Papers by Xian Gao
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations (2025.naacl-long)
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Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi
| Challenge: | Existing benchmarks for recommendation explanation evaluation lack item diversity and user preferences data. |
| Approach: | They propose a model-agnostic recommendation explanation evaluation benchmark based on Amazon e-commerce categories with implicit preferences . they propose two novel automatic evaluators that enable scalable and human-preference aligned evaluation of explanations . |
| Outcome: | The proposed model-agnostic evaluation benchmark outperforms existing methods in a variety of domains. |
Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)
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Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li
| Challenge: | Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL). |
| Approach: | They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer. |
| Outcome: | Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks . |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)
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| Challenge: | Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation. |
| Approach: | They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space. |
| Outcome: | The proposed framework improves cross-lingual alignment and reduces redundancy. |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
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Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
| Approach: | They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals. |
| Outcome: | The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
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| Challenge: | Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue. |
| Approach: | They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items . |
| Outcome: | The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set. |
Does Large Language Model Contain Task-Specific Neurons? (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks. |
| Approach: | They propose a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST) this method identifies task- specific neurons by concentrating on the most significant tokens during task processing, eliminating redundant tokens and minimizing interference from non-essential neurons. |
| Outcome: | The proposed method can locate task-specific neurons across eight public tasks. |
Beyond Uniform SVD: Dual-Level Optimization across Columns and Modules for LLM Compression (2026.findings-acl)
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| Challenge: | Existing methods for low-rank decomposition overlook decomposing errors and suboptimal approximation. |
| Approach: | They propose a low-rank decomposition framework that integrates low-level optimization at column and module levels. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and baselines in SVD and pruning. |
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (2025.coling-main)
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| Challenge: | a new benchmark evaluates video-based optical character recognition (Video OCR) performance of multi-modal models in videos . the benchmark aims to improve video LLMs' ability to extract text from video content . previous benchmarks have focused on video QA, but not video-related QA. |
| Approach: | They propose to evaluate the video OCR performance of multi-modal models in videos . they use a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement . |
| Outcome: | The proposed benchmark includes 1,028 videos and 2,961 question-answer pairs . it integrates the OCR ability of image LLMs with manual refinement . |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
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Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |