Papers by Le Hu
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
| Approach: | They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation. |
| Outcome: | The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents. |
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)
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| Challenge: | In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation. |
| Approach: | They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements. |
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these models reflect human-like cognition versus advanced pattern recognition remains an open question. |
| Approach: | They conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner. |
| Outcome: | The proposed framework can be used to assess the cognitive and linguistic capabilities of large language models (LLMs). |
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)
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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu
| Challenge: | Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts. |
| Approach: | They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs. |
| Outcome: | The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks. |
MOSEL: Inference Serving Using Dynamic Modality Selection (2024.emnlp-main)
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| Challenge: | Recent advances in machine learning have enabled deep learning to exceed human capabilities in various tasks. |
| Approach: | They propose a new form of dynamism, modality selection, where modality picks modalities from inference inputs while maintaining the model quality. |
| Outcome: | The proposed system improves system throughput by 3.6 and job completion times by 11 compared to modality-agnostic approaches. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
Towards Robust Mathematical Reasoning (2025.emnlp-main)
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Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu Hoang Trinh, Quoc V Le, Junehyuk Jung
| Challenge: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the international mathematical Olympiad level. |
| Approach: | They propose IMO-Bench, a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
| Outcome: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |
Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have been driven by large-scale training corpora drawn from diverse sources such as websites, news articles, and books. |
| Approach: | They propose a framework for analyzing dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
| Outcome: | The proposed framework analyzes the dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense (2022.findings-acl)
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| Challenge: | ANTHRO extracts over 600K human-written text perturbations and leverages them for realistic adversarial attacks. |
| Approach: | They propose an adversarial text manipulation algorithm that inductively extracts over 600K human-written text perturbations and leverages them for realistic adversarials. |
| Outcome: | The proposed algorithm outperforms the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by layperson and professional human workers. |
Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs (2026.eacl-long)
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Karin de Langis, Jong Inn Park, Bin Hu, Khanh Chi Le, Andreas Schramm, Michael C. Mensink, Andrew Elfenbein, Dongyeop Kang
| Challenge: | Working memory is a critical component of human intelligence and executive functioning . it is correlated with performance on various cognitive tasks, including fluid intelligence . |
| Approach: | They apply working memory tasks to large language models to estimate working memory capacity . they find that LLMs exceed normative human scores, but not executive functioning benchmarks . |
| Outcome: | The proposed models do not show higher performance on executive functioning tasks or problem solving benchmarks. |
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)
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Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu
| Challenge: | Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions. |
| Approach: | They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary. |
| Outcome: | The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy. |
Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents (2021.naacl-main)
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| Challenge: | Existing summarization models suffer from the length limitation of text encoder, which results in huge loss of summary-relevant contents. |
| Approach: | They propose a sliding selector network with dynamic memory for extractive summarization of long-form documents that employs a window to extract summary sentences segment by segment. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets showing that it is highly efficient and fluent. |
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)
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Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella
| Challenge: | Existing techniques like distillation and pruning are not efficient for large language models. |
| Approach: | They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately. |
| Outcome: | The proposed framework improves system throughput while minimizing performance degradation. |
ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)
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Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise . |
| Approach: | They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following. |
| Outcome: | The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches. |
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)
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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu
| Challenge: | Recent advances in pre-trained language models have made it possible to generate human-like text. |
| Approach: | They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached. |
| Outcome: | The proposed game lacks incentives and relies on players to explore on their own. |