May 23, 2023 · We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Explore LLM models' ability to generate step-by-step reasoning for answers and have been shown to improve performance on complex reasoning tasks. The models are ...
Dec 14, 2023 · Code for the arXiv paper: "LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond" - salesforce/factualNLG.
Dec 10, 2023 · In this work, we explore the capabilities of LLMs to act as factual reasoners through the lens of fac- tual evaluation in text summarization. As ...
SummEdits is a benchmark designed to measure the ability of Large Language Models (LLMs) to reason about facts and detect inconsistencies.
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May 25, 2023 · This new benchmark is 20 times more cost-effective per sample than previous benchmarks and highly reproducible, as we estimate inter-annotator ...
Sep 23, 2023 · Recent analysis shows that LLMs fail to provide coherent explanations when identifying factual inconsistencies, indicating they do not ...
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond. LLMs are used to summarize documents across different domains. The summarizations must ...
Llms as factual reasoners: Insights from existing benchmarks and beyond. P Laban, W Kryściński, D Agarwal, AR Fabbri, C Xiong, S Joty, CS Wu. arXiv preprint ...
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. 28. Paper · Code ...