Papers by Zehan Li
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
Pub-LawBench: Public-Oriented Benchmarking for LegalAI (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on legal professionals, not legal professionals. |
| Approach: | They propose a public-oriented LegalAI benchmark grounded in legal functionalism and genre analysis to address this gap. |
| Outcome: | The proposed model evaluates 17 large language models on Pub-LawBench using simple prompts and Chain-of-Thought under a vanilla inference setting. |
Zero-shot Jianzi Recognition as Structured Visual Information Extraction in Open Compositional Symbolic Systems (2026.acl-long)
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| Challenge: | Optical character recognition (OCR) is a relatively new form of tablature recognition, but its accuracy is limited due to its unbounded composition and manuscript-level variability. |
| Approach: | They propose a method that predicts component sequences under a zero-shot split and synthesize manuscript-like training images via component-wise style recomposition and manuscript-domain noise modeling. |
| Outcome: | The proposed method achieves 63.02% sequence accuracy on real-world Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%. |
Temporal Leakage in Search-Engine Date-Filtered Web Retrieval: A Retrospective Forecasting Case Study (2026.acl-short)
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| Challenge: | Search-engine date filters are widely used to enforce pre-cutoff retrieval in retrospective evaluations of search-augmented forecasters. |
| Approach: | They propose stronger retrieval safeguards or evaluation on frozen, time-stamped web snapshots to prevent post-cutoff leakage. |
| Outcome: | The proposed approach is unreliable across two major search engines, and the results are inflated. |
AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences. |
| Approach: | They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods. |
| Outcome: | The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction. |
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)
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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao
| Challenge: | Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors. |
| Approach: | They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages. |
| Outcome: | The proposed model can translate audio-visual speech into audio-visual speech in other languages. |
Text Representation Distillation via Information Bottleneck Principle (2023.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have recently shown great success in text representation field, however, the high computational cost and high-dimensional representation of PLMs pose significant challenges for practical applications. |
| Approach: | They propose a Knowledge Distillation method that distills large models into smaller representation models to reduce performance degradation after distillation. |
| Outcome: | Empirical results on two main downstream applications of the proposed method show that it reduces the risk of over-fitting and maximizes the mutual information between the model and the input data. |
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search (2024.lrec-main)
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| Challenge: | Existing approaches to code question answering use bi-modal and unimodal pretraining to align text and code representations. |
| Approach: | They propose a modality-agnostic contrastive pre-training approach to improve alignment of text and code representations of current code language models. |
| Outcome: | The proposed model exhibits significant performance improvements across a wide range of code retrieval benchmarks. |
From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (2026.findings-acl)
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| Challenge: | Existing methods for zero-shot relationship extraction do not distinguish between unseen, semantically similar relations. |
| Approach: | They propose a framework to enable global reasoning across a set of predictions. |
| Outcome: | The proposed framework outperforms existing methods and establishes new state-of-the-art results on widely used datasets. |
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding (2023.emnlp-main)
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| Challenge: | 3D visual grounding aims to localize the desired objects in a 3D point cloud by a free-form language description. |
| Approach: | They propose a relation-aware framework which captures relative spatial relationships between objects and enhances object attributes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmarks . it captures relative spatial relationships between objects and enhances object attributes . |
Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)
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| Challenge: | Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance. |
| Approach: | They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text. |
| Outcome: | The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods. |
Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction (2025.coling-main)
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| Challenge: | Existing methods to extract triplets from context often decompose into named entity recognition and relation classification, which may introduce error propagation. |
| Approach: | They propose a Relation-centric joint ZSRTE method which leverages unseen relation labels to extract triplets in one go. |
| Outcome: | The proposed method achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. |