98 citations · 372 across the 23 of their papers we have counts for
5 papers · 1 filter
GEE! Grammar Error Explanation with Large Language Models
Yixiao Song, Kalpesh Krishna, Rajesh Bhatt +2
Grammatical error correction tools are effective at correcting grammatical errors in users' input sentences but do not provide users with \textit{natural language} explanations abo…
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu +6
Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pie…
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska +2
The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated…
Stealing the Decoding Algorithms of Language Models
Ali Naseh, Kalpesh Krishna, Mohit Iyyer +1
A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. These algorithms determine how to generate text from the int…
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Kalpesh Krishna, Erin Bransom, Bailey Kuehl +4
While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty a…