Papers by Yuchen Pan
F2RL: Factuality and Faithfulness Reinforcement Learning Framework for Claim-Guided Evidence-Supported Counterspeech Generation (2024.emnlp-main)
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| Challenge: | Existing methods for generating evidence-supported counterspeech lack clear guidance with a core claim for organizing evidence. |
| Approach: | They propose a Factuality and Faithfulness Reinforcement Learning framework for generating claim-guided and evidence-supported counterspeech (F2RL) they generate counter-claims based on hate speech and design a self-evaluation mechanism to select the most appropriate one. |
| Outcome: | The proposed framework achieves excellent performance on three benchmark datasets with strong factuality and faithfulness. |
Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)
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| Challenge: | Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity. |
| Approach: | They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models. |
| Outcome: | The proposed framework matches intents with hate mitigation intents and performs well. |
CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution (2026.acl-long)
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Teng Pan, Yuchen Yan, Zixuan Wang, Ruiqing Zhang, Guiyang Hou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Label-free reinforcement learning enables large language models to improve reasoning capabilities . but as training maximizes self-consistency, output diversity collapses, authors say . authors propose a framework where a single model alternates between generator and verifier roles . |
| Approach: | They propose a framework where a model alternates between generator and verifier roles, bootstrapping each other. |
| Outcome: | Experiments show that CoVerRL outperforms label-free baselines on reasoning benchmarks . the framework can be used to improve reasoning abilities without ground-truth supervision . |
Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)
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| Challenge: | Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals. |
| Approach: | They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art performance on three datasets. |
Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans? (2023.emnlp-main)
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| Challenge: | Visual illusions are a phenomenon that is often seen in human perception but are not always faithful to the physical world. |
| Approach: | They build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusion in state-of-the-art VLMs. |
| Outcome: | The proposed dataset reveals that larger models are closer to human perception and more susceptible to visual illusions. |