AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models

Challenge:	Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks.
Approach:	They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification .
Outcome:	The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs.

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Challenge

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Outcome

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Alperen Yildiz, Sin G Teo, Yiling Lou, Yebo Feng, Chong Wang, Dinil Mon Divakaran

Programming Languages in NLP

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AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding (2026.findings-acl)

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Samuel Osebe, Fan Yang, Junyi Li, Yue Gu, Yongxin Wang, Satyapriya Krishna, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Weitong Ruan

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) are evolving rapidly on code generation tasks.
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AutoCVSS: Assessing the Performance of LLMs for Automated Software Vulnerability Scoring (2025.emnlp-industry)

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Davide Sanvito, Giovanni Arriciati, Giuseppe Siracusano, Roberto Bifulco, Michele Carminati

Language Models

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LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

Responsible & Trustworthy NLP

Challenge:	Using generic and efficient benchmark generators, human annotators are limited by inefficiency . current benchmark generator methods rely on seed signals, leading to long cycles and high costs .
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Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)

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Zixiang Xu, Yanbo Wang, Yue Huang, Xiuying Chen, Jieyu Zhao, Meng Jiang, Xiangliang Zhang

Language Models

Challenge:	Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge.
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Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)

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Yu Fu, Erfan Shayegan, Md. Mamun Al Abdullah, Pedram Zaree, Nael Abu-Ghazaleh, Yue Dong

Robustness in NLP

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Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)

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Xiaohu Du, Ming Wen, Jiahao Zhu, Zifan Xie, Bin Ji, Huijun Liu, Xuanhua Shi, Hai Jin

Language Models

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AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)

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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao

Language Models

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LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)

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Jiahao Ying, Mingbao Lin, Yixin Cao, Wei Tang, Bo Wang, Qianru Sun, Xuanjing Huang, Shuicheng Yan

Challenge:	Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors.
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APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)

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Jun Rao, Zepeng Lin, Xuebo Liu, Xiaopeng Ke, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

Semantic Text Processing

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