Papers by Guoshun Nan
ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)
Copied to clipboard
Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao
| Challenge: | Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD. |
| Approach: | They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD. |
| Outcome: | The proposed method can yield comparable results with GPT-4V, despite fewer parameters. |
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning. |
| Approach: | They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction. |
| Outcome: | The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters. |
Uncovering Main Causalities for Long-tailed Information Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Information Extraction (IE) aims to extract structural information from unstructured texts. |
| Approach: | They propose a framework that aims to uncover the main causalities behind data in the view of causal inference. |
| Outcome: | The proposed framework can detect the main causalities behind data in the view of causal inference. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)
Copied to clipