🥇Top ML Papers of the Week

Welcome to The Top ML Papers of the Week (June 24 - June 30).

1). ESM3 - a new LLM-based biological model that generates a new green fluorescent protein called esmGFP; builds on a bidirectional transformer, uses masked language models for the objective function, leverages geometric attention to represent atomic coordinates, and applies chain-of-thought prompting to generate fluorescent proteins; estimates that esmGFP represents an equivalent of over 500 million years of natural evolution performed by an evolutionary simulator. (paper | tweet)

2). Gemma 2 - presents a family of open models ranging between 2B to 27B parameters; demonstrates strong capabilities in reasoning, math, and code generation, outperforming models twice its size. (paper | tweet)

3). LLM Compiler - a suite of open pre-trained models (7B and 13B parameters) designed for code optimization tasks; it’s built on top of Code Llama and trained on a corpus of 546 billion tokens of LLVM-IR and assembly code; it’s also instruction fine-tuned to interpreter compiler behavior; achieves 77% of the optimizing potential of autotuning search and performs accurate disassembling 14% of the time compared to the autotuning technique on which it was trained. (paper | tweet)

4). Enhancing RAG with Long-Context LLMs - proposes LongRAG, which combines RAG with long-context LLMs to enhance performance; uses a long retriever to significantly reduce the number of extracted units by operating on longer retrieval units; the long reader takes in the long retrieval units and leverages the zero-shot answer extraction capability of long-context LLMs to improve performance of the overall system; claims to achieve 64.3% on HotpotQA (full-wiki), which is on par with the state-of-the-art model. (paper | tweet)

5). Improving Retrieval in LLMs through Synthetic Data - proposes a fine-tuning approach to improve the accuracy of retrieving information in LLMs while maintaining reasoning capabilities over long-context inputs; the fine-tuning dataset comprises numerical dictionary key-value retrieval tasks (350 samples); finds that this approach mitigates the "lost-in-the-middle" phenomenon and improves performance on both information retrieval and long-context reasoning. (paper | tweet)

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6). GraphReader - proposes a graph-based agent system to enhance the long-context abilities of LLMs; it structures long text into a graph and employs an agent to explore the graph (using predefined functions guided by a step-by-step rational plan) to effectively generate answers for questions; consistently outperforms GPT-4-128k across context lengths from 16k to 256k. (paper | tweet)

7). Faster LLM Inference with Dynamic Draft Trees - presents a context-aware dynamic draft tree to increase the speed of inference; the previous speculative sampling method used a static draft tree for sampling which only depended on position but lacked context awareness; achieves speedup ratios ranging from 3.05x-4.26x, which is 20%-40% faster than previous work; these speedup ratios occur because the new method significantly increases the number of accepted draft tokens. (paper | tweet)

8). Following Length Constraints in Instructions - presents an approach for how to deal with length bias and train instruction following language models that better follow length constraint instructions; fine-tunes a model using DPO with a length instruction augmented dataset and shows less length constraint violations and while keeping a high response quality. (paper | tweet)

9). On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation - survey on LLM-based synthetic data generation, curation, and evaluation. (paper | tweet)

10). Adam-mini - a new optimizer that reduces memory footprint (45%-50% less memory footprint) by using fewer learning rates and achieves on-par or even outperforms AdamW; it carefully partitions parameters into blocks and assigns a single high-quality learning that outperforms Adam; achieves consistent results on language models sized from 125M -7B for pre-training, SFT, and RLHF. (paper | tweet)