Papers by Rui Sheng
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)
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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong
| Challenge: | Existing studies on LLM confidence estimations in languages other than English have been limited to English. |
| Approach: | They propose to use question-related language to prompt LLMs to assess their confidence in large language models. |
| Outcome: | The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations. |
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering (2025.emnlp-main)
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| Challenge: | Misleading visualizations can distort perception and lead to incorrect conclusions. |
| Approach: | They propose a large-scale multimodal dataset to evaluate MLLMs on misleading chart reasoning. |
| Outcome: | The proposed framework evaluates MLLMs on misleading chart reasoning on a large-scale multimodal dataset spanning 21 misleader types and 10 chart types . it contains 3,026 curated examples spanning standard chart code, CSV data, multiple-choice questions, and labeled explanations, validated through iterative MLML checks and exhausted expert human review. |
Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)
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Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani
| Challenge: | Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. |
| Approach: | They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs . |
| Outcome: | The proposed refiner can mitigate context interference in multi-turn search agents. |
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)
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| Challenge: | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts. |
| Approach: | They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training. |
| Outcome: | The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed. |
Locally Aggregated Feature Attribution on Natural Language Model Understanding (2022.naacl-main)
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| Challenge: | a growing popularity of deep-learning models makes model understanding more important . feature attribution methods have shown promising results in computer vision but are not trivial . |
| Approach: | They propose a gradient-based feature attribution method that smooths gradients by aggregating similar reference texts derived from language model embeddings. |
| Outcome: | The proposed method outperforms existing methods on public datasets and key words detection tasks. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)
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| Challenge: | Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies. |
| Approach: | They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters. |
| Outcome: | The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems. |
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous. |
| Approach: | They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge. |
| Outcome: | The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks. |
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing. |
| Approach: | They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities. |
| Outcome: | The proposed framework outperforms baselines by 16.5 - 27.8%. |