1 citations · 1 across the 5 of their papers we have counts for
5 papers
DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
Xi Fang, Weijie Xu, Yingqiang Ge +3
Personalization changes what a model says to a user; we show that it can also change the reasoning trajectory used to justify the response. Modern LLMs personalize interactions by…
Can MLLMs "Read" What is Missing?
Jindi Guo, Chaozheng Huang, Xi Fang
We introduce MMTR-Bench, a benchmark designed to evaluate the intrinsic ability of Multimodal Large Language Models (MLLMs) to reconstruct masked text directly from visual context.…
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
Xi Fang, Weijie Xu, Yuchong Zhang +3
When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI sys…
Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective
Weijie Xu, Yiwen Wang, Chi Xue +4
Large Language Models (LLMs) often generate responses with inherent biases, undermining their reliability in real-world applications. Existing evaluation methods often overlook bia…
SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions
Weijie Xu, Shixian Cui, Xi Fang +3
Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of o…