7 citations · 20 across the 40 of their papers we have counts for
48 papers
PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
Shuo Lin, Jinzhe Li, Chenlu Guo +3
Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that user queries are valid and answ…
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
Zewen Jiang, Yahan Li, Chenlu Guo +3
Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-switching requires more than…
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks
Lukas Niehaus, Robin Rawiel, Ulf Krumnack
KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the widely successful TensorFlow Pla…
TRACESTEER: Trace Driven Activation Steering for Hallucination Mitigation in Diffusion Language Models
Xin Zhang, Yili Wang, Yue Tan +5
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive LLMs, yet remain susceptible to hallucination. Activation steering offers a promis…
More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models
Tianxiang Gao, Jinzhe Li, Zhiyuan Li +2
Direct-decision models enable efficient classification and evaluation by selecting from predefined candidates, but their reliability depends on more than prediction accuracy. Acros…
ConflictVLA-Bench: Benchmarking Behavioral Responses of Vision-Language-Action Models to Premise Conflicts
Liyu Hou, Yuan Wu, Yi Chang
While Vision-Language-Action (VLA) models perform strongly on manipulation tasks, their responses to invalid task premises remain underexplored. Existing evaluations of premise con…
What if LLMs Ate Their Words: Causal History Effects in Multi-Turn Interaction
Jinnan Li, Zheren Fu, Yue Wang +3
Multi-turn interaction creates a feedback process in which an LLM's previous responses become context for later behavior. Prior work shows substantial multi-turn degradation and th…
RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendation
Zhongru Chen, Yuan Wu, Yi Chang
Large language models are increasingly used as interactive recommender assistants. Their evaluation should therefore go beyond plausible item recommendation and test whether they c…
Beyond Blind Compliance: Benchmarking Task Verification in OCR Reasoning
Yue Zhou, Yuan Wu, Yi Chang
Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmarks. Yet existing evaluations…
OCR-MetaReasoning Benchmark: Evaluating the Meta-Reasoning Ability of MLLMs in Text-Rich Image Understanding
Gengxu Li, Yuan Wu, Yi Chang
Text-rich image understanding requires multimodal large language models (MLLMs) to organize OCR (Optical Character Recognition)-grounded evidence across words, layout, fields, char…
MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs
Jinzhe Li, Gengxu Li, Jinnan Li +2
As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructions but also on validating th…
CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval
Zhiyuan Li, Linyuan Gao, Xuechun Ding +3
Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a challenging retrieval problem.…
ToolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling Agents
YiShan Zheng, Yuan Wu, Yi Chang
Large language models (LLMs) rely on tool calling as a fundamental agent capability, enabling them to invoke external systems and complete tasks beyond text generation. However, cl…
How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network
Huangchen Xu, Yuan Wu, Yi Chang
LLM-based agents are increasingly deployed in persistent social environments, where generated claims can be posted, replied to, remembered, and reused. We study human-directed ster…
CD-LoRA: Consistency-Driven Low-Rank Adaptation for Multi-Task Fine-Tuning
Qian Zha, Jinda Liu, Yuan Wu +1
While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex routing mechanisms that par…
Thinking vs. NoThinking: Towards Interpreting Reasoning Mechanisms of Large Language Models via Sparse Autoencoders
Bo Cheng, Qiaolin Lu, Yi Chang +1
While Large Language Models (LLMs) employing Chain-of-Thought (CoT) exhibit superior reasoning capabilities, the neural mechanisms distinguishing this explicit Thinking mode from d…
DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
Xin Zhang, Yili Wang, Yue Tan +6
Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs remain vulnerable to hallucinatio…
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
Yupeng Chang, Yuan Wu, Yi Chang
Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models. Under a fixed rank budget, LoRA parameterizes each adapted weig…
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
Yupeng Chang, Yuan Wu, Yi Chang
Critic-free reinforcement learning with verifiable rewards (RLVR), exemplified by Group Relative Policy Optimization (GRPO), avoids training a value function (critic) and reduces m…
How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations
Yuxing Cheng, Yuan Wu, Yi Chang
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled vi…
A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs
Huangchen Xu, Yuan Wu, Yi Chang
Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positiona…
VCIFBench: Evaluating Complex Instruction Following for Video Understanding
Huangchen Xu, Yuan Wu, Yi Chang
Multimodal large language models have made rapid progress in video understanding, yet existing benchmarks largely rely on simple prompts and provide limited evidence about whether…
ClarEval: A Benchmark for Evaluating Clarification Skills of Code Agents under Ambiguous Instructions
Jialin Li, Yuan Wu, Yi Chang
To integrate seamlessly into real-world software engineering, Code Agents must evolve from passive instruction followers into proactive collaborative partners. However, current eva…
AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training
Zhiyuan Li, Yuan Wu, Yi Chang
To stabilize the training of Large Language Models (LLMs), gradient clipping is a nearly ubiquitous heuristic used to alleviate exploding gradients. However, traditional global nor…