Papers by Xiaotong Feng
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
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| 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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| 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. |
A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection (2024.findings-emnlp)
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| 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. |
Evidence-guided Inference for Neutralized Zero-shot Transfer (2024.lrec-main)
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| Challenge: | Existing knowledge transfer frameworks that use label skewness to neutralize biased language are costly and impractical when it comes to scarcely labeled data. |
| Approach: | They propose a neutralized Knowledge Transfer framework to equip pre-trained language models with neutralized transferability. |
| Outcome: | The proposed framework shows that it can be used to train pre-trained models with neutralized transferability . it is compared with baselines with a zero-shot cross-domain transfer setting . |
Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)
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| 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. |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
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
| 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. |
Exploring Logographic Image for Chinese Aspect-based Sentiment Classification (2022.findings-emnlp)
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| Challenge: | Existing methods for aspect-based sentiment classification have focused on English text, but Chinese is a language derived from pictographs and different from other phonetic languages. |
| Approach: | They propose to use a logographic image to capture internal morphological structure from character sequence . they propose to explicitly incorporate a symbolic image with review text for sentiment classification . |
| Outcome: | The proposed method improves over baselines and improves on existing methods. |