🥇Top ML Papers of the Week
The Top ML Papers of the Week (Feb 19 - Feb 25)
1). Stable Diffusion 3 - a suite of image generation models ranging from 800M to 8B parameters; combines diffusion transformer architecture and flow matching for improved performance in multi-subject prompts, image quality, and spelling abilities; technical report to be published soon and linked here. (paper | tweet)
2). Gemma - a series of open models inspired by the same research and tech used for Gemini; includes 2B (trained on 2T tokens) and 7B (trained on 6T tokens) models including base and instruction-tuned versions; trained on a context length of 8192 tokens; generally outperforms Llama 2 7B and Mistral 7B. (paper | tweet)
3). LLMs for Data Annotation - an overview and a good list of references that apply LLMs for data annotation; includes a taxonomy of methods that employ LLMs for data annotation; covers three aspects: LLM-based data annotation, assessing LLM-generated annotations, and learning with LLM-generated annotations. (paper | tweet)
4). GRIT - presents generative representational instruction tuning where an LLM is trained to perform both generative and embedding tasks and designed to distinguish between them via the instructions; produces new state-of-the-art on MTEB and the unification is reported to speed up RAG by 60% for long documents. (paper | tweet)
5). LoRA+ - proposes LoRA+ which improves performance and finetuning speed (up to ∼ 2X speed up), at the same computational cost as LoRA; the key difference between LoRA and LoRA+ is how the learning rate is set; LoRA+ sets different learning rates for LoRA adapter matrices while in LoRA the learning rate is the same. (paper | tweet)
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6). Revisiting REINFORCE in RLHF - shows that many components of PPO are unnecessary in an RLHF context; it also shows that a simpler REINFORCE variant outperforms both PPO and newly proposed alternatives such as DPO and RAFT; overall, it shows that online RL optimization can be beneficial and low cost. (paper | tweet)
7). Recurrent Memory Finds What LLMs Miss - explores the capability of transformer-based models in extremely long context processing; finds that both GPT-4 and RAG performance heavily rely on the first 25% of the input, which means there is room for improved context processing mechanisms; reports that recurrent memory augmentation of transformer models achieves superior performance on documents of up to 10 million tokens. (paper | tweet)
8). When is Tree Search Useful for LLM Planning - investigates how LLM solves multi-step problems through a framework consisting of a generator, discriminator, and planning method (e.g., iterative correction and tree search); reports that planning methods demand discriminators with at least 90% accuracy but current LLMs don’t demonstrate these discrimination capabilities; finds that tree search is at least 10 to 20 times slower but regardless of it good performance it’s impractical for real-world applications. (paper | tweet)
9). CoT Reasoning without Prompting - proposes a chain-of-thought (CoT) decoding method to elicit the reasoning capabilities from pre-trained LLMs without explicit prompting; claims to significantly enhance a model’s reasoning capabilities over greedy decoding across reasoning benchmarks; finds that the model's confidence in its final answer increases when CoT is present in its decoding path. (paper | tweet)
10). OpenCodeInterpreter - a family of open-source systems for generating, executing, and iteratively refining code; proposes a dataset of 68K multi-turn interactions; integrates execution and human feedback for dynamic code refinement and produces high performance on benchmarks like HumalEval and EvalPlus. (paper | tweet)
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