Papers by Shiyu Chang
A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation (2025.naacl-long)
Copied to clipboard
| Challenge: | Current large language models (LLMs) produce factually incorrect statements . |
| Approach: | They propose a probabilistic framework for LLM hallucination detection that generates a belief tree by expanding a statement into logically related claims and reasoning globally about the relationships between these claims. |
| Outcome: | The proposed method improves on multiple hallucination detection benchmarks by 3%-9% over state-of-the-art models. |
Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
Copied to clipboard
| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
| Outcome: | The proposed models outperform existing models on a CNN/DM dataset. |
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
Copied to clipboard
| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
Copied to clipboard
| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks (2026.findings-acl)
Copied to clipboard
| Challenge: | Large-language-model (LLM) agents are competent at straightforward web tasks, but struggle with complex tasks. |
| Approach: | They propose a general framework that decomposes web tasks into three subtasks . they show that WebDART lifts end-to-end success rates by 13.7 percentage points . |
| Outcome: | Evaluated on WebChoreArena, WebDART lifts success rates by 13.7 percentage points over previous state-of-the-art agents. |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
Copied to clipboard
| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)
Copied to clipboard
Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang
| Challenge: | Existing methods to integrate external information into a given table neglect the structured nature of the table. |
| Approach: | They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question. |
| Outcome: | The proposed method outperforms strong baselines on three table QA benchmarks. |
A Reinforcement Learning Framework for Robust and Secure LLM Watermarking (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing watermarking algorithms rely on heuristic green/red token lists . however, these lists are inconsistent and can be compromised . |
| Approach: | They propose a framework for robust and secure LLM watermarking using reinforcement learning. |
| Outcome: | The proposed method achieves state-of-the-art trade-off across all criteria with notable improvements in resistance to spoofing attacks without degrading other criteria. |
One-Shot Relational Learning for Knowledge Graphs (D18-1)
Copied to clipboard
| Challenge: | Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training. |
| Approach: | They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures. |
| Outcome: | The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases (2025.acl-demo)
Copied to clipboard
Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Zhang Mei, Shiyu Wang, Hezixiang Hezixiang, Hongzhi Chen, Jin Wei, Bin Cui
| Challenge: | et al., 2017) address domain-specific knowledge barriers, schemas complexity, and computational costs of large LLMs. |
| Approach: | They propose a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. |
| Outcome: | The proposed system achieves 97% execution accuracy on real-world databases . it is faster than existing systems and has a higher performance than existing ones. |
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets. |
| Approach: | They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple benchmarks. |
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader (P19-1)
Copied to clipboard
| Challenge: | Existing models that use incomplete knowledge bases and text data to answer open-domain questions are insufficient to cover full evidence. |
| Approach: | They propose a model which learns to aggregate answer evidence from incomplete knowledge bases and text snippets. |
| Outcome: | The proposed model improves on the widely-used KBQA benchmark WebQSP across settings with different extents of incompleteness. |
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to improve lifelong event detection performance are limited by the limited stored examples. |
| Approach: | They propose to use Episodic Memory Prompts to explicitly retain the learned task-specific knowledge. |
| Outcome: | The proposed method can be used to update a model with new event types while retaining the capability on previously learned types. |
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
Copied to clipboard
| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings (2022.naacl-main)
Copied to clipboard
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Scott Yih, Yoon Kim, James Glass
| Challenge: | Recent work shows that finetuning pretrained models with contrastive learning makes it possible to learn good sentence embeddings without labeled data. |
| Approach: | They propose an unsupervised contrastive learning framework for learning sentence embeddings . they use a masked language model to mask out the edited sentence . |
| Outcome: | The proposed framework outperforms SimCSE on semantic textual similarity tasks by 2.3 absolute points. |
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing (2024.naacl-short)
Copied to clipboard
Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
| Challenge: | Existing adversarial attacks can cause LLMs to make wrong predictions on downstream tasks or generate harmful content misaligned with human values. |
| Approach: | They propose to use randomized smoothing to add noise to the input and then make predictions based on these denoised versions. |
| Outcome: | The proposed method surpasses existing methods in both empirical and certified robustness in defending against adversarial perturbations for both downstream tasks and human alignments (i.e., jailbreak attacks). |
Complementary Evidence Identification in Open-Domain Question Answering (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches to QA that only measure the relevance between the question and each paragraph are not effective. |
| Approach: | They propose a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. |
| Outcome: | The proposed method significantly improves the accuracy of complementary evidence selection in open-domain question answering domain. |
Context-Aware Conversation Thread Detection in Multi-Party Chat (D19-1)
Copied to clipboard
| Challenge: | In multi-party chat, it is common for multiple conversations to occur concurrently . a new model that automatically disentangles conversation threads is proposed . |
| Approach: | They propose a Context-Aware Thread Detection model that automatically disentangles conversation threads in chat logs. |
| Outcome: | The proposed model outperforms state-of-the-art models on four real-world chat logs. |
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior (D18-1)
Copied to clipboard
| Challenge: | Recent research shows that Reinforcement Learning (RL) approaches suffer from the exposure bias problem. |
| Approach: | They propose a Reinforcement Learning (RL) based training framework that constrains the action space using an n-gram language prior. |
| Outcome: | The proposed model is more human readable and graceful. |
Revisiting Who’s Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing and new datasets show that our approach achieves competitive performance in all of the criteria. |
| Approach: | They propose a new task of LLM targeted unlearning where unlearning targets only the information about the unlearning target, rather than everything in the unlearned documents. |
| Outcome: | The proposed method achieves competitive performance on existing and new datasets without optimizing for the aforementioned criteria. |
Deriving Machine Attention from Human Rationales (D18-1)
Copied to clipboard
| Challenge: | Attention-based models are successful when trained on large amounts of data. |
| Approach: | They propose an approach to map human-annotated rationales to high-performing attention and use this to guide models trained in low-resource scenarios. |
| Outcome: | The proposed model yields over 15% error reduction on benchmark datasets. |
A Co-Matching Model for Multi-choice Reading Comprehension (P18-2)
Copied to clipboard
| Challenge: | Existing approaches to machine comprehension are based on pairwise sequence matching, but this approach is not suitable for multi-choice reading comprehension since questions and answers are often equally important. |
| Approach: | They propose a co-matching approach that models whether a passage can match both a question and a candidate answer using a dataset from Chinese exams. |
| Outcome: | The proposed approach achieves state-of-the-art on the RACE dataset from Chinese middle and high school English examinations. |
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. |
| Approach: | They propose to re-formulate IF game solving as Multi-Passage Reading Comprehension tasks using context-query attention mechanisms and structured prediction to efficiently generate and evaluate action outputs. |
| Outcome: | The proposed methods achieve high winning rates and low data requirements on the recent IF benchmark (Jericho) |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
Copied to clipboard
| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
Copied to clipboard
Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |
Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
Copied to clipboard
Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
| Challenge: | Existing methods for few-shot learning are insufficient to capture task variations in natural language domains. |
| Approach: | They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. |
| Outcome: | The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets. |