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Recent research focuses on enhancing Large Language Models (LLMs) for better recommendations and reasoning. Microsoft proposes a two-stage approach to improve LLMs' ability to follow instructions. Strategies like RAT and CoRAL aim to refine LLMs' reasoning by iteratively revising chain-of-thought with retrieved information. Innovations in prompting strategies continue to evolve to address challenges in long-horizon tasks and reduce biases in explanations.
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation Presents an approach that augments LLMs with collaboratively retrieved user-item interactions through RL, aligning reasoning with collaborative patterns. 📝https://t.co/l2ecwguzFt https://t.co/TH9wA6AWcH
Retrieval Augmented Thoughts (RAT): An AI Prompting Strategy that Synergies Chain of Thought (CoT) Prompting and Retrieval Augmented Generation (RAG) to Address the Challenging Long-Horizon Reasoning and Generation Tasks Quick read: https://t.co/awa0GcsGXc Researchers from… https://t.co/8MIGFVUnTk
The ability of popular LLMs to adhere to instructions and deliver helpful responses can often be attributed to RLHF. Tomorrow we'll breakdown a recent paper that discusses what's essential when it comes to reinforcement learning in the era of #LLMs https://t.co/R9u2xbXUNh https://t.co/FLwJzYvHFy
