AI-generated tests.https://t.co/BSmwS0WEtj
— DAIR.AI (@dair_ai) 21 janvier 20242/ AlphaCodium – a code-oriented iterative flow that improves LLMs on code generation; involves additional generated data (problem self-reflection & test reasoning) to aid the iterative process, and enrich public tests using AI-generated tests.
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RAG versus Fine-tuning: LLM Optimization Trade-offs Analysis
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3/ RAG vs. Finetuning – report discussing the tradeoff between RAG and fine-tuning when using LLMs like Llama 2 and GPT-4; performs a detailed analysis and highlights insights when applying the pipelines on an agricultural dataset.
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Leakers Disappointment and Expected GPT 4.5 Release
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Honestly, been disappointed with all the so called leakers lately (besides Jimmy). Still, when Gemini Ultra is released next week and trys to take the crown of best AI, OAI is in the need to release something better. So, I assume GPT 4.5 out of necessity soon