Papers by Jianfeng Qu
Cooperative or Competitive? Understanding the Interaction between Attention Heads From A Game Theory Perspective (2025.acl-long)
Copied to clipboard
| Challenge: | a number of attention-based large language models (LLMs) focus on individual head contributions, but the precise interaction mechanisms between attention heads remain poorly understood. |
| Approach: | They propose a game-theoretic attention calibration method that uses the Harsanyi dividend . they selectively retain heads demonstrating significant cooperative gains and apply fine-grained adjustments to remaining heads . |
| Outcome: | The proposed framework is based on the Harsanyi dividend, a concept from cooperative game theory. |
Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions. |
| Approach: | They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in the zero-shot directions. |
GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to address address standardization are lacking in the current field. |
| Approach: | They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization. |
| Outcome: | The proposed framework incorporates spatial knowledge into address texts and achieves efficient address standardization. |
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning (2025.coling-main)
Copied to clipboard
| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities in multi-modal context comprehension, but they still suffer from hallucination problems due to inconsistent outputs with the image content. |
| Approach: | They propose a training-free framework MVP to reduce hallucinations in Large Vision-Language Models . they propose multi-view information-seeking strategy to perceive the comprehensive information in the image . |
| Outcome: | The proposed framework reduces hallucinations in large vision-language models by combining multi-view multi-path reasoning with multi-vision multi-path reasoning. |
PIP: Perturbation-based Iterative Pruning for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are growing in size and complexity, causing significant challenges for their practical deployment in resource-constrained environments. |
| Approach: | They propose a double-view structured pruning method that combines information from two different views to iteratively prune those that struggle to distinguish between them. |
| Outcome: | The proposed method reduces the parameter count by approximately 20% while retaining over 85% of the original model’s accuracy across varied benchmarks. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
Copied to clipboard
Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning. |
| Approach: | They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG. |
| Outcome: | The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation. |
A Survey on Cross-Lingual Summarization (2022.tacl-1)
Copied to clipboard
| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
| Approach: | They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other . |
| Outcome: | The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis. |
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to building cross-lingual summarization systems on dialogue documents are limited. |
| Approach: | They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents. |
| Outcome: | The proposed model outperforms pipeline models on ClidSum and mDialBART. |
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)
Copied to clipboard
| Challenge: | Temporal sentence localization in videos is an important yet challenging task in natural language processing. |
| Approach: | They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three challenging benchmarks. |
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
| Approach: | They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation . |
| Outcome: | The proposed method achieves state-of-the-art performance on three public datasets. |