Papers by Yicheng Xu
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
Are LLM-based Evaluators Confusing NLG Quality Criteria? (2024.acl-long)
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| Challenge: | Existing studies show that LLMs confuse evaluation criteria, which reduces their reliability. |
| Approach: | They propose a hierarchical classification system for 11 common aspects with corresponding different evaluation criteria. |
| Outcome: | The proposed system is based on 11 common aspects with different evaluation criteria. |
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Recent work on learning from multiple tasks has shown that adding an extra fusion layer to implement knowledge composition is non-scalable for some applications. |
| Approach: | They propose a two-stage knowledge distillation algorithm to extract task specific knowledge by using local data to train a student adapter. |
| Outcome: | Experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation. |
Towards Understanding Omission in Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem . |
| Approach: | They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances . |
| Outcome: | The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available . |
TACR: A Table Alignment-based Cell Selection Method for HybridQA (2023.findings-acl)
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| Challenge: | Hybrid Question-Answering datasets lack a robust reasoning model for text-based QA. |
| Approach: | They propose a table-question-alignment-based cell-selection and reasoning model for hybrid text and table QA. |
| Outcome: | The proposed model outperforms baselines on HybridQA and WikiTableQuestions datasets on cell selection and argumentation. |
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. |
Bayesian Calibration of Win Rate Estimation with LLM Evaluators (2024.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) show the potential of using LLMs as evaluators for text quality evaluation. |
| Approach: | They propose two methods to improve the accuracy of LLM evaluators by Bayesian inference. |
| Outcome: | The proposed methods improve the accuracy of the win rate estimation using LLMs . the proposed methods are based on six datasets covering story generation, summarization, and instruction following tasks . |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
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| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness (2026.findings-acl)
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Bingguang Hao, Zengzhuang Xu, Maolin Wang, Yuntao Wen, Yicheng Chen, Cunyin Peng, Long Chen, Xiangyu Zhao, Jinjie Gu, Chenyi Zhuang, Ji Zhang
| Challenge: | Currently, Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, but its effectiveness is often compromised by two challenges: 1) lengthy Chain-of-Thought (CoT) reasoning tokens dominate training signals over concise function calls in the learning objective; 2) scarcity of hard training examples. |
| Approach: | They propose a framework that uses a self-adjusted signal balancing loss and a hard data re-sampling strategy to selectively generate new, high-quality complex data guided by model errors. |
| Outcome: | The proposed framework surpasses state-of-the-art models like GPT-5 in function calling performance. |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
| Approach: | They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data. |
| Outcome: | The proposed approach improves model performance even in domain-shifted scenarios. |