Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs (2026 · 1 citation)
When models generate text from a given source, such as summaries or retrieval-augmented answers, they sometimes add claims that the source cannot support.
Will it Merge? On The Causes of Model Mergeability (2026)
Merging combines several fine-tuned models into one multitask model without retraining, yet it is unclear why some fine-tuned updates survive merging better than others.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

A Summarization System for Scientific Documents ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations · no summary yetShai Erera et al.2019 · 68 citations
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMsFindings of the Association for Computational LinguisticsYehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass, et al.2026 · 1 citation
Will it Merge? On The Causes of Model MergeabilityFindings of the Association for Computational LinguisticsAdir Rahamim, Asaf Yehudai, Boaz Carmeli, et al.2026

Co-authors here

5 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Papers of our library cite their works 17 times (of the 149,439 papers whose bibliographies we read).

Updated 1 November 2026