3 citations · 4 across the 3 of their papers we have counts for
4 papers
Compared to What? Baselines and Metrics for Counterfactual Prompting
Zihao Yang, Mosh Levy, Yoav Goldberg +1
Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and CoT faithfulness. But in this work we ar…
Do Automatic Factuality Metrics Measure Factuality? A Critical Evaluation
Sanjana Ramprasad, Byron C. Wallace
Modern LLMs can now produce highly readable abstractive summaries, to the point that traditional automated metrics for evaluating summary quality, such as ROUGE, have saturated. Ho…
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains
Sanjana Ramprasad, Kundan Krishna, Zachary C Lipton +1
Recent work has shown that large language models (LLMs) are capable of generating summaries zero-shot (i.e., without explicit supervision) that, under human assessment, are often c…
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence
Kundan Krishna, Sanjana Ramprasad, Prakhar Gupta +3
LLMs can generate factually incorrect statements even when provided access to reference documents. Such errors can be dangerous in high-stakes applications (e.g., document-grounded…