Papers by Hua Shen
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)
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Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
| Challenge: | Large language models (LLMs) can handle extensive context and multi-turn reasoning. |
| Approach: | They propose a taxonomy dividing psychotherapy into stages of assessment, diagnosis, and treatment to examine LLM advancements and challenges. |
| Outcome: | The proposed taxonomy reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. |
REALM: A Dataset of Real-World LLM Use Cases (2025.findings-acl)
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| Challenge: | Existing studies on LLM adoption and their social implications lack empirical grounding, weakening their validity. |
| Approach: | They propose to integrate a dataset of over 94,000 LLM use cases collected from Reddit and news articles to provide insights into LLM adoption across different domains. |
| Outcome: | The proposed dataset includes over 94,000 LLM use cases collected from Reddit and news articles. |
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup (2023.emnlp-main)
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| Challenge: | Disfluency detection models focus on individual utterances, but discontinuities in spoken transcripts occur across multiple turns. |
| Approach: | They propose a multi-turn "cleanup task" to detect discontinuities in spoken conversations . they leverage two modeling approaches for experimental evaluation as benchmarks . |
| Outcome: | The proposed task detects "discontinuities" in spoken conversations that can be removed . the results are compared with existing methods and are expected to be validated in the future . |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
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Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
A Multi-Agent Framework for High-Interaction Terminal Simulation (2026.acl-long)
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| Challenge: | Terminal simulation is a problem of symbolic language generation in dialogue and interactive systems. |
| Approach: | They propose a terminal command-level Turing test framework that improves realism, consistency and robustness in command-language generation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks by more than 9% on multi-turn terminal simulation. |
Adaptive Rank Selections for Low-Rank Approximation of Language Models (2024.naacl-long)
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| Challenge: | Singular Value Decomposition (SVD) or its weighted variants has progressed in compressing language models. |
| Approach: | They propose a binary masking mechanism for optimizing the number of ranks in a differentiable framework. |
| Outcome: | The proposed algorithm achieves much better accuracy than previous SVD and its weighted variants. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
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| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
| Outcome: | The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. |
Agentic Episodic Control (2026.findings-acl)
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| Challenge: | Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization. |
| Approach: | They propose a novel architecture that integrates large language models into episodic RL. |
| Outcome: | The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success. |
VALUE ALIGNMENT TAX: Measuring Value Trade-offs in LLM Alignment (2026.findings-acl)
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| Challenge: | Existing work on value alignment characterizes value relations statically, ignoring how interventions reshape the value system. |
| Approach: | They propose a framework that quantifies value trade-offs by measuring how alignment-induced changes propagate across interconnected values relative to achieved on-target gain. |
| Outcome: | The proposed framework measures how value trade-offs propagate across values . it can be used to evaluate intended improvements and unintended side effects . |
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)
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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen
| Challenge: | Psychological defenses are strategies people use to manage distress. |
| Approach: | They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations. |
| Outcome: | The proposed framework reduces annotation time by 24.0% in a counterbalanced study. |
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)
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| Challenge: | Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored. |
| Approach: | They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning. |
| Outcome: | The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. |
Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)
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| Challenge: | Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices. |
| Approach: | They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop. |
| Outcome: | The proposed method can perform better than current SOTA methods in compressing Transformer-based language models. |
Are Shortest Rationales the Best Explanations for Human Understanding? (2022.acl-short)
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| Challenge: | Existing models favor extracting the shortest possible rationales to explain model predictions . however, this assumption has yet to be validated . |
| Approach: | They propose a model that extracts rationales at any target length from text inputs . they show that rationale lengths too short do not help humans predict labels better . |
| Outcome: | The proposed model achieves compatible end-task performance and human-annotated rationale agreement compared to baseline models . |
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing continual learning approaches suffer from low accuracy and performance fluctuation when the distributions of old and new data are significantly different. |
| Approach: | They propose a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
| Outcome: | The proposed model outperforms the best state-of-the-art method by 20% in average accuracy and each component contributes effectively to overall performance. |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)
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Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql. |
| Approach: | They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions. |
| Outcome: | The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation. |
Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)
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| Challenge: | RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models. |
| Approach: | They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels . |
| Outcome: | The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates . |
Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values? (2025.emnlp-main)
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| Challenge: | Existing research assesses LLMs’ values by analyzing their stated inclinations . a framework to evaluate the alignment between stated values and value-informed actions is lacking . |
| Approach: | They propose a framework to evaluate the alignment between LLMs’ stated values and their value-informed actions. |
| Outcome: | The proposed framework shows significant misalignment between LLM-generated values and their actions . misaligned values have shown real-world risks, such as amplifying stereotypes and reinforcing bias algorithms in hiring. |
IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding (2023.findings-emnlp)
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| Challenge: | Instance attribution (IA) aims to identify the training instances leading to the prediction of a test example. |
| Approach: | They propose a systematic and comprehensive evaluation scheme covering four significant requirements: sufficiency, completeness, stability and plausibility. |
| Outcome: | The proposed evaluation scheme covers four significant requirements: sufficiency, completeness, stability and plausibility. |
Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)
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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens
| Challenge: | a key intent behind many emails is to get a reply from the recipient. |
| Approach: | They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations. |
| Outcome: | The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates . |