Papers by Weihao Wang
The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents (2026.acl-long)
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| Challenge: | a fundamental pillar of trustworthiness is calibration, which refers to an agent’s ability to express confidence that reliably reflects its actual performance. |
| Approach: | They propose a reinforcement learning framework that jointly optimizes task accuracy and calibration, supported by a holistic benchmark of reward designs. |
| Outcome: | The proposed framework improves calibration across tool types and shows that trained agents achieve superior calibration and exhibit robust generalization from local training environments to noisy web settings and to distinct domains such as mathematical reasoning. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration (2024.findings-acl)
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Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) have led to an influx of AI-generated content on the internet, transforming corpus of Information Retrieval (IR) systems from human-written to a coexistence with LLM-generated contents. |
| Approach: | They propose a benchmark named Cocktail that compares IR models with LLMs to find relevant documents and passages from a corpus. |
| Outcome: | The proposed benchmark aims to evaluate IR models in the mixed-sourced data landscape of the LLM era. |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
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| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations (2024.findings-naacl)
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| Challenge: | Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context. |
| Approach: | They propose a model that pre-trains LLMs to learn diverse task-oriented dialogue representations by removing domain knowledge that contradicts TODs. |
| Outcome: | The proposed model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues. |
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)
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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian
| Challenge: | Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic. |
| Approach: | They propose a chain-of-thought reasoning framework with three key designs to address these issues. |
| Outcome: | The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG. |
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)
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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
| Challenge: | Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy. |
| Approach: | They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction. |
| Outcome: | The proposed model improves generalization ability and makes distant link prediction significantly easier. |
On the Perception Bottleneck of VLMs for Chart Understanding (2025.findings-emnlp)
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| Challenge: | a perception bottleneck in large vision-language models is critical for chart understanding . instruction tuning improves the extraction capability of LVLMs, but the vision encoder remains a bottleneck . |
| Approach: | They propose to decompose the perception bottleneck into two components . the vision encoder bottleneck is where visual representation fails to encapsulate the correct information . |
| Outcome: | The proposed approach significantly mitigates the vision encoder bottleneck and improves the ability of LVLMs to comprehend charts. |
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning (2024.acl-long)
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Yejie Wang, Keqing He, Guanting Dong, Pei Wang, Weihao Zeng, Muxi Diao, Weiran Xu, Jingang Wang, Mengdi Zhang, Xunliang Cai
| Challenge: | Numerous code large language models (LLMs) have been proposed to enhance code generation performance. |
| Approach: | They propose a diverse instruction model DolphCoder with self-evaluating for code generation that learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. |
| Outcome: | The proposed model achieves superior performance on the HumanEval and MBPP benchmarks, demonstrating new insights for future code instruction tuning work. |
Using Subtext to Enhance Generative IDRR (2025.acl-short)
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| Challenge: | Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT. |
| Approach: | They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama . |
| Outcome: | The proposed approach achieves higher F1 scores on two benchmarks than previous models. |
Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to quantify uncertainty are limited in vision-language models . however, current models display notable miscalibration across diverse tasks and settings . |
| Approach: | They evaluate verbalized confidence in vision-language models using visual reasoning . they propose a prompting strategy that improves confidence alignment in multimodal settings . |
| Outcome: | The proposed method improves confidence alignment across multimodal settings. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses. |
| Approach: | They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies. |
| Outcome: | The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies. |
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses (2024.lrec-main)
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| Challenge: | Existing pre-trained language models lack diversity and linguistic challenges in task-oriented dialogues. |
| Approach: | They propose a self-bootstrapping dialogue pre-training model called BootTOD . it learns task-oriented dialogue representations via a framework . |
| Outcome: | The proposed model outperforms strong TOD baselines on diverse dialogue tasks. |
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)
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| Challenge: | Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments. |
| Approach: | They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences. |
| Outcome: | The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously. |
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)
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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao
| Challenge: | Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. |
| Approach: | They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China. |
| Outcome: | The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance. |