Papers by Guilin Qi
Towards relation extraction from speech (2022.emnlp-main)
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| Challenge: | Existing methods for extracting relations from speech have been neglected due to the nature of speech. |
| Approach: | They propose a listening information extraction task that uses speech to extract relation extraction from speech . they use a text-to-speech system and crowd-sourced native English speakers to test the task . |
| Outcome: | The proposed task extracts semantic relationships from speech data using a new model . the proposed task is more challenging than the existing method due to the characteristics of speech . |
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM (2025.coling-main)
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| Challenge: | Existing methods for generating follow-up questions are limited to shallow contextual questions that are uninspiring and have a large gap to the human level. |
| Approach: | They propose a three-stage external knowledge-enhanced follow-up question generation method which generates questions by identifying contextual topics, building a knowledge graph online, and finally combining these with a large language model to generate the final question. |
| Outcome: | The proposed method generates questions by identifying contextual topics, building a knowledge graph (KG) online, and finally combining these with a large language model to generate the final question. |
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing (2024.findings-acl)
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Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
| Challenge: | Current benchmarks focus on coarse-grained knowledge, leaving the intricacies of fine-grounded knowledge unexplored. |
| Approach: | They propose a benchmark and dataset specifically designed for FG multimodal entity knowledge editing. |
| Outcome: | The proposed benchmark underscoring the complexity of FG knowledge editing in MLLMs. |
SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning (2026.acl-long)
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| Challenge: | Existing approaches focus on general utility metrics, overlooking the preservation of semantically related concepts. |
| Approach: | They propose a method that introduces self-generated proximal visual tokens to prevent forgetting vulnerability. |
| Outcome: | The proposed framework outperforms existing methods in preserving semantically related concepts while achieving effective target unlearning. |
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases (2020.coling-main)
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| Challenge: | Existing methods for question generation over knowledge bases have low diversity and poor fluency due to the limited information contained in the subgraphs and semantic drift due to decoder’s oblivion of the semantics of the answer entity. |
| Approach: | They propose a knowledge-enriched, type-constrained and grammar-guided KBQG model that generates natural-language questions over a set of triples in the KB. |
| Outcome: | The proposed model outperforms existing methods on two widely-used benchmark datasets. |
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (2021.findings-acl)
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| Challenge: | Event detection typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem. |
| Approach: | They propose a knowledge-based fewshot event detection method which introduces external event knowledge as the knowledge prior of event types. |
| Outcome: | Experiments show that the proposed method outperforms baselines by 15 F 1 points . event detection is an important task in information extraction . |
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown remarkable proficiency in understanding textual data and revolutionizing the field of natural language processing. |
| Approach: | They empirically analyze LLMs' capability of understanding Description Logic (DL) ontologies covering 6 representative tasks from syntactic and semantic aspects. |
| Outcome: | The proposed model can understand formal syntax and model-theoretic semantics of concepts and roles, but struggle with understanding TBox NI transitivity and handling ontologies with large ABoxes. |
Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)
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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi
| Challenge: | Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs). |
| Approach: | They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD). |
| Outcome: | The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation. |
TabPrompt: Graph-based Pre-training and Prompting for Few-shot Table Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods of Table Understanding (TU) focus on the textual content within the tabular data, disregarding the topological information of the table. |
| Approach: | They propose a framework that uses tabs to understand tabular data without ignoring the topological information of the table. |
| Outcome: | The proposed framework outperforms baselines in few-shot table understanding tasks. |
TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (2025.coling-main)
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| Challenge: | Existing research still faces spurious query-anchor matching due to unobserved factors. |
| Approach: | They propose a model that uses the front-door criteria to decompose the expansion process into a parser module and a connector to isolate confounding effects. |
| Outcome: | Extensive experiments on three benchmarks validate the effectiveness of the proposed model. |
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping (2024.naacl-long)
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| Challenge: | Existing methods to collect high-quality instruction-response pairs suffer from unaffordable labor costs or severe hallucinations in the self-generation of LLMs. |
| Approach: | They propose a method that trains LLMs to generate instruction-response pairs based on human-written documents rather than relying solely on self-generation without context. |
