Papers by Tenghao Huang
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |
Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)
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| Challenge: | a recent study shows that language models are prone to self-contradiction during dialogues. |
| Approach: | They propose a red teaming framework that detects and attempts to explain dialogues, then modifies existing contradictory content using the explanation. |
| Outcome: | The proposed task improves the ability to detect contradictory dialogues and provides valid explanations. |
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources. |
| Approach: | They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model. |
| Outcome: | The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates. |
NewsInterview: a Dataset and a Playground to Evaluate LLMs’ Grounding Gap via Informational Interviews (2025.acl-long)
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Alexander Spangher, Michael Lu, Sriya Kalyan, Hyundong Justin Cho, Tenghao Huang, Weiyan Shi, Jonathan May
| Challenge: | Existing large datasets (1k-10k transcripts) are generated via crowdsourcing and are inherently unnatural. |
| Approach: | They curate a dataset of 40,000 two-person informational interviews from NPR and CNN . they find that LLMs are significantly less likely than human interviewers to use acknowledgements and pivot to higher-level questions. |
| Outcome: | The proposed model is based on 40,000 interviews with journalists and CNN . |
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)
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Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng
| Challenge: | a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories . |
| Approach: | They propose a computational framework to analyze narratives through three discourse-level aspects. |
| Outcome: | The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding . |
Creative Planning with Language Models: Practice, Evaluation and Applications (2025.naacl-tutorial)
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| Challenge: | This tutorial explores how planning has been learned and deployed in creative workflows . many human creative tasks involve extensive planning, and actions need to be taken . |
| Approach: | This tutorial explores how planning has been learned and deployed in creative workflows . authors discuss forward and backward learning approaches for planning in LLMs - and evaluation metrics tailored to latent plans . |
| Outcome: | This tutorial examines how planning has been learned and deployed in creative workflows . it discusses forward and backward learning approaches for planning in LLMs - evaluation metrics tailored to latent plans . |
GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)
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Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey, Yilun Zhou, Muhao Chen, Jonathan May, Chien-Sheng Wu
| Challenge: | Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process . |
| Approach: | They propose a framework that integrates crawling, retrieval-based seeding, in-context generation and automated quality control to produce realistic tasks paired with executable trajectories. |
| Outcome: | The proposed framework decouples crawling from generation for greater efficiency and ensures dense supervision through deterministic replays and systematic validation. |
Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)
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| Challenge: | Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools. |
| Approach: | They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks. |
| Outcome: | The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models. |
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
| Challenge: | Existing methods for extracting multi-document news summarization neglect relative importance of documents. |
| Approach: | They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model. |
| Outcome: | The proposed approach outperforms state-of-the-art methods with more complex architectures. |
Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)
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| Challenge: | AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. |
| Approach: | They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques. |
| Outcome: | The proposed method outperforms baseline models in generating affectively charged and interesting narratives. |
R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory (2025.acl-long)
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| Challenge: | Existing methods for web agents struggle with efficient navigation and action execution due to limited visibility and understanding of web structures. |
| Approach: | They propose a framework that integrates memory-enhanced navigation and reflective learning to improve web agents' performance. |
| Outcome: | The proposed framework shows significant improvements over existing methods, including 50% reduction in navigation errors and threefold increase in task completion rates. |
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
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| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |
Teaching Language Models To Gather Information Proactively (2025.findings-emnlp)
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| Challenge: | Large language models are often defaulted to passive responses or narrow clarifications when faced with incomplete or under-specified prompts. |
| Approach: | They propose a new task paradigm where LLMs must identify gaps in context and strategically elicit implicit user knowledge through targeted questions. |
| Outcome: | The proposed framework outperforms o3-mini on evaluation metrics and human annotators favor clarification questions and final outlines. |