Papers by Houqiang Li
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)
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| Challenge: | a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations. |
| Approach: | They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space . |
| Outcome: | Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning. |
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
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| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
Hybrid and Collaborative Passage Reranking (2023.findings-acl)
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| Challenge: | Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages. |
| Approach: | They propose a Hybrid and Collaborative Passage Reranking method that leverages the similarity measurements of upstream retrievers for passage collaboration. |
| Outcome: | Experiments show that HybRank improves over existing methods and improves performance. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language? (2024.findings-emnlp)
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| Challenge: | Dense retrieval systems focus on optimizing text embedding space while overlooking Boolean logic in language. |
| Approach: | They propose a task to investigate whether retrieval systems can comprehend Boolean logic in language. |
| Outcome: | The proposed method is based on a benchmark dataset covering complex queries containing basic Boolean logic and corresponding annotated passages. |
Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance. |
| Approach: | They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
Controllable Style Arithmetic with Language Models (2025.acl-long)
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| Challenge: | Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency. |
| Approach: | They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity. |
| Outcome: | The proposed approach achieves three key capabilities while achieving optimal computational efficiency. |
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)
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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li
| Challenge: | Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases. |
| Approach: | They propose a feedback-driven iterative framework that leverages Large Language Models to generate initial test cases, execute them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability. |
| Outcome: | The proposed method outperforms the existing codecontests and codecontests+ models by 4.30% and 8.78%. |
Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding (2021.findings-emnlp)
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| Challenge: | Existing weakly supervised methods for temporal language grounding lose the complexity of the video and the semantics of the sentence. |
| Approach: | They propose a candidate-free framework for weakly supervised Temporal Language Grounding . they use a token-by-clip cross-modal semantic alignment module to learn alignment . |
| Outcome: | The proposed framework achieves state-of-the-art on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo. |
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)
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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li
| Challenge: | Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student. |
| Approach: | They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures. |
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)
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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan
| Challenge: | Existing models for pre-training are not convenient for users to find and set them up. |
| Approach: | They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model . |
| Outcome: | The proposed models achieve new state-of-the-art on 10 benchmarks. |
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026.acl-long)
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| Challenge: | Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation. |
| Approach: | They propose a framework that learns and corrects underlying bias patterns by fitting a length-reward relationship into a reward model. |
| Outcome: | The proposed framework improves length-controlled win rate and reduces verbosity without compromising performance. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
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| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)
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| Challenge: | Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input. |
| Approach: | They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer . |
| Outcome: | The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input . |
Semi-Supervised Spoken Language Glossification (2024.acl-long)
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| Challenge: | Spoken language glossification (SLG) aims to translate spoken language text into sign language gloss, i.e., written record of sign language. |
| Approach: | They propose a framework to translate spoken language into a sign language gloss . they use monolingual spoken language text to integrate it into training . |
| Outcome: | The proposed framework incorporates large-scale monolingual spoken language text into SLG training. |
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)
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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan
| Challenge: | Existing work generates long videos segment by segment sequentially, which is inefficient. |
| Approach: | They propose a Diffusion over Difference architecture for eXtremely Long video generation. |
| Outcome: | The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence. |
Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)
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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li
| Challenge: | Large Reasoning Models (LRMs) are constrained by the overthinking issue. |
| Approach: | They propose a policy optimization framework that reshapes the exploration and exploitation through two core components: self-imitation and self-guidance exploration. |
| Outcome: | The proposed model achieves superior reasoning efficiency without compromising overall accuracy. |
Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)
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| Challenge: | Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages. |
| Approach: | They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model. |
| Outcome: | The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages . |