Forewarned is Forearmed: Harnessing LLMs for Data Synthesis via Failure-Induced Exploration
Jan 1, 2025·,
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Qintong Li
Jiahui Gao
Sheng Wang
Renjie Pi
Xueliang Zhao
Chuan Wu
Xin Jiang
Zhenguo Li
Lingpeng Kong
Abstract
Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. This paper presents ReverseGen, a novel approach designed to automatically generate effective training samples that expose the weaknesses of LLMs through failure-inducing exploration.
Type
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In The Thirteenth International Conference on Learning Representations
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Sheng Wang
(Forence)
PhD Graduate in Computer Science
Sheng Wang is a PhD graduate from The University of Hong Kong, supervised by Prof. Chuan Wu and Prof. Lingpeng Kong.
His research focuses on Agent, LLM Super-Alignment, and Data Synthesis. He has published 14+ papers in top-tier
conferences including NIPS2025 (Spotlight), ICLR2025, ACL2024/2025, EMNLP2025.
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