Papers by Xin Miao
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)
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| Challenge: | Existing supervised defense methods rely on labeled malicious agents to train a supervised model of malicious behavior. |
| Approach: | They propose an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors. |
| Outcome: | The proposed method detects diverse attack types across MAS with various communication patterns while maintaining superior generalizability compared to baselines. |
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation (2022.emnlp-main)
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| Challenge: | Evaluation metrics for dialogue systems are expensive and time-consuming . current evaluation metrics focus on a single quality or several qualities . |
| Approach: | They propose an interpretable, multi-faceted, and controllable framework to combine dialogue metrics which are good at measuring different qualities. |
| Outcome: | The proposed framework integrates a large number of evaluation metrics to improve the performance of the model. |
SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs. |
| Approach: | They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction. |
| Outcome: | The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models. |
ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency. |
| Approach: | They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets. |
| Outcome: | The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets. |
An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (2024.findings-acl)
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| Challenge: | Existing methods for continual relation extraction (CRE) are rehearsal-based and need to store samples and thus may encounter privacy and security issues. |
| Approach: | They propose an Ensemble-of-Experts framework for rehearsal-free continual relation extraction that discriminates between experts and augments analogous relations across tasks. |
| Outcome: | The proposed method outperforms existing rehearsal-free methods and is even better than existing methods. |
Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved satisfactory performance in counterfactual generation, however, there are misalignments between LLMs and humans which hinder LLM from handling complex tasks like relation extraction. |
| Approach: | They propose to mimic the episodic memory retrieval mechanism of human hippocampus to align LLMs’ generation process with that of humans. |
| Outcome: | The proposed framework improves over existing methods in terms of quality of counterfactuals. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically. |
| Approach: | They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design. |
| Outcome: | The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals. |
YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)
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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen
| Challenge: | prevailing pre-training approaches for large language models involve several complexities. |
| Approach: | They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data . |
| Outcome: | The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data . |
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)
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| Challenge: | Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance. |
| Approach: | They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER) |
| Outcome: | The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs. |
Generating Commonsense Counterfactuals for Stable Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for relation extraction struggle to identify causal terms under the invariant entity constraint. |
| Approach: | They propose a framework to generate commonsense counterfactuals for stable relation extraction by using a knowledge base WordNet and a constituency parser. |
| Outcome: | The proposed framework significantly enhances the stability of relation extraction models. |
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (2021.emnlp-main)
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| Challenge: | Existing models for text classification are limited in performance, resulting in poor rumor detection. |
| Approach: | They propose to use Chinese microblogs to detect rumors using pre-trained language models and auxiliary features such as comments to mask co-attention. |
| Outcome: | The proposed model outperforms the state-of-the-art on Weibo20 and three existing social media datasets. |
The Impact of Language Mixing on Bilingual LLM Reasoning (2025.emnlp-main)
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| Challenge: | Recent studies show multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points . |
| Approach: | They find that multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points . authors suggest that language mixing is not merely a byproduct of multilingual training . |
| Outcome: | The proposed model can be used to predict whether a language switch would benefit or harm reasoning. |
Rethinking Negative Pairs in Code Search (2023.emnlp-main)
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| Challenge: | Comparative learning is a key component in fine-tuning code search models . however, negative samples of InfoNCE may deteriorate its representation learning . |
| Approach: | They propose a loss function that inserts weight terms into InfoNCE to improve contrastive learning. |
| Outcome: | The proposed loss function is a special case of Soft-InfoNCE, the authors show . it is more accurate than other loss functions, and it is faster than other models. |
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (2025.emnlp-main)
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| Challenge: | Empirical studies for communication topology design often overlook why and when sparse and dense topologies help or hinder collaboration. |
| Approach: | They propose a topology design approach that balances error suppression and beneficial information propagation by fusing connectivity patterns from dense and sparse graphs. |
| Outcome: | The proposed topology design achieves superior performance across tasks with sparse and dense graphs. |