Papers by Wenting Zhao
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Challenges in Trustworthy Human Evaluation of Chatbots (2025.findings-naacl)
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| Challenge: | apathetic or adversarial annotators can corrupt the reliability of open leaderboard rankings . human annotation is widely accepted as the gold standard for open-ended text generation tasks . |
| Approach: | They show that bad annotations can corrupt the reliability of open leaderboard rankings . they argue that human annotation is widely accepted as the gold standard . |
| Outcome: | The proposed algorithm can corrupt the reliability of open leaderboard rankings by up to 5 places. |
Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023.emnlp-main)
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| Challenge: | Existing methods rely on supervision for both answers and rationales, but they have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. |
| Approach: | They propose a principled, probabilistic approach for training explainable multi-hop question answering systems without rationale supervision. |
| Outcome: | The proposed method is more accurate at selecting rationales than previous methods while maintaining similar accuracy in predicting answers. |
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)
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| Challenge: | Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts. |
| Approach: | They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation. |
| Outcome: | The proposed model achieves faster speed and keeps translation quality compared with other models. |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)
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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu
| Challenge: | Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data. |
| Approach: | They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach. |
| Outcome: | The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts. |
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)
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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing
| Challenge: | Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases. |
| Approach: | They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. |
| Outcome: | The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |
I Could’ve Asked That: Reformulating Unanswerable Questions (2024.emnlp-main)
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| Challenge: | Existing large language models do not assist users in reformulating unanswerable questions . a recent study found that the models failed to reformulate questions based on assumptions that conflict with or cannot be verified with the information available in documents. |
| Approach: | They evaluate open-source and proprietary LLMs on couldAsk to evaluate their performance . they found that GPT-4 and Llama2-7B successfully reformulate questions only 26% and 12% of the time . |
| Outcome: | The proposed model successfully reformulates questions only 26% and 12% of the time . the proposed model is not able to reformulate questions, but it can be improved . |
Towards LLM Agents for Earth Observation (2026.findings-acl)
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Chia Hsiang Kao, Wenting Zhao, Cheryl Lam, Aarush Umap, Shreelekha Revankar, Samuel Speas, Snehal Bhagat, Rajeev Datta, Cheng Perng Phoo, Utkarsh Mall, Carl Vondrick, Kavita Bala, Bharath Hariharan
| Challenge: | specialized automated systems for specific earth observation tasks lack flexibility for general-purpose, customized queries. |
| Approach: | They propose a coding benchmark of 408 yes/no questions from NASA Earth Observatory articles . they analyze the impact of using JavaScript API versus Python and the effect of providing documentation . |
| Outcome: | The proposed frameworks reduce errors by 60%, but are only marginally above random chance. |
WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild (2024.emnlp-demo)
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| Challenge: | Currently, the volume and complexity of chat logs makes it difficult to analyze individual conversations. |
| Approach: | They propose a tool that enables fast, versatile, and large-scale conversation analysis by combining search and visualization capabilities with a list of criteria. |
| Outcome: | The proposed tool can be extended to handle millions of chat logs and other datasets. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
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| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |
Compositional Task-Oriented Parsing as Abstractive Question Answering (2022.naacl-main)
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| Challenge: | Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. |
| Approach: | They propose to reduce TOP to abstractive question answering by using canonical paraphrasing to generate linearized parse trees. |
| Outcome: | The proposed technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings. |
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots (2021.findings-emnlp)
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| Challenge: | Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. |
| Approach: | They propose a novel approach to generate faithful table-to-text sentences using limited data . they aim to exploit table structure and natural linguistic information to generate accurate sentences . |
| Outcome: | The proposed approach generates higher qualified sentences when compared with state-of-the-art models on humans, songs, and books. |
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations (2024.naacl-long)
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Wenting Zhao, Justin Chiu, Jena Hwang, Faeze Brahman, Jack Hessel, Sanjiban Choudhury, Yejin Choi, Xiang Li, Alane Suhr
| Challenge: | Existing work evaluating commonsense reasoning focuses on making inferences about common, everyday situations. |
| Approach: | They propose to use an English language corpus to investigate commonsense reasoning . they characterize performance differences between human explainers and best-performing large language models . |
| Outcome: | The proposed method reduces the loss rate of human-written explanations on commonsense reasoning compared with the vanilla supervised fine-tuning approach . |
Great Memory, Shallow Reasoning: Limits of kNN-LMs (2025.naacl-short)
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| Challenge: | Existing models trained on poor quality data have shown strong performance in language modeling and some downstream benchmarks. |
| Approach: | They evaluate kNN-LMs on a diverse set of tasks and evaluate their performance. |
| Outcome: | The proposed extension could improve on a variety of tasks, but it fails to perform on reasoning tasks that require integrating multiple pieces of information. |
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations (2021.emnlp-main)
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| Challenge: | Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format. |
| Approach: | They propose to use multiple-choice MRC to explain a trained model and reveal how it arrives at the prediction by punishing illogical attributions. |
| Outcome: | The proposed method improves model performance without external information and model structure change without any external information. |
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations (2023.acl-long)
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| Challenge: | Existing approaches for abductive reasoning in natural language processing rely on manual supervision. |
| Approach: | They propose an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context. |
| Outcome: | The proposed approach outperforms or is comparable to knowledge-augmented zero-shot methods on a diverse set of abductive reasoning datasets. |
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search (2024.emnlp-main)
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Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
| Challenge: | Logic-Induced-Knowledge-Search (LINK) is a framework for generating factually-correct yet long-tail inferential knowledge. |
| Approach: | They introduce a framework to obtain factually-correct yet long-tail inferential statements using variable-wise prompting grounded on symbolic rules. |
| Outcome: | The proposed framework is able to obtain factually-correct yet long-tail inferential statements while ensuring factual correctness. |
Symbolic Planning and Code Generation for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Large language models excel at processing and generating text and code, but lack a grounded task-oriented dialogue system that can handle grounding. |
| Approach: | They propose a modular and interpretable grounded dialogue system that integrates a reader and planner to convert partner utterances into executable code and a symbolic planner to determine the next appropriate response. |
| Outcome: | The proposed system outperforms the existing state-of-the-art on a one-common dialogue task and improves task success in human evaluations from 56% to 69% in the most challenging setting. |