Top ML Papers of the Week (April 3 – 9): – Segment Anything Model
– SegGPT
– A Survey of LLMs
– Instruction Tuning with GPT-4
– 8 Things to Know about LLMs
– Summary of ChatGPT/GPT-4 Research
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@dair_ai
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Top ML Papers Week April 3-9 Segment Anything Model
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Community Contributors Wanted for AI Initiatives
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Important to call out how important the community is with these efforts. We are always looking for contributors, supporters, and partners. Reach out if you are interested. Discord:
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Prompt Engineering Guide and ML Papers Trending on GitHub
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GitHub Trending is one of the best places to learn what's trending in AI. Both our Prompt Engineering Guide & ML Papers of the Week are trending today! We are excited to continue with our efforts to make ML more accessible. https://
github.com/dair-ai/Prompt
-Engineering-Guide
… https://
github.com/dair-ai/ML-Pap
ers-of-the-Week
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Machine Learning Advances for Solving Partial Differential Equations
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10/ ML for Partial Differential Equations – a review examining avenues of partial differential equations research advanced by machine learning.
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Dialog-Enabled Resolving Agents Enhance LLM Clinical Task Performance
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8/ Dialog-Enabled Resolving Agents (DERA) – a paradigm to enhance large language model completions by allowing models to communicate feedback and iteratively improve output; DERA outperforms base GPT-4 on clinically-focused tasks.
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Natural Selection Favors AI Systems Over Humans: Risks and Mitigation
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9/ Natural Selection Favors AIs over Humans – discusses why AI systems will be more fit than humans and the potential dangers and risks involved, including ways to mitigate them.
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ChatGPT Outperforms Crowd Workers on Text Annotation Tasks
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6/ ChatGPT for Text-Annotation – shows that ChatGPT outperforms crowd-workers on several annotation tasks such as relevance, topics, and frames detection; besides better 0-shot accuracy, the per-annotation cost of ChatGPT is ~20 times cheaper than MTurk.
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LLM Agents Execute Computer Tasks Through Recursive Self-Improvement
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7/ LLMs for Computer Tasks – shows that a pre-trained LLM agent can execute computer tasks using a simple prompting scheme where the agent recursively criticizes and improves its outputs.
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ChatDoctor: Medical LLM Fine-tuned on LLaMA for Healthcare
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4/ ChatDoctor – a medical chat model fine-tuned on LLaMA using medical domain knowledge. Collects data on around 700 diseases and generated 5K doctor-patient conversations to finetune the LLM.
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LLaMA-Adapter: Lightweight Method Fine-Tune Instruction-Following Models
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5/ LLaMA-Adapter – a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model; generates responses comparable to Alpaca with fully fine-tuned 7B parameter; it’s also extended for multi-modal input support.