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20242026
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cs.CL2026

If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs

Siqi Fan, Xiusheng Huang, Yiqun Yao +6

Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent int…

cs.CL2025

Event Extraction in Large Language Model

Bobo Li, Xudong Han, Jiang Liu +11

Large language models (LLMs) and multimodal LLMs are changing event extraction (EE): prompting and generation can often produce structured outputs in zero shot or few shot settings…

cs.CL2025

The Price of a Second Thought: On the Evaluation of Reasoning Efficiency in Large Language Models

Siqi Fan, Bowen Qin, Peng Han +3

Recent thinking models trained with reinforcement learning and backward-checking CoT often suffer from overthinking: they produce excessively long outputs even on simple problems,…

cs.CL2025

Evaluating LLM Adaptation to Sociodemographic Factors: User Profile vs. Dialogue History

Qishuai Zhong, Zongmin Li, Siqi Fan +1

Effective engagement by large language models (LLMs) requires adapting responses to users' sociodemographic characteristics, such as age, occupation, and education level. While man…

cs.CL2025

Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks

Yixin Cao, Shibo Hong, Xinze Li +24

Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…

cs.CL2025

FLM-101B: An Open LLM and How to Train It with $100K Budget

Xiang Li, Yiqun Yao, Xin Jiang +10

Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…