Papers by Xuchao Zhang
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)
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| Challenge: | Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages . |
| Approach: | They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks. |
| Outcome: | The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI). |
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
CARMO: Dynamic Criteria Generation for Context Aware Reward Modelling (2025.findings-acl)
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Taneesh Gupta, Shivam Shandilya, Xuchao Zhang, Rahul Madhavan, Supriyo Ghosh, Chetan Bansal, Huaxiu Yao, Saravan Rajmohan
| Challenge: | Reward modeling in large language models is susceptible to reward hacking . flawed reward signals often lead to outputs that optimize for spurious correlates . |
| Approach: | They propose a new approach that generates dynamic, context-relevant criteria to ground the reward model prior to producing reward scores. |
| Outcome: | The proposed approach generates dynamic, context-relevant criteria to ground the model prior to producing reward scores. |
Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)
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| Challenge: | Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. |
| Approach: | They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features . |
| Outcome: | The proposed method achieves a better F1 score than baseline models on real-world data sets. |
PKAD: Pretrained Knowledge is All You Need to Detect and Mitigate Textual Backdoor Attacks (2024.findings-emnlp)
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| Challenge: | Current defense methods can be classified into inference-time and training-time ones based on their execution phase. |
| Approach: | They propose a two-stage poison detection strategy using pre-trained language models to detect poisoned samples before model training. |
| Outcome: | The proposed method achieves better performance than current methods more quickly and with fewer training costs. |
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)
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| Challenge: | Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits. |
| Approach: | They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates . |
| Outcome: | The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE. |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) enhanced with retrieval augmentation have shown great performance in many applications, but their computational overhead and additional retrieval step limit their effectiveness in real-time tasks. |
| Approach: | They propose a system that combines a cloud-based LLM with a smaller client-side model through retrieval augmented memory to provide real-time text prediction. |
| Outcome: | The proposed system can generate better responses from the cloud-based model while maintaining low latency. |
SynthAgent: Adapting Web Agents with Synthetic Supervision (2026.acl-long)
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Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao
| Challenge: | Existing studies have focused on synthetic supervision but have encountered data quality issues. |
| Approach: | They propose a fully synthetic supervision framework that aims at improving data quality via dual refinement of both tasks and trajectories. |
| Outcome: | The proposed framework outperforms existing methods on standardized benchmarks and shows promising results on a standardized test. |
Towards More Accurate Uncertainty Estimation In Text Classification (2020.emnlp-main)
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Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao, ChangTien Lu
| Challenge: | Existing models of uncertainty score depend on winning score, which is the maximum probability in a semantic vector. |
| Approach: | They propose to generate accurate uncertainty score by improving the confidence of winning scores by reducing the effect of overconfidence of winning score and considering the impact of different categories simultaneously. |
| Outcome: | The proposed model reduces the effect of overconfidence of winning score and considers impact of different categories of uncertainty simultaneously. |
Verifiable Format Control for Large Language Model Generations (2025.findings-naacl)
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| Challenge: | Existing methods focus on benchmarking general instruction following while overlooking how to improve specific format following ability for small LLMs. |
| Approach: | They propose to synthesize massive datasets to improve LLMs' format following abilities by using a verifiable format following feature. |
| Outcome: | The proposed method improves the format following ability of small LLMs with about 7B parameters. |
Synergistic Weak-Strong Collaboration by Aligning Preferences (2025.acl-long)
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Yizhu Jiao, Xuchao Zhang, Zhaoyang Wang, Yubo Ma, Zhun Deng, Rujia Wang, Chetan Bansal, Saravan Rajmohan, Jiawei Han, Huaxiu Yao
| Challenge: | Current Large Language Models excel in general reasoning yet struggle with specialized tasks requiring proprietary or domain-specific knowledge. |
| Approach: | They propose a collaborative framework that pairs a specialized weak model with a general strong model to optimize collaboration. |
| Outcome: | The proposed framework outperforms each model alone by leveraging complementary strengths. |