Adversarial competition between attacker and defender teams generates diverse multi-turn conversational data that improves LLM performance on secure code generation benchmarks by 18-29%.
Data augmentation using llms: Data perspectives, learning paradigms and challenges
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DeBERTa-V3-base with focal loss, discourse features, and LLM-augmented data for minority classes achieves 0.76 Macro F1 on clarity-level classification of political QA pairs, ranking 8th in SemEval-2026 Task 6.
The paper surveys reinforced reasoning techniques for LLMs, covering automated data construction, learning-to-reason methods, and test-time scaling as steps toward Large Reasoning Models.
citing papers explorer
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Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition
Adversarial competition between attacker and defender teams generates diverse multi-turn conversational data that improves LLM performance on secure code generation benchmarks by 18-29%.
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Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions
DeBERTa-V3-base with focal loss, discourse features, and LLM-augmented data for minority classes achieves 0.76 Macro F1 on clarity-level classification of political QA pairs, ranking 8th in SemEval-2026 Task 6.
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Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
The paper surveys reinforced reasoning techniques for LLMs, covering automated data construction, learning-to-reason methods, and test-time scaling as steps toward Large Reasoning Models.