2 citations · 2 across the 2 of their papers we have counts for
6 papers
On the Impossibility of Retrain Equivalence in Machine Unlearning
Jiatong Yu, Yinghui He, Anirudh Goyal +1
Machine unlearning seeks to selectively remove the "influence" of specific training data on a model's outputs. The ideal goal is Retrain Equivalence--behavior identical to a model…
Skill-Targeted Adaptive Training
Yinghui He, Abhishek Panigrahi, Yong Lin +1
Language models often show little to no improvement (i.e., "saturation") when trained via vanilla supervised fine-tuning (SFT) on data similar to what they saw in their training se…
OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs
Caorui Li, Yu Chen, Yiyan Ji +40
Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively eva…
AdaptMI: Adaptive Skill-based In-context Math Instruction for Small Language Models
Yinghui He, Abhishek Panigrahi, Yong Lin +1
In-context learning (ICL) allows a language model to improve its problem-solving capability when provided with suitable information in context. Since the choice of in-context infor…
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
Jiahao Qiu, Yinghui He, Xinzhe Juan +7
The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a mu…
LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation
Xi Ye, Fangcong Yin, Yinghui He +5
Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical…