Papers by Wenhao Hu
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
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| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention (2022.emnlp-main)
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| Challenge: | Recent studies have shown that powerful Transformer architectures produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. |
| Approach: | They propose a method to control the sharpness of the attention distribution by python code and use it to learn a Bayesian approximation of posterior attention. |
| Outcome: | The proposed method improves diversity and novelty while maintaining comparable quality on conditional and unconditional generation tasks. |
Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
| Outcome: | This tutorial outlines the state-of-the-art methods to tackle the problem . it aims to show how to represent knowledge, feed knowledge into a generation model, evaluate results . |
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)
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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
| Challenge: | Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. |
| Approach: | They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset. |
| Outcome: | The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. |
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction (2025.acl-industry)
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Yinghui Li, Shang Qin, Jingheng Ye, Haojing Huang, Yangning Li, Shu-Yu Guo, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Recent studies have shown that Large Language Models’ performance as correctors on Chinese Grammatical Error Correction (CGEC) remains unsatisfactory due to the challenging nature of the task. |
| Approach: | They propose a training framework EXAM that uses LLMs as explainers to enhance CGEC small models and a novel evaluation method SEE that utilizes LLM as evaluators to bring more reasonable evaluations. |
| Outcome: | The proposed methods improve the performance of LLMs on Chinese Grammatical Error Correction (CGEC) task. |
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs). |
| Approach: | They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs. |
| Outcome: | The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026.findings-acl)
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| Challenge: | Existing routing strategies rely on local token probabilities or post-hoc verification, introducing significant inference overhead. |
| Approach: | They propose a step-wise collaboration framework that generates only the first token of each reasoning step and routes it to a larger model only when initial token entropy exceeds a threshold. |
| Outcome: | The proposed approach reduces inference latency while preserving accuracy. |
Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts. |
| Approach: | They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy. |
| Outcome: | The proposed method selectively removes less informative tokens while maintaining performance. |
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation (2022.findings-emnlp)
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| Challenge: | Variational Auto-Encoder (VAE) has been widely adopted in text generation due to its ability to learn flexible representations. |
| Approach: | They propose a Transformer-based recurrent VAE structure that imposes recurrence on segment-wise latent variables with arbitrarily separated text segments and constructs the posterior distribution with residual parameterization. |
| Outcome: | The proposed structure can deduce a non-zero lower bound of the KL term and enhance the entanglement of each segment and preceding latent variables, providing a theoretical guarantee of generation diversity. |
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)
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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
| Challenge: | Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities. |
| Approach: | They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks. |
| Outcome: | The proposed benchmark renders fragmented documents directly from Markdown. |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation (2022.naacl-main)
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| Challenge: | Variational Auto-Encoders are often used for text generation tasks due to the sequential nature of the text. |
| Approach: | They propose a variational Transformer framework that learns a series of layer-wise latent variables with each inferred from those of lower layers and tightly coupled with the hidden states by low-rank tensor product. |
| Outcome: | The proposed framework can learn latent variables from lower layers and incorporate more information. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation (2025.findings-acl)
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| Challenge: | Existing code benchmarks for large language models remain static, resulting in data contamination and unreliable evaluation results. |
| Approach: | They propose a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets and provides a memorization-advantaged benchmark. |
| Outcome: | DynaCode generates 189 million unique nested code problems across 4 units of code complexity and 16 types of call graphs. |