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cs.CL2026

RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation for Explainable QA Hallucination Detection

Yiming Huang, Junyan Zhang, Zihao Wang +5

Large Language Models (LLMs) have become powerful, but hallucinations remain a vital obstacle to their trustworthy use. Previous works improved the capability of hallucination dete…

cs.CL2025

EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models

Junquan Huang, Haotian Wu, Yubo Gao +7

Large language models (LLMs) with Chain-of-Thought (CoT) prompting achieve strong reasoning but often produce unnecessarily long explanations, increasing cost and sometimes reducin…

cs.CL2025

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities

Junyan Zhang, Yubo Gao, Yibo Yan +8

The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improv…

cs.CL2025

Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?

Junyan Zhang, Yiming Huang, Shuliang Liu +2

The rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. This study challenges the prevailing "LLM-centric" tren…

cs.CL2025

PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions

Song Dai, Yibo Yan, Jiamin Su +8

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplo…

cs.CL2025

CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality

Junyan Zhang, Shuliang Liu, Aiwei Liu +4

Watermarking technology is a method used to trace the usage of content generated by large language models. Sentence-level watermarking aids in preserving the semantic integrity wit…