Chen Gong
Chen Gong has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Sentiment & emotion, Summarization; often with Guohong Fu, Han Hu, Indraneil Paul.
OpenAlex · h-index 0 · 2 works · 0 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 153 times in our library · ⭐ Paper Stars ranking: #5883
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 2 worksSoftware · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work
🧭 Topics 7Research subjects and languages the work is about.Code & programming languages · 1 workLanguage models & pretraining · 2 worksReasoning · 1 workSentiment & emotion · 1 workSpeech & spoken language · 1 workSummarization · 1 workVision & language · 1 work
Works by year
2020s
Papers
- BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions (2024 · 176 citations)
Popular code benchmarks such as HumanEval and MBPP mostly test short, standalone algorithmic exercises, and recent models score near the top on them. - SCAN: Structured Capability Assessment and Navigation for LLMs (2026 · 1 citation)
Most automatic LLM benchmarks try to reproduce leaderboard orderings such as Chatbot Arena. They reveal little about where a particular model is strong or weak. SCAN (Structured Capability Assessment and Navigation) treats evaluation as a way to understand a model rather than to rank it. - Locate and Explain: Joint Multimodal Emotion Cause Extraction and Summarization in Conversation (2026)
Studies of why speakers feel emotions in multimodal dialogue usually take one of two approaches. Some pick out the utterances that trigger an emotion, which shows where the cause is but not what it is. Others write free-text cause descriptions, which explain the cause but cannot be traced to specific turns. The… - Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language Models (2026)
Large Audio Language Models (LALMs) that use chain-of-thought tend to apply the same reasoning depth to every question.
Co-authors here
- Guohong Fu
- Han Hu
- Indraneil Paul
- Siqi Bao
- Terry Yue Zhuo
- Tianle Gu
- Wenhao Yu
- Xin Tian
- Yujiu Yang
- Zhenghua Li
- Zongqi Wang
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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