Key academic papers regarding factual consistency in summarization include: SUMMAC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization; TRUE: Re-evaluating Factual Consistency Evaluation; TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models; ALIGNSCORE: Evaluating Factual Consistency with A Unified Alignment Function; MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents; TOFUEVAL: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization; RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models; and FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs.

Authors

• Person: Not available Organization: Vectara
  vectara/hallucination-leaderboard - GitHub

Sources

• vectara/hallucination-leaderboard - GitHub github.com Vectara via serper

Referenced by nodes (2)

• RAGTruth concept
• SummaC concept