Papers by Eric Yang
EVM-QuestBench: An Execution-Grounded Benchmark for Natural-Language Transaction Code Generation (2026.acl-long)
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| Challenge: | Existing evaluations of large language models overlook execution accuracy and safety. |
| Approach: | They propose an execution-grounded benchmark for natural-language transaction-script generation on EVM-compatible chains. |
| Outcome: | The proposed benchmark finds large performance gaps in the models with 5 independent rounds. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study (2025.findings-acl)
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| Challenge: | General-purpose models lack depth for expert-level tasks because of limited domain-specific information. |
| Approach: | They propose a method for curating domain-specific datasets from noisy web sources to improve model performance. |
| Outcome: | The proposed model outperforms the baseline model on the astronomy benchmark and on the AstroBench. |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
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| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |
Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment (2023.acl-srw)
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| Challenge: | The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used worldwide to classify and code diseases, injuries, and other health conditions. |
| Approach: | They evaluate the usefulness of correlation bias and suggest it could improve ICD-9 code assignment in some cases. |
| Outcome: | The proposed model improves on classes that are more imbalanced and less correlated with other codes, but the effect on individual class can be negative or positive. |
Automated Crossword Solving (2022.acl-long)
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| Challenge: | Using neural question answering models, our system generates answer candidates and then combines loopy belief propagation with local search to find full puzzle solutions. |
| Approach: | They propose a new approach to automatically solving crossword puzzles that uses neural question answering models and loopy belief propagation with local search to find full puzzle solutions. |
| Outcome: | The proposed system outperforms even the best human solvers and can solve crosswords from a wide range of domains with perfect accuracy. |
MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification (2026.findings-acl)
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Jingwei Song, Xinyu Wang, Hanbin Wang, Xiaoxuan Lei, Tianyu Shi, Shixin Han, Eric Yang, Xiao-Wen Chang, Lynn Ai
| Challenge: | Autoregressive large language models suffer from high inference latency due to memorybandwidth constraints. |
| Approach: | They propose a method that decouples generation and verification by decoupling tokens and a lightweight draft model. |
| Outcome: | The proposed method delivers consistent and significant speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks. |
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations (2021.emnlp-main)
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| Challenge: | Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. |
| Approach: | They propose a method that factors out language identity information from semantic related components in multilingual representations pre-trained on monolingual data. |
| Outcome: | The proposed method improves cross-lingual transfer performance on weak alignment models. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Data-to-Text Generation with Style Imitation (2020.findings-emnlp)
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| Challenge: | Recent approaches to data-to-text generation focus on improving content fidelity, but lack explicit control over writing styles. |
| Approach: | They propose a way to control writing styles by using existing sentences as "soft" templates . they conduct experiments in restaurants and sports domains to test their approach . |
| Outcome: | The proposed approach achieves stronger performance than a range of comparison methods. |
Entity Resolution in Open-domain Conversations (2021.naacl-industry)
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Mingyue Shang, Tong Wang, Mihail Eric, Jiangning Chen, Jiyang Wang, Matthew Welch, Tiantong Deng, Akshay Grewal, Han Wang, Yue Liu, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Recent work on incorporating external knowledge into the response generation models has attracted great interest. |
| Approach: | They propose a neural entity linking approach to incorporate external knowledge into the response generation models to improve the relevancy of retrieved knowledge. |
| Outcome: | The proposed approach outperforms the baseline model by 62.8% relative to the baseline. |
Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency (2023.emnlp-main)
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| Challenge: | Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses. |
| Approach: | They propose to fine-tune large language models to simulate how previous students would have responded to unseen items to generate high-quality parallel tests. |
| Outcome: | The proposed test forms are designed to be content-equivalent and produce identical individual scores as the original test form. |
Embedding Imputation with Grounded Language Information (P19-1)
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| Challenge: | Existing approaches to embedding imputation use vector space properties or subword information to learn representations for rare or unseen words. |
| Approach: | They propose an online method to construct a knowledge graph from grounded information and an algorithm to map from the resulting graph to the space of the pre-trained embeddings. |
| Outcome: | The proposed method improves on a card-660 task by 11% and 17.8% respectively using GloVe embeddings. |
Progressive Generation of Long Text with Pretrained Language Models (2021.naacl-main)
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| Challenge: | Existing methods for "long" text generation are limited to outputs of 50-200 tokens . however, our proposed ProGen generates coherent long passages of text in a progressive manner . |
| Approach: | They propose a method for generating coherent long passages of text in a progressive manner . they first produce domain-specific content keywords and then refine them into complete passages . human evaluation validates that their proposed generation is more coherent . |
| Outcome: | The proposed method produces domain-specific content keywords and refines them into complete passages in multiple stages. |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising (2020.findings-emnlp)
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| Challenge: | Existing abstractive summarization models ignore abundant unlabeled corpora resources . TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets . |
| Approach: | They propose a transformer-based unsupervised text summarization system with pretraining on large-scale data. |
| Outcome: | The proposed system outperforms baseline models on NYT, CNN/DM and English Gigaword datasets with various document styles. |
Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions (2025.findings-acl)
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Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides PhD, Logan Douglas Schneider, Isaac Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja Mataric
| Challenge: | Structured data is generated grounded in health and lifestyle factors and full profiles of synthetic users are developed conditioned on the structured data. |
| Approach: | They propose an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. |
| Outcome: | The proposed framework is validated in the domains of sleep and diabetes coaching using two independently-developed agents for sleep and diabetic coaching as case studies. |