Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024 · 67 citations)
Model editing updates facts stored in large language models without full retraining. However, it is usually judged only on whether the edit sticks, generalizes to paraphrases and leaves unrelated facts intact. This EMNLP 2024 paper asks whether such edits quietly damage the models' broader capabilities. The authors…
GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling (2026)
Training LLMs to call functions reliably requires large amounts of varied, accurate tool-use data. Real data is expensive to collect and annotate, and existing synthetic pipelines tend to rely on unstable APIs, scale poorly to new tools and lack diversity and quality control. GenesisFunc is an automated, three-stage…

Co-authors here

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

Venues

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

Papers of our library cite their works 52 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated 2 November 2026