Papers by Xiaotong Li

SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025.findings-emnlp)

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Xiaotong Xu, Yizhao Wang, Yunfei Liu, Shengyang Li

Semantic Text Processing

Challenge:	Existing KG-based question answering frameworks face inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs.
Approach:	They propose a Skeleton-guided RAG framework for knowledge graph question answering . SKRAG leverages a lightweight language model enhanced with the Finite State Machine constraint .
Outcome:	The proposed framework outperforms baselines and general-domain benchmarks on a KGQA dataset in the space science and utilization domain.

SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)

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Han Liu, Shuqin Li, Xiaotong Zhang, Yuanyuan Wang, Feng Zhang, Hongyang Chen, Hong Yu

Information Retrieval

Challenge:	Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance.
Approach:	They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories.
Outcome:	The proposed method significantly outperforms state-of-the-art models.

AdaDHP: Fine-Grained Fine-Tuning via Dual Hadamard Product and Adaptive Parameter Selection (2025.acl-long)

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Han Liu, Changya Li, Xiaotong Zhang, Feng Zhang, Fenglong Ma, Wei Wang, Hong Yu

Semantic Text Processing

Challenge:	Increasing number of parameters can be challenging under resource-constrained environments.
Approach:	They propose a parameter-efficient fine-tuning method with fewer parameters and finer granularity that can adaptively select important parameters for each task.
Outcome:	The proposed method can fine-tune important parameters for each task, while maintaining the same weights.

Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020.acl-main)

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Guangfeng Yan, Lu Fan, Qimai Li, Han Liu, Xiaotong Zhang, Xiao-Ming Wu, Albert Y.S. Lam

Semantic Text Processing

Challenge:	Existing methods for unknown intent detection are limited by prior knowledge of class labels.
Approach:	They propose to use a Gaussian mixture model to model utterance embeddings with a distribution and inject dynamic class semantic information into Gausssian means.
Outcome:	The proposed model performs well on three real task-oriented dialogue datasets in two languages.

A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection (2024.findings-emnlp)

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Xiaotong Zhang, Xinyi Li, Feng Zhang, Zhiyi Wei, Junfeng Liu, Han Liu

Low-Resource NLP

Challenge:	Existing methods for few-shot intent detection are limited due to data scarcity and lack of information for unseen domains.
Approach:	They propose to enhance utterance representations with label synset augmentation and refine prototypes by distilling coarse domain knowledge from a universal teacher model.
Outcome:	The proposed approach outperforms existing methods in terms of accuracy and generalization across domains.

Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)

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Yuhao Wang, Keyan Ding, Kehua Feng, Zeyuan Wang, Ming Qin, Xiaotong Li, Qiang Zhang, Huajun Chen

Structured Data in NLP

Challenge:	Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc.
Approach:	They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences.
Outcome:	The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality.

Cross-Domain Sentiment Classification using Semantic Representation (2022.findings-emnlp)

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Shichen Li, Zhongqing Wang, Xiaotong Jiang, Guodong Zhou

Information Retrieval

Challenge:	Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains.
Approach:	They propose to use Abstract Meaning Representation to help with cross-domain sentiment classification by combining sentence-level AMRs with text-graph interaction models.
Outcome:	The proposed model is effective over strong baselines and shows its importance over strong models.

Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning (2025.acl-long)

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Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, Huajun Chen

Semantic Text Processing

Challenge:	Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis.
Approach:	They propose a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation that leverages Monte Carlo Tree Search for test-time scaling as a plugin to extend the LLMs’ coverage of the chemical structure space.
Outcome:	The proposed framework significantly improves on both GPT-4o-mini and GPT4o, and a specialized molecule-spectrum scorer improves performance.

Improving Clustering with Positive Pairs Generated from LLM-Driven Labels (2025.emnlp-main)

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Xiaotong Zhang, Ying Li

Text Clustering

Challenge:	Existing unsupervised clustering methods lack label knowledge, resulting in suboptimal performance.
Approach:	They propose to use LLM-driven labels to generate positive pairs from embedded data and an embedder to obviate the need for negative pairs.
Outcome:	The proposed framework surpasses state-of-the-art benchmarks on a range of datasets and generates interpretable labels for improved understanding of clustering results.

InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)

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Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen

Multimodality

Challenge:	Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins.
Approach:	They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment.
Outcome:	The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin.

Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought (2026.findings-acl)

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Han Liu, Shuotian Ma, Hui Li, Xiaotong Zhang, Fenglong Ma, Hong Yu

Language Models

Challenge:	Existing heuristics fail to capture global causal logic due to rigid rules and limited search spaces.
Approach:	They propose a framework that extracts the essential logical structure from reasoning chains.
Outcome:	Experiments show that Pru-CoT models generate more compact reasoning paths compared to models trained on verbose data.

Reconstructing Capsule Networks for Zero-shot Intent Classification (D19-1)

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Han Liu, Xiaotong Zhang, Lu Fan, Xuandi Fu, Qimai Li, Xiao-Ming Wu, Albert Y.S. Lam

Language Models

Challenge:	Existing methods for intent classification are limited due to fast-emerging intents . a recent study shows that existing methods are not effective in recognizing unseen intents.
Approach:	They propose to reconstruct capsule networks for zero-shot intent classification by using latent information from labeled utterances.
Outcome:	The proposed method outperforms existing methods on two task-oriented dialogue datasets in different languages.