Papers by Hanghang Tong
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
Copied to clipboard
Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
Event Time Extraction and Propagation via Graph Attention Networks (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on grounding events into a precise timeline has been limited due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. |
| Approach: | They propose a 4-tuple temporal representation for entity slot filling to ground events into a timeline using a graph attention network approach. |
| Outcome: | The proposed approach yields 7.0% match rate over contextualized embedding approaches and 16.3% higher match rate compared to sentence-level manual event time argument annotation. |
To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach (2025.findings-emnlp)
Copied to clipboard
Yuchen Yan, Aakash Kolekar, Sahika Genc, Wenju Xu, Edward W Huang, Anirudh Srinivasan, Mukesh Jain, Qi He, Hanghang Tong
| Challenge: | Existing studies assume that generated answers integrate all relevant information from the textual graph. |
| Approach: | They propose a novel GraphRAG model that integrates all relevant information from the textual graph into the generated answer. |
| Outcome: | Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks. |
Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)
Copied to clipboard
Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong
| Challenge: | Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning. |
| Approach: | They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations. |
| Outcome: | The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion. |
dLLM: Simple Diffusion Language Modeling (2026.acl-demo)
Copied to clipboard
| Challenge: | diffusion language models (DLMs) are evolving rapidly but many lack transparent implementations or are scattered across codebases. |
| Approach: | They propose an open-source framework that unifies diffusion language modeling components while remaining flexible enough to support new methods and architectures. |
| Outcome: | dLLM unifies the core components of diffusion language modeling and makes them easy to customize for new designs. |
Harnessing Consistency for Robust Test-Time LLM Ensemble (2026.findings-eacl)
Copied to clipboard
Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, Hanghang Tong
| Challenge: | Existing efforts to improve LLM ensemble quality have focused on model consistency, but failures are often due to heterogeneous tokenization schemes and varying model expertise. |
| Approach: | They propose a plug-and-play technique that harnesses model consistency for robust LLM ensemble. |
| Outcome: | The proposed technique improves ensemble performance and robustness against erroneous signals. |
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024.acl-long)
Copied to clipboard
| Challenge: | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLM. |
| Approach: | They propose a group discussion framework to enrich the set of discussion mechanisms. |
| Outcome: | The proposed framework performs better on a wide range of reasoning tasks and backbone LLMs. |
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)
Copied to clipboard
Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
| Challenge: | Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts. |
| Approach: | They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents. |
| Outcome: | The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. |
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing due to the nature of the named entity. |
| Approach: | They propose a nested NER model that leverages two key properties pertaining to the named entity, including explicit boundary tokens and tight internal connection between tokens within the boundary. |
| Outcome: | The proposed model achieves state-of-the-art on three public NER datasets. |
PAPERMIND: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs (2026.findings-acl)
Copied to clipboard
Yanjun Zhao, Tianxin Wei, Jiaru Zou, Xuying Ning, Yuanchen Bei, Lingjie Chen, Simmi Rana, Wendy H. Yang, Hanghang Tong, Jingrui He
| Challenge: | Existing benchmarks assess integrated and agent-oriented scientific reasoning in isolation . Existing systems assess integrated reasoning in isolated tasks . |
| Approach: | They propose a benchmark to evaluate integrated and agent-oriented scientific reasoning over research papers. |
| Outcome: | The proposed benchmark evaluates integrated and agent-oriented scientific reasoning over scientific papers. |
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning (2023.findings-acl)
Copied to clipboard
Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
| Challenge: | State-of-the-art methods fail in speculative reasoning task on knowledge graphs . state-of the-art approaches assume correctness of fact is determined by its presence in KG . |
| Approach: | They propose a speculative reasoning task on real-world knowledge graphs . they propose nPUGraph that estimates correctness of both collected and uncollected facts . |
| Outcome: | The proposed framework improves the robustness of a label posterior-aware graph encoder against false positive links and identifies missing facts to provide high-quality grounds of reasoning. |
EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks. |
| Approach: | They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links . |
| Outcome: | The proposed model incorporates event nodes into the original knowledge graphs . it can be used to fuse event information into the KG embeddings on multiple tasks . |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
Copied to clipboard
Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)
Copied to clipboard
| Challenge: | State-of-the-art ConvQA methods struggle with inexplicit question-answer pairs, which can degrade ConvQ performance. |
| Approach: | They propose a reinforcement learning based model, CoRnNet, which utilizes question reformulations generated by large language models to improve ConvQA performance. |
| Outcome: | The proposed model outperforms state-of-the-art ConvQA models by using question reformulations generated by large language models (LLMs). |
Multiplex Graph Neural Network for Extractive Text Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extractive text summarization do not consider multiple types of inter-sentential relationships, nor model intra-sententential relationships. |
| Approach: | They propose a novel method to combine different types of relationships among sentences and words to model sentence embedding. |
| Outcome: | The proposed model is compared with existing methods on CNN/DailyMail benchmark dataset to demonstrate its effectiveness. |
Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR (2026.acl-long)
Copied to clipboard
Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong
| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has improved reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose an online pruning method that prunes rollouts while steering correct ones to enhance learning signals. |
| Outcome: | The proposed method improves average accuracy by +2.30 to +2.99 across GRPO and DAPO on Qwen-3 and LLaMA-3.2 models. |
SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Language Models struggle to fully comprehend and utilize key evidence from the context. |
| Approach: | They propose an inference-time approach that helps LMs focus on key contextual evidence through self-guided explicit highlighting. |
| Outcome: | The proposed method improves on multiple evidence-based QA tasks while maintaining computational efficiency. |