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20212026
most citedDialogSum: A Real-Life Scenario Dialogue Summarization Dataset

14 citations · 32 across the 11 of their papers we have counts for

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13 papers · 1 filter

cs.CL2026

Can LLM Agents Stick to the Script? A Benchmark for Long-Horizon Consistency in Interactive Narratives

Yingpeng Ma, Jianhao Yan, Bei Shi +6

The rapid advancement of Large Language Models (LLMs) is revolutionizing AI for Games by enabling open-ended and fluid interactive storytelling. However, existing research has larg…

cs.CL2025

The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang

Fenghua Liu, Yulong Chen, Yixuan Liu +3

Large Language Models (LLMs) achieve gold-medal performance across many benchmarks, yet it remains unclear whether such success reflects genuine reasoning or pattern matching. From…

cs.CL20241 cited

Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels

Jianhao Yan, Pingchuan Yan, Yulong Chen +3

This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation us…

cs.CL20242 cited

See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses

Yulong Chen, Yang Liu, Jianhao Yan +6

The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of…

cs.CL20248 cited

GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levels

Jianhao Yan, Pingchuan Yan, Yulong Chen +3

This study comprehensively evaluates the translation quality of Large Language Models (LLMs), specifically GPT-4, against human translators of varying expertise levels across multi…

cs.CL2023

Constituency Parsing using LLMs

Xuefeng Bai, Jialong Wu, Yulong Chen +4

Constituency parsing is a fundamental yet unsolved challenge in natural language processing. In this paper, we examine the potential of recent large language models (LLMs) to addre…