Papers by Weitao Ma
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)
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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem. |
| Approach: | They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task. |
| Outcome: | The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis. |
Beyond "I Don’t Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty (2026.acl-long)
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| Challenge: | Prior studies treat refusal as a generic "I don't know" lack of distinction limits downstream action decisions like requesting clarification or invoking external tools. |
| Approach: | They propose a benchmark to evaluate explicit uncertainty attribution in large language models. |
| Outcome: | The proposed method improves uncertainty attribution while preserving answer accuracy. |
Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)
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| Challenge: | Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users. |
| Approach: | They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information. |
| Outcome: | The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin
| Challenge: | Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage. |
| Approach: | They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
| Outcome: | The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
Citation-Enhanced Generation for LLM-based Chatbots (2024.acl-long)
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| Challenge: | Existing efforts to alleviate hallucination in chatbots require additional training and data annotation. |
| Approach: | They propose a Citation-Enhanced Generation approach that uses retrieval argumentation to generate citations and a natural language inference-based citation generation module to generate content. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three benchmarks. |
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)
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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation . |
| Approach: | They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input. |
| Outcome: | The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. |
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)
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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin
| Challenge: | Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment. |
| Approach: | They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning. |
| Outcome: | The proposed framework outperforms baselines and supports generalization across different model configurations and backbones. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
UR2 : Unify RAG and Reasoning through Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing attempts to unify large language models are limited to open-domain QA with fixed retrieval settings. |
| Approach: | They propose a general reinforcement learning framework that dynamically coordinates retrieval and reasoning. |
| Outcome: | The proposed framework outperforms existing paradigms on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks. |
x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language. |
| Approach: | They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. |
| Outcome: | The proposed model can reason in a single dominant language on a per-instance basis. |
ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026.acl-long)
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| Challenge: | Existing memory benchmarks for LLMs evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. |
| Approach: | They propose a benchmark that evaluates implicit memory using three constructs from non-declarative memory. |
| Outcome: | The new benchmark reframes evaluation from "what agents recall" to "what they automatically enact" no model exceeds 66% overall, with top performers far below human baselines . |
Two-Stage Parameter Alignment for Multi-LoRA Merging in Large Language Models (2026.findings-acl)
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| Challenge: | Current general model merging methods are prone to parameter interference problems . a novel two-stage parameter alignment framework is proposed to address this problem . |
| Approach: | They propose a two-stage parameter alignment framework that integrates low-rank LoRAs . they propose to reduce the computational complexity of existing methods by preserving fine-grained functions . |
| Outcome: | The proposed framework exhibits greater robustness than other methods in high-rank and high-interference scenarios while preserving fine-grained functions. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong
| Challenge: | Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes. |
| Approach: | They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer. |
| Outcome: | The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks. |
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)
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| Challenge: | RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships . |
| Approach: | They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content. |
| Outcome: | The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets. |
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions. |
| Approach: | They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. |
| Outcome: | The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)
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| Challenge: | Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content. |
| Approach: | They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size. |
| Outcome: | The proposed task systematically evaluates popular unlearning algorithms and reveals that the knowledge coverage of the forget set and its size play pivotal roles. |
Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning . |
| Approach: | They propose a framework that teaches large language models to generate fine-grained citations. |
| Outcome: | The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality. |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong
| Challenge: | Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding. |
| Approach: | They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. |
| Outcome: | The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet. |
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin
| Challenge: | Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing. |
| Approach: | They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations. |
| Outcome: | The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks. |