🥇Top ML Papers of the Week
The Top ML Papers of the Week (May 20 - May 26)
1). Extracting Interpretable Features from Claude 3 Sonnet - presents an effective method to extract millions of abstract features from an LLM that represent specific concepts; these concepts could represent people, places, programming abstractions, emotion, and more; reports that some of the discovered features are directly related to the safety aspects of the model; finds features directly related to security vulnerabilities and backdoors in code, bias, deception, sycophancy; and dangerous/criminal content, and more; these features are also used to intuititively steer the model’s output. (paper | tweet)
2). Agent Planning with World Knowledge Model - introduces a parametric world knowledge model to facilitate agent planning; the agent model can self-synthesize knowledge from expert and sampled trajectories; this is used to train the world knowledge model; prior task knowledge is used to guide global planning and dynamic state knowledge is used to guide the local planning; demonstrates superior performance compared to various strong baselines when adopting open-source LLMs like Mistral-7B and Gemma-7B. (paper | tweet)
3). Risks and Opportunities of Open-Source Generative AI - analyzes the risks and opportunities of open-source generative AI models; argues that the overall benefits of open-source generative AI outweigh its risks. (paper | tweet)
4). Enhancing Answer Selection in LLMs - proposes a hierarchical reasoning aggregation framework for improving the reasoning capabilities of LLMs; the approach, called Aggregation of Reasoning (AoR), selects answers based on the evaluation of reasoning chains; AoR uses dynamic sampling to adjust the number of reasoning chains with respect to the task complexity; it uses results from the evaluation phase to determine whether to sample additional reasoning chains; a known flaw of majority voting is that it fails in scenarios where the correct answer is in the minority; AoR focuses on evaluating the reasoning chains to improve the selection of the final answer; AoR outperforms various prominent ensemble methods and can be used with various LLMs to improve performance on complex reasoning tasks. (paper | tweet)
5). How Far Are We From AGI - presents an opinion paper addressing important questions to understand the proximity to artificial general intelligence (AGI); it provides a summary of strategies necessary to achieve AGI which includes a detailed survey, discussion, and original perspectives. (paper)
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6). Efficient Inference of LLMs - proposes a layer-condensed KV cache to achieve efficient inference in LLMs; only computes and caches the key-values (KVs) of a small number of layers which leads to saving memory consumption and improved inference throughput; can achieve up to 26x higher throughput than baseline transformers while maintaining satisfactory performance. (paper | tweet)
7). Guide for Evaluating LLMs - provides guidance and lessons for evaluating large language models; discusses challenges and best practices, along with the introduction of an open-source library for evaluating LLMs. (paper | tweet)
8). Scientific Applications of LLMs - presents INDUS, a comprehensive suite of LLMs for Earth science, biology, physics, planetary sciences, and more; includes an encoder model, embedding model, and small distilled models. (paper | tweet)
9). DeepSeek-Prover - introduces an approach to generate Lean 4 proof data from high-school and undergraduate-level mathematical competition problems; it uses the synthetic data, comprising of 8 million formal statements and proofs, to fine-tune a DeepSeekMath 7B model; achieves whole-proof generation accuracies of 46.3% with 64 samples and 52% cumulatively on the Lean 4 miniF2F test; this surpasses the baseline GPT-4 (23.0%) with 64 samples and a tree search RL method (41.0%). (paper | tweet)
10). Efficient Multimodal LLMs - provides a comprehensive and systematic survey of the current state of efficient multimodal large language models; discusses efficient structures and strategies, applications, limitations, and promising future directions. (paper | tweet)
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