ISQA: Informative Factuality Feedback for Scientific Summarization

Code

• github.com/lizekai-richard/isqa
  OfficialIn paperpytorch★ 2

Abstract

We propose Iterative Facuality Refining on Informative Scientific Question-Answering (ISQA) feedbackCode is available at https://github.com/lizekai-richard/isqa, a method following human learning theories that employs model-generated feedback consisting of both positive and negative information. Through iterative refining of summaries, it probes for the underlying rationale of statements to enhance the factuality of scientific summarization. ISQA does this in a fine-grained manner by asking a summarization agent to reinforce validated statements in positive feedback and fix incorrect ones in negative feedback. Our findings demonstrate that the ISQA feedback mechanism significantly improves the factuality of various open-source LLMs on the summarization task, as evaluated across multiple scientific datasets.

Tasks

Reproductions