??Top ML Papers of the Week

The top ML Papers of the Week (July 31- August 6):

1). Open Problem and Limitation of RLHF?- provides an overview of open problems and the limitations of RLHF. (paper?|?tweet)

2). Med-Flamingo?- a new multimodal model that allows in-context learning and enables tasks such as few-shot medical visual question answering; evaluations based on physicians, show improvements of up to 20% in clinician's rating; the authors occasionally observed low-quality generations and hallucinations. (paper?|?tweet)

3). ToolLLM?- enables LLMs to interact with 16000 real-world APIs; it’s a framework that allows data preparation, training, and evaluation; the authors claim that one of their models, ToolLLaMA, has reached the performance of ChatGPT (turbo-16k) in tool use. (paper?|?tweet)

4). Skeleton-of-Thought?- proposes a prompting strategy that firsts generate an answer skeleton and then performs parallel API calls to generate the content of each skeleton point; reports quality improvements in addition to speed-up of up to 2.39x. (paper?|?tweet)

5). MetaGPT?- a framework involving LLM-based multi-agents that encodes human standardized operating procedures (SOPs) to extend complex problem-solving capabilities that mimic efficient human workflows; this enables MetaGPT to perform multifaceted software development, code generation tasks, and even data analysis using tools like AutoGPT and LangChain. (paper?|?tweet)

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6). OpenFlamingo?- introduces a family of autoregressive vision-language models ranging from 3B to 9B parameters; the technical report describes the models, training data, and evaluation suite. (paper?|?tweet)

7). The Hydra Effect?- shows that language models exhibit self-repairing properties — when one layer of attention heads is ablated it causes another later layer to take over its function. (paper?|?tweet)

8). Self-Check?- explores whether LLMs have the capability to perform self-checks which is required for complex tasks that depend on non-linear thinking and multi-step reasoning; it proposes a zero-shot verification scheme to recognize errors without external resources; the scheme can improve question-answering performance through weighting voting and even improve math word problem-solving. (paper?|?tweet)

9). Agents Model the World with Language?- presents an agent that learns a multimodal world model that predicts future text and image representations; it learns to predict future language, video, and rewards; it’s applied to different domains and can learn to follow instructions in visually and linguistically complex domains. (paper?|?tweet)

10). AutoRobotics-Zero?- discovers zero-shot adaptable policies from scratch that enable adaptive behaviors necessary for sudden environmental changes; as an example, the authors demonstrate the automatic discovery of Python code for controlling a robot. (paper?|?tweet)

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