| Outcome: | The proposed method outperforms existing typical methods on multiple benchmarks and shows that it is 100% scalable. |
Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning (2025.acl-long)
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| Challenge: | Current AM methods focus on extracting attributes from unimodal text, underutilizing multimodal data. |
| Approach: | They propose a framework for multimodal self-correction instruction tuning to extract new attributes from images and text with Multimodal Large Language Models. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets. |
Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)
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| Challenge: | Existing work on complex questions does not consider controlling complexity of generated questions. |
| Approach: | They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates. |
| Outcome: | The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets. |
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers (2025.naacl-long)
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| Challenge: | Existing information retrieval models assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings. |
| Approach: | They propose a unified instruction-aware heterogeneous knowledge retriever that builds a heterogenous retrieval space for heterogenized knowledge and follows diverse user instructions to retrieve knowledge in specified types. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on CompMix-IR . it achieves 6.36% relative improvements and 54.23% relative improvements . |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| Challenge: | Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing . |
| Approach: | They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce. |
| Outcome: | The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks. |
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing knowledge rewriting methods may include irrelevant information, omit crucial details, or fail to align with the question’s semantics. |
| Approach: | They propose a new rewriting method CoTKR for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewrite. |
| Outcome: | The proposed method mitigates the limitations of single-step knowledge rewriting and bridges the preference gap between the knowledge reactor and the question answering (QA) model. |
Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths (2025.acl-long)
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| Challenge: | Large language models (LLMs) encode vast amounts of knowledge in their parameters, but the acquired knowledge can be incorrect or outdated over time, necessitating rectification after pre-training. |
| Approach: | They propose a method that captures key information flows that influence model predictions . they propose 'critical transmission paths' to improve model editing . |
| Outcome: | The proposed method improves on two prominent datasets and three widely used LLMs. |
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level. |
| Approach: | They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. |
| Outcome: | The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set. |
Event Causality Identification via Derivative Prompt Joint Learning (2022.coling-1)
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| Challenge: | Existing methods for event causality identification lack annotated data, and they lack the ability to identify explicit and implicit causality. |
| Approach: | They propose a derivative prompt joint learning model which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. |
| Outcome: | The proposed model can identify explicit and implicit causality on two benchmark datasets and it has great advantages over previous methods. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
| Approach: | They propose a text-video based multimodal event extraction framework that integrates video appearance features and motion representations with video appearance. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in the event extraction field. |
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism (2020.coling-main)
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| Challenge: | Existing methods for event extraction cannot express connections between arguments, which are crucial in legal events. |
| Approach: | They propose a dynamic event structure for Chinese legal events to distinguish between similar events by hierarchical event features for event detection and a pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. |
| Outcome: | The proposed model surpasses state-of-the-art models on a Chinese legal event dataset. |
PRIMO: Progressive Induction for Multi-hop Open Rule Generation (2024.lrec-main)
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| Challenge: | Existing approaches focus on single-hop open rule generation, ignoring scenarios involving multiple hops, leading to logical inconsistencies between premise and hypothesis atoms, as well as semantic duplication of generated rule atom. |
| Approach: | They propose a multi-stage open rule generation method called PRIMO that introduces ontology information during the rule generation stage to reduce ambiguity and improve rule accuracy. |
| Outcome: | The proposed method reduces the repetition rate of rule atoms while preserving the latent knowledge within the model. |
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs (2025.acl-long)
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Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Hongru Wang, Sheng Bi, Yongrui Chen, Tongtong Wu, Jeff Z. Pan
| Challenge: | Attributed Question Answering (AQA) has attracted wide attention, but there are several limitations in evaluating the attributions. |
| Approach: | They propose a large-scale benchmark containing comprehensive attribution categories . they compare 25 automatic evaluators with human evaluers and tested LLM evalators . |
| Outcome: | The proposed method can compare attributions with subtle differences and provide feedback to improve them. |