Papers by Dan Huang

Incorporating Worker Perspectives into MTurk Annotation Practices for NLP (2023.emnlp-main)

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Challenge:	Current approaches to data collection for natural language processing on Amazon Mechanical Turk (MTurk) are susceptible to issues regarding workers’ rights and poor response quality without considering the perspectives of MTurq workers.
Approach:	They conducted a critical literature review and a survey of MTurk workers to address open questions regarding fair payment, worker privacy, data quality, and considering worker incentives.
Outcome:	The findings suggest that future studies may better account for MTurk workers’ experiences in order to respect workers' rights and improve response quality.

A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang

Language Models

Challenge:	Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach:	They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome:	The proposed model can be used to understand and generate human natural languages.

Affect inTweets: A Transfer Learning Approach (2020.lrec-1)

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Linrui Zhang, Hsin-Lun Huang, Yang Yu, Dan Moldovan

Language Models

Challenge:	Existing machine learning models require considerable effort to design task specific features to understand affectual states of people.
Approach:	They propose a transfer-learning based approach to infer the affectual state of a person from tweets.
Outcome:	The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets.

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)

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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth

Question Answering

Challenge:	Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive.
Approach:	They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C.
Outcome:	The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness.

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)

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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

Responsible & Trustworthy NLP

Challenge:	Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains.
Approach:	They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control .
Outcome:	The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control.

FLARE: Fine-Grained Length-Aware Routing for Resource-Efficient Heterogeneous LLM Serving (2026.acl-long)

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Yujia Fu, Heming Zhong, Dan Huang, Yutong Lu

Green & Sustainable NLP

Challenge:	Existing routers lack fine-grained resource awareness across deployment settings, which degrades efficiency metrics in real-world serving.
Approach:	They propose a length-centric, resource-aware multi-LLM routing framework that uses length-based models to estimate per-query latency and cost.
Outcome:	Experiments show that FLARE reduces latency and cost by up to 68% and 75% while maintaining competitive accuracy.

GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)

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Zhengxin Zhang, Chengyu Huang, Xufu Liu, Dan Zhao, Jinyan Su, Claire Cardie

Robustness in NLP

Challenge:	Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust .
Approach:	They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions.
Outcome:	The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning.

ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition (2024.lrec-main)

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Ruizhe Huang, Mahsa Yarmohammadi, Jan Trmal, Jing Liu, Desh Raj, Leibny Paola Garcia, Alexei V. Ivanov, Patrick Ehlen, Mingzhi Yu, Dan Povey, Sanjeev Khudanpur

Multimodality

Challenge:	Existing work on contextual speech recognition (ASR) systems focuses on recognizing words that are not frequently seen in training data, such as rare words, but word error rate on rare words remains over 20%.
Approach:	They propose to use public-domain earnings calls and supplementary materials to evaluate contextual ASR approaches grounded on real-world applications.
Outcome:	The proposed frameworks are noisier than artificially synthesized contexts that contain the ground truth, yet still make great room for future improvement of contextual ASR technology.

DeAL: Decoding-time Alignment for Large Language Models (2025.acl-long)

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James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-An Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth

Language Models

Challenge:	Large Language Models (LLMs) are expected to generate content aligned with human preferences.
Approach:	They propose a framework that allows the user to customize reward functions and enables Decoding-time Alignment of LLMs (DeAL).
Outcome:	The proposed framework allows the user to customize reward functions and enables Decoding-time Alignment of LLMs.

New Frontiers of Information Extraction (2022.naacl-tutorials)

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

Information Extraction & Text Mining

Challenge:	Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data.
Approach:	This tutorial will provide an introduction to recent advances in IE by answering several important research questions.
Outcome:	The tutorial will address several important research questions and outline directions for further investigation.

Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)

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Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Ge Lan, Yue Wang

Language Models

Challenge:	Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability.
Approach:	They propose a framework that provides targeted mechanisms for exploration and stabilization.
Outcome:	The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably.

A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)

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Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, Shujian Huang

Responsible & Trustworthy NLP

Challenge:	Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency.
Approach:	They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts.
Outcome:	The proposed framework improves the attack success rate while reducing the time cost compared to baselines.

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Multimodality

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation (2026.acl-long)

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Zhihao Gong, Zeyu Sun, Dong Huang, Qingyuan Liang, Jie M. Zhang, Dan Hao

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) have improved the functional correctness of code translation, but execution efficiency remains overlooked.
Approach:	They propose a benchmark to explicitly assess execution efficiency in LLM-translated code.
Outcome:	The proposed benchmark identifies that execution efficiency is an essential dimension of code translation . the results highlight that correctness and efficiency are often misaligned .