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20202026
most citedLarge Language Models Meet NLP: A Survey

20 citations · 55 across the 34 of their papers we have counts for

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Showing 2025 · cs.CLShow all

12 papers · 2 filters

cs.CL2025

Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks

Jiannan Guan, Qiguang Chen, Libo Qin +5

Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks that require generating comprehensive and d…

cs.CL2025

The Universal Landscape of Human Reasoning

Qiguang Chen, Jinhao Liu, Libo Qin +14

Understanding how information is dynamically accumulated and transformed in human reasoning has long challenged cognitive psychology, philosophy, and artificial intelligence. Exist…

cs.CL2025

COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes

Yunwen Li, Shuangshuang Ying, Xingwei Qu +16

Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. W…

cs.CL2025

AutoPR: Let's Automate Your Academic Promotion!

Qiguang Chen, Zheng Yan, Mingda Yang +10

As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to en…

cs.CL2025

Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models

Qiguang Chen, Hanjing Li, Libo Qin +7

Recently, Diffusion Large Language Models (DLLMs) have offered high throughput and effective sequential reasoning, making them a competitive alternative to autoregressive LLMs (ALL…

cs.CL2025

Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures

Shuangshuang Ying, Yunwen Li, Xingwei Qu +21

Current preference learning methods achieve high accuracy on standard benchmarks but exhibit significant performance degradation when objective quality signals are removed. We intr…