Papers by Xiaoyang Liu
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives (2024.emnlp-main)
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| Challenge: | Using sports data, an LLM can analyze sports narratives to infer points from actions, identify related entities, attribute points accurately to players and teams, and draw conclusions. |
| Approach: | They propose a method to synthesize NBA basketball game narratives using real NBA basketball data and propose 'SportsGen' they find that most models fail to accurately aggregate basketball scores due to frequent scoring patterns and open-source models suffer from significant score hallucinations. |
| Outcome: | The proposed method can evaluate LLMs’ reasoning capabilities under complex scenarios with varying narrative lengths and density of information. |
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4 (2023.emnlp-main)
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| Challenge: | Human preference judgments are important in large language models to produce outputs that align with human values. |
| Approach: | They conduct an in-depth examination of pairwise human judgments released by OpenAI . they find that most favored factors vary across tasks and genres . |
| Outcome: | The proposed model reveals that most favored factors vary across tasks and genres . the findings have implications on the construction of balanced datasets in human preference evaluations - crucial step in shaping behavior of future LLMs. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
Generating User-Engaging News Headlines (2023.acl-long)
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| Challenge: | Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article. |
| Approach: | They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation. |
| Outcome: | The proposed framework can generate personalized headlines that meet the needs of a diverse audience. |
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)
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| Challenge: | Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness. |
| Approach: | They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations. |
| Outcome: | The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. |
MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation (2026.acl-long)
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Dawei Wang, Di Zhao, Xinyuan Liu, Marci Chi Ma, Xiaoyang Liu, Chengming Zhou, Gary Ushaw, Richard Davison
| Challenge: | Embodied AI systems are open, where agents may leave or enter mid-task due to hardware failures or task-related errors. |
| Approach: | They propose a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models. |
| Outcome: | The proposed framework can guide agents toward effective cooperation in complex tasks of different types. |
Tracing the Light of Thought: A Probabilistic Self- and Cross-Consistency Verification Mechanism Improving Mathematical Reasoning in LLMs (2026.findings-acl)
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| Challenge: | Existing methods for evaluating reasoning paths are not efficient, but they are prone to errors. |
| Approach: | They propose a probabilistic self- and cross-consistency framework for mathematical reasoning that employs an accept-reject mechanism to encourage high-quality reasoning paths. |
| Outcome: | The proposed framework improves on 9 LLMs across 4 challenging benchmarks. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs (2024.acl-long)
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| Challenge: | Large language models can handle text and data, but blending text and numerical data presents significant challenges. |
| Approach: | They propose four tasks to evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics. |
| Outcome: | The proposed tasks evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics. |
InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024.findings-acl)
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Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
| Challenge: | Existing methods for evaluating Large Language Models (LLMs) ability to follow instructions have not been able to provide a detailed analysis of their compliance with instructions. |
| Approach: | They propose a new metric for evaluating Large Language Models' ability to follow instructions and a benchmark for DRFR. |
| Outcome: | The proposed metric and benchmark compared with traditional scoring methods and explores annotation sources including human experts, crowd-sourced workers, and GPT-4. |
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)
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| Challenge: | Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. |
| Approach: | They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types . |
| Outcome: | The proposed framework outperforms existing models that rely on training. |
Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)
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| Challenge: | Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate . |
| Approach: | They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
| Outcome: | The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
Proactive Guidance of Multi-Turn Conversation in Industrial Search (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users’ interactions. |
| Approach: | They propose a goal-adaptive supervised fine-tuning framework that generates proactive guidance for users to click for the next turn of the conversation. |
| Outcome: | The proposed framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement). |
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024.naacl-long)
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Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
| Challenge: | Existing large language models have limited ability to perform tasks effectively. |
| Approach: | They propose a large-scale multimodal chart instruction dataset with 600k instances supporting diverse tasks and chart types. |
| Outcome: | The proposed LMM achieves state-of-the-art performance on existing chart QA benchmarks. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |
Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)
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| Challenge: | Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge. |
| Approach: | They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences. |
| Outcome: | The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation. |
Understanding Differential Search Index for Text Retrieval (2023.findings-acl)
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| Challenge: | Differentiable Search Index (DSI) is a new information retrieval framework . however, due to the black-box nature of the end-to-end neural architecture, it remains unclear to what extent it possesses basic indexing and retrieval abilities. |
| Approach: | They propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model. |
| Outcome: | The proposed method outperforms baselines on various datasets. |