Papers by Xuan Guo
Multi-teacher Distillation for Multilingual Spelling Correction (2023.emnlp-industry)
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| Challenge: | a multilingual spelling correction model is needed to meet the tight latency requirements of multilingual NLP . a monolingual teacher model is trained for each language/locale, and individual models are distilled into a single student model . |
| Approach: | They propose a multilingual approach to spelling correction using multi-teacher distillation . they train a monolingual teacher model for each language and distill them into a single model . |
| Outcome: | The proposed model can meet the tight latency requirements of deployed services. |
DebateQA: Evaluating Question Answering on Debatable Knowledge (2026.findings-eacl)
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
| Approach: | They propose to use a dataset of 2,941 debatable questions to assess their ability to provide comprehensive answers to inherently debatably asked questions. |
| Outcome: | The proposed model performs well on 2,941 debatable questions accompanied by human-annotated partial answers that capture a variety of perspectives. |
Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models (2026.findings-acl)
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| Challenge: | Large Speech Language Models (LSLMs) typically operate at high token rates to ensure acoustic fidelity, yet this results in sequence lengths that exceed the underlying semantic content, incurring prohibitive inference costs. |
| Approach: | They propose a token-based token merging mechanism that uses a training-free token pooling mechanism to reduce prefilling FLOPs by 27.48% while maintaining competitive accuracy. |
| Outcome: | The proposed method reduces prefilling FLOPs by 27.48% while maintaining competitive accuracy. |
Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)
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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo
| Challenge: | Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification . |
| Approach: | They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning. |
| Outcome: | The proposed method significantly improves reasoning capabilities of Large Language Models. |
Context-Aware Tracking and Dynamic Introduction for Incomplete Utterance Rewriting in Extended Multi-Turn Dialogues (2024.findings-acl)
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| Challenge: | Existing methods to reconstruct utterance with omitted information and pronouns are limited to brief multi-turn dialogues. |
| Approach: | They propose a method to reconstruct utterance with omitted information and pronouns to be standalone and complete based on context. |
| Outcome: | The proposed method improves existing models and achieves state-of-the-art on three benchmarks. |
Retrieval Augmented Spelling Correction for E-Commerce Applications (2024.emnlp-industry)
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| Challenge: | e-commerce spelling correction services face a challenge with new brand names . we propose a new approach that uses a fine-tuned retrieval algorithm to correct for brand names. |
| Approach: | They propose a method that uses product names to be incorporated into a large language model to do contextual spelling correction. |
| Outcome: | The proposed approach improves performance with only minor latency increases . the proposed approach is more efficient than a stand-alone LLM . |
MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025.findings-acl)
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| Challenge: | Large Multimodal Models (LMMs) have raised concerns about model toxicity. |
| Approach: | They propose a model to measure the toxicity gap between models and their hard level to determine whether they can handle dual-implicit toxicity. |
| Outcome: | The proposed model can handle dual-implicit toxicity effectively on 13 prominent LMMs, but its performance drops significantly in hard level. |