1 citations · 2 across the 12 of their papers we have counts for
12 papers
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Yansen Zhang, Yilu Liu, Tianyu Liu +7
Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers ass…
UXBench: Benchmarking User Experience in AI Assistants
Mengze Hong, Xia Zeng, Zeyang Lei +27
As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present \textbf{UXBench}, the…
SEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?
Jiamin Chen, Yidi Wu, Qiexiang Wang +6
Widely used language-model benchmarks are increasingly saturated, with frontier systems often receiving near-tied scores that standard metrics cannot resolve. Rather than construct…
DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation
Jiamin Chen, Qianben Chen, Jiawen Zhang +5
Long-form video generation is rapidly moving from short, single-scene synthesis toward minute-long, multi-shot creation with narrative structure, cinematic control, audio, and cros…
Beyong Tokens: Item-aware Attention for LLM-based Recommendation
Xiaokun Zhang, Bowei He, Jiamin Chen +2
Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and…
Have We Really Understood Collaborative Information? An Empirical Investigation
Xiaokun Zhang, Zhaochun Ren, Bowei He +2
Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. Howev…
Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization
Bowei He, Lihao Yin, Huiling Zhen +5
Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods,…
Grounding Long-Context Reasoning with Contextual Normalization for Retrieval-Augmented Generation
Jiamin Chen, Yuchen Li, Xinyu Ma +5
Retrieval-Augmented Generation (RAG) has become an essential approach for extending the reasoning and knowledge capacity of large language models (LLMs). While prior research has p…
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang, Bowei He, Xiaokun Zhang +3
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent eff…
Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
Renqi Jia, Xiaokun Zhang, Bowei He +4
User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods…
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks
Bowei He, Lihao Yin, Hui-Ling Zhen +4
The widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage. These attack…
PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
Bowei He, Lihao Yin, Hui-Ling Zhen +3
Model pruning is an effective approach for compressing large language models (LLMs). However, this process often leads to significant degradation of model capabilities. While post-…