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20242026
most citedDo LLMs "Feel"? Emotion Circuits Discovery and Control

2 citations · 4 across the 11 of their papers we have counts for

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cs.CL2026

LITERARYBIGFIVE: Author-Personalized Text Generation in a Unified Interpretable Space

Jinghui Zhang, Lang Gao, Ao Li +5

Personalized text generation for authors and literary writing is essential for applications such as adaptive writing assistants, creative support tools, and computational literary…

cs.CL2026

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

Zirui Song, Huaxing Liu, Xiang Wang +8

Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outpu…

cs.CL2025

Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies

Zirui Song, Yuan Huang, Junchang Liu +7

Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying natural language and social intelligence. However, most studies red…

cs.CL2025★ 2 cited

Do LLMs "Feel"? Emotion Circuits Discovery and Control

Chenxi Wang, Yixuan Zhang, Ruiji Yu +8

As the demand for emotional intelligence in large language models (LLMs) grows, a key challenge lies in understanding the internal mechanisms that give rise to emotional expression…

cs.CL2025

DyFlow: Dynamic Workflow Framework for Agentic Reasoning

Yanbo Wang, Zixiang Xu, Yue Huang +9

Agent systems based on large language models (LLMs) have shown great potential in complex reasoning tasks, but building efficient and generalizable workflows remains a major challe…

cs.CL2025

Under the Shadow of Babel: How Language Shapes Reasoning in LLMs

Chenxi Wang, Yixuan Zhang, Lang Gao +4

Language is not only a tool for communication but also a medium for human cognition and reasoning. If, as linguistic relativity suggests, the structure of language shapes cognitive…

cs.CL2025

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs

Lang Gao, Kaiyang Wan, Wei Liu +6

Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness. We focus on a common form of bias: when two reference concepts in the model's concept…

cs.CL2025★ 1 cited

SocialMaze: A Benchmark for Evaluating and Enhancing Social Reasoning in Large Language Models in Complex Social Environments

Zixiang Xu, Yanbo Wang, Yue Huang +13

Large language models (LLMs) are increasingly deployed in socially grounded applications, where success requires interpreting context, inferring others' mental states, and reasonin…