🥇Top ML Papers of the Week
The Top ML Papers of the Week (September 2 - September 8)
1). AlphaProteo - presents a family of ML models trained for protein design; reports a 3-to 300-fold better binding affinities and higher experimental success rates compared to other existing methods on seven target proteins; shows that AlphaProteo’s performance on hundreds of target proteins from the PDB is comparable to the seven targets. (paper | tweet)
2). RAG in the Era of Long-Context LLMs - reports that longer-context LLMs suffer from a diminished focus on relevant information, which is one of the primary issues that a RAG system addresses (i.e., uses more relevant information); they propose an order-preserving RAG mechanism that improves performance on long-context question answering; it's not perfect and in fact, as retrieved chunks increase the quality of responses go up and then declines; they mention a sweet spot where it can achieve better quality with a lot fewer tokens than long-context LLMs. (paper | tweet)
3). Strategic Chain-of-Thought - a method to refine LLM performance by incorporating strategic knowledge before the intermediate CoT reasoning steps; the problem-solving strategy helps to guide the generation of the CoT paths and final answers; claims to achieve a 21.05% increase on the GSM8K datasets using the Llama3-8b model. (paper)
4). Effective of AI on High Skilled Work - studies the impact of generative AI on software developers; reveals a 26.08% increase in the number of completed tasks among the developers that use AI tools like GitHub Copilot; also shows that less experienced developers are likely to adopt the AI tools and have greater productivity gains. (paper | tweet)
5). OLMoE - introduces a fully-open LLM that leverages sparse Mixture-of-Experts. OLMoE is a 7B parameter model and uses 1B active parameters per input token; there is also an instruction-tuned version that claims to outperform Llama-2-13B-Chat and DeepSeekMoE 16B. (paper | tweet)
6). LongCite - synthesizes a large-scale SFT dataset with off-the-shelf LLMs to improve long-context question answering with citations; it trains 8B and 9B parameter models that enhance citation generation capabilities from lengthy contexts while improving response correctness; claims to even surpass GPT-4o on their proposed LongBench-Cite benchmark. (paper | tweet)
7). MemLong - utilizes an external retriever for retrieving historical information which enhances the capabilities of long-context LLMs; it consistently outperforms other SoTA LLMs on long-context benchmarks and can extend the context length on a single 3090 GPU from 4k up to 80k. (paper | tweet)
8). Role of RAG Noise in LLMs - proposes a benchmark (NoiserBench) to measure how different kinds of noisy information affect RAG's performance; reports that from different kinds of beneficial noise studied (e.g., semantic, datatype, and illegal sentence), illegal sentence noise exhibits the most improved model performance across models and datasets. (paper | tweet)
9). Beyond Preference in AI Alignment - challenges the dominant practice of AI alignment known as human preference tuning; explains in what ways human preference tuning fails to capture the thick semantic content of human values; argues that AI alignment needs reframing, instead of aligning on human preferences, AI should align on normative standards appropriate to their social roles. (paper | tweet)
10). LLM-Based Agents for Software Engineering - a survey paper on LLM-based agents for software engineering, covering perspectives ranging from requirement engineering to test generation to software maintenance. (paper | tweet)