🥇Top ML Papers of the Week
The Top ML Papers of the Week (March 11 - March 17)
1). SIMA - a generalist AI agent for 3D virtual environments that follows natural-language instructions in a broad range of 3D virtual environments and video games; SIMA is evaluated across 600 basic skills, spanning navigation, object interaction, and menu use. Language seems to be a huge factor in performance. (paper | tweet)
2). Retrieval Augmented Thoughts - shows that iteratively revising a chain of thoughts with information retrieval can significantly improve LLM reasoning and generation in long-horizon generation tasks; the key idea is that each thought step is revised with relevant retrieved information to the task query, the current and past thought steps; Retrieval Augmented Thoughts (RAT) can be applied to different models like GPT-4 and CodeLlama-7B to improve long-horizon generation tasks (e.g., creative writing and embodied task planning); RAT is a zero-shot prompting approach and provides significant improvements to baselines that include zero-shot CoT prompting, vanilla RAG, and other baselines. (paper | tweet)
3). LMs Can Teach Themselves to Think Before Speaking - presents a generalization of STaR, called Quiet-STaR, to enable language models (LMs) to learn to reason in more general and scalable ways; Quiet-STaR enables LMs to generate rationales at each token to explain future text; it proposes a token-wise parallel sampling algorithm that helps improve LM predictions by efficiently generating internal thoughts; the rationale generation is improved using REINFORCE. (paper | tweet)
4). Knowledge Conflicts for LLMs - an overview of the common issue of knowledge conflict when working with LLMs; the survey paper categorizes these conflicts into context-memory, inter-context, and intra-memory conflict; it also provides insights into causes and potential ways to mitigate these knowledge conflict issues. (paper | tweet)
5). Stealing Part of a Production Language Model - presents the first model-stealing attack that extracts information from production language models like ChatGPT or PaLM-2; shows that it's possible to recover the embedding projection layer of a transformer-based model through typical API access; as an example, the entire projection matrix was extracted from the OpenAI ada and babbage models for under $20. (paper | tweet)
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6). Branch-Train-MiX - proposes mixing expert LLMs into a Mixture-of-Experts LLM as a more compute-efficient approach for training LLMs; it's shown to be more efficient than training a larger generalist LLM or several separate specialized LLMs; the approach, BTX, first trains (in parallel) multiple copies of a seed LLM specialized in different domains (i.e., expert LLMs) and merges them into a single LLM using MoE feed-forward layers, followed by fine-tuning of the overall unified model. (paper | tweet)
7). LLMs Predict Neuroscience Results - proposes a benchmark, BrainBench, for evaluating the ability of LLMs to predict neuroscience results; finds that LLMs surpass experts in predicting experimental outcomes; an LLM tuned on neuroscience literature was shown to perform even better. (paper | tweet)
8). C4AI Command-R - a 35B parameter model, with a context length of 128K, optimized for use cases that include reasoning, summarization, and question answering; Command-R has the capability for multilingual generation evaluated in 10 languages and performant tool use and RAG capabilities; it has been released for research purposes. (paper | tweet)
9). Is Cosine-Similarity Really About Simirity? - studies embeddings derived from regularized linear models and derive analytically how cosine-similarity can yield arbitrary and meaningless similarities; also finds that for some linear models, the similarities are not even unique and others are controlled by regularization; the authors caution against blindly using cosine similarity and presents considerations and alternatives. (paper | tweet)
10). Multimodal LLM Pre-training - provides a comprehensive overview of methods, analysis, and insights into multimodal LLM pre-training; studies different architecture components and finds that carefully mixing image-caption, interleaved image-text, and text-only data is key for state-of-the-art performance; it also proposes a family of multimodal models up to 30B parameters that achieve SOTA in pre-training metrics and include properties such as enhanced in-context learning, multi-image reasoning, enabling few-shot chain-of-thought prompting. (paper | tweet)
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