3 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends
Sanjana Ramprasad, Elisa Ferracane, Zachary C. Lipton
Recent advancements in large language models (LLMs) have considerably advanced the capabilities of summarization systems. However, they continue to face concerns about hallucinatio…
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…
USB: A Unified Summarization Benchmark Across Tasks and Domains
Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad +3
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to cont…
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
Sanjana Ramprasad, Denis Jered McInerney, Iain J. Marshal +1
We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on…