Papers by Yun Xue
Advancing Collaborative Debates with Role Differentiation through Multi-Agent Reinforcement Learning (2025.acl-long)
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| Challenge: | Multi-agent collaborative tasks exhibit exceptional capabilities in natural language applications and generation. |
| Approach: | They propose a multi-LLM Cooperation framework with automatic role assignment capabilities that allows multiple agents to embed roles in turn-based speaking. |
| Outcome: | The proposed framework improves collaboration and expertise among agents and teams by enabling them to share roles and develop complementary strengths from the optimization level. |
Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction (2024.lrec-main)
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| Challenge: | Argument pair extraction (APE) aims to extract interactive argument pairs from two separate passages. |
| Approach: | They propose to tackle the lexical and semantic relevance of arguments with a pre-trained Rouge-guided Transformer (ROT) by using a word graph and a gating mechanism to fuse two graphs. |
| Outcome: | The proposed approach achieves state-of-the-art on F1 score and significantly improves on existing best alternative. |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
| Approach: | They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text. |
| Outcome: | The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date. |
MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)
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| Challenge: | Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures. |
| Approach: | They propose to use graph kernel search to discover subgraphs from the entire graph to bridge the graph and LLMs, helping with graph retrieval and LRM generation. |
| Outcome: | The proposed method achieves state-of-the-art on two graph-based tasks and the results are published in the journal Nature. |
MeMoTune: A Measure and Moment-Driven Fine-Tuning Framework for Quantized Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods combine quantization with parameter-efficient fine-tuning but fail to meet practical performance requirements. |
| Approach: | They propose a measure and moment approach to optimize objective function for superior fine-tuning results by scaling the update process through a gradient. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on tasks like text generation, summarization, and understanding. |
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information. |
| Approach: | They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset. |
D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)
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| Challenge: | Existing methods for multimodal sentiment detection use the same fixed framework to classify the sentiment polarity of image-text pairs. |
| Approach: | They propose a multimodal dynamic interaction model that uses a fixed framework to classify the sentiment polarity of a given imagetext pair. |
| Outcome: | The proposed model outperforms state-of-the-art models on three publicly available datasets. |
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
| Outcome: | The survey examines the effectiveness of MERC and its evaluation strategies. |
PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning (2024.findings-acl)
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| Challenge: | In-context instruction tuning is a method that fine tunes with supervised instruction data to enhance the instruction following ability of large language models. |
| Approach: | They propose an in-context instruction tuning method that incorporates positive and negative examples into the prompt for better performance. |
| Outcome: | The proposed method outperforms baseline instruction tuning methods on in-domain and out-domain tasks up to 9.16 and 3.14 average ROUGE-L scores. |
MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis (2026.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has seen success with English texts, but real-world social media interactions often involve multiple languages. |
| Approach: | They propose a framework for cross-lingual ABSA that incorporates code-switched bilingual sentences into the language discriminator and consistency training modules to enhance cross-linguistic alignment. |
| Outcome: | The proposed framework achieves cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. |
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |