Papers by Baohang Zhou

DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models (2024.findings-acl)

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Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong

Challenge:	Existing methods to detect pretraining data from large language models are unrealistic to them.
Approach:	They propose to detect pre-training data from LLM in a black-box way by using GPT-2 as reference model and feed it with sequence probabilities to detect whether it was used to train it.
Outcome:	The proposed framework outperforms existing methods on the benchmark datasets and shows that it is effective on different popular LLMs.

MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)

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Baohang Zhou, Ying Zhang, Kehui Song, Hongru Wang, Yu Zhao, Xuhui Sui, Xiaojie Yuan

Multimodality

Challenge:	Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data.
Approach:	They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors.
Outcome:	The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it.

PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)

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Ying Zhang, Baohang Zhou, Kehui Song, Xuhui Sui, Guoqing Zhao, Ning Jiang, Xiaojie Yuan

Multimodality

Challenge:	Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series.
Approach:	They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks.
Outcome:	The proposed method is superior to existing models on MIMIC-III benchmark.

Selecting Key Views for Zero-Shot Entity Linking (2023.findings-emnlp)

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Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Xiaojie Yuan, Wensheng Zhang

Semantic Text Processing

Challenge:	Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases.
Approach:	They propose a framework to align mentions in text to entities in knowledge bases . they use unsupervised clustering to select key views from descriptions .
Outcome:	The proposed framework achieves state-of-the-art on the zero-shot entity linking dataset.

Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting (2024.lrec-main)

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Ying Zhang, Xinying Qian, Yu Zhao, Baohang Zhou, Kehui Song, Xiaojie Yuan

Semantic Text Processing

Challenge:	Existing methods for temporal knowledge graph forecasting are insufficient structural contexts to learn effective representations.
Approach:	They propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting that brings time-invariant entity background information to time-variant structural information.
Outcome:	The proposed framework is effective and stays competitive in inference with limited structural information.

UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)

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Hongru Wang, Boyang Xue, Baohang Zhou, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Kam-Fai Wong

Low-Resource NLP

Challenge:	Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency.
Approach:	They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation.
Outcome:	The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously.

TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)

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Xinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou, Xuhui Sui, Li Zhang, Kehui Song

Semantic Text Processing

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs).
Approach:	They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints.
Outcome:	The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs.

Beyond Timestamps: Bridging Forward and Backward Reasoning in Temporal Numerical and Relational Understanding (2026.acl-long)

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Xinying Qian, Ying Zhang, Xuhui Sui, Yu Zhao, Baohang Zhou, Jeff Z. Pan

Reasoning

Challenge:	Existing benchmarks for Temporal Numerical and Relational reasoning rely on single-task evaluation paradigms.
Approach:	They propose a benchmark to evaluate Temporal Numerical and Relational reasoning . they propose QA and verification, and a Consistency Rate to quantify robustness .
Outcome:	The proposed framework evaluates both Temporal Numerical and Relational reasoning . it measures the alignment between QA and FV and the Consistency Rate measures robustness across these directions.

Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions (2025.naacl-long)

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Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-Fai Wong

Semantic Text Processing

Challenge:	Existing studies focus on leveraging internal knowledge of Large Language Models (LLMs) to answer known questions.
Approach:	They propose a framework that allows LLMs to choose between internal and external knowledge . they use a dataset to analyze compositional questions that are composed of unknown sub-questions .
Outcome:	The proposed framework can achieve comparable or even better performance with much fewer external calls compared with several strong baselines.

MELOV: Multimodal Entity Linking with Optimized Visual Features in Latent Space (2024.findings-acl)

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Xuhui Sui, Ying Zhang, Yu Zhao, Kehui Song, Baohang Zhou, Xiaojie Yuan

Multimodality

Challenge:	Existing approaches to multimodal entity linking focus on textual contexts but lack in social media vision modality.
Approach:	They propose a latent space vision feature optimization framework MELOV to address these challenges . they exploit variational autoencoder to mine shared information and generate text-based visual features .
Outcome:	The proposed framework is superior to existing methods on three benchmark datasets.

BioFEG: Generate Latent Features for Biomedical Entity Linking (2023.emnlp-main)

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Xuhui Sui, Ying Zhang, Xiangrui Cai, Kehui Song, Baohang Zhou, Xiaojie Yuan, Wensheng Zhang

Knowledge Representation

Challenge:	Existing approaches to biomedical entity linking suffer from multiple types of errors due to the rarity of many biomedically relevant entities in real-world scenarios.
Approach:	They propose a latent feature generation framework to generate latent semantic features for unseen entities to capture fine-grained coherence information of unseened entities.
Outcome:	The proposed framework is superior to existing models on two benchmark datasets.

An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)

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Baohang Zhou, Xiangrui Cai, Ying Zhang, Xiaojie Yuan

Semantic Text Processing

Challenge:	Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks.
Approach:	They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way.
Outcome:	The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance.

Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)

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Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan

Semantic Text Processing

Challenge:	Entity linking is a task of assigning entity mentions to referent entities in a knowledge base.
Approach:	They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information.
Outcome:	The proposed model achieves state-of-the-art in the zero-shot entity linking task .

A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)

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Baohang Zhou, Ying Zhang, Kehui Song, Wenya Guo, Guoqing Zhao, Hongbin Wang, Xiaojie Yuan

Multimodality

Challenge:	Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image.
Approach:	They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs.
Outcome:	The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods.