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Human Feedback

SLiC-HF: Sequence Likelihood Calibration with Human Feedback - Summary

The paper presents a new approach called SLiC-HF that uses Sequence Likelihood Calibration with Human Feedback to improve language models. The approach is shown to be effective on the TL;DR summarization task and is a simpler and more computationally efficient alternative to Reinforcement Learning

Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models - Summary

The paper introduces Chameleon, a plug-and-play compositional reasoning framework that augments large language models (LLMs) to address their inherent limitations and tackle a broad range of reasoning tasks. Chameleon synthesizes programs to compose various tools, including LLM models, off-the-shel

Training language models to follow instructions with human feedback - Summary

The paper presents a method for aligning language models with user intent by fine-tuning with human feedback. The resulting models, called InstructGPT, show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Th