92 citations · 247 across the 33 of their papers we have counts for
8 papers · 1 filter
A Comprehensive Evaluation of Tool-Assisted Generation Strategies
Alon Jacovi, Avi Caciularu, Jonathan Herzig +3
A growing area of research investigates augmenting language models with tools (e.g., search engines, calculators) to overcome their shortcomings (e.g., missing or incorrect knowled…
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit, Johan Ferret, Lior Shani +16
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon i…
Surfacing Biases in Large Language Models using Contrastive Input Decoding
Gal Yona, Or Honovich, Itay Laish +1
Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the…
Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Benjamin Muller, John Wieting, Jonathan H. Clark +6
Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for th…
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann +7
Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other tha…
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni +2
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work imp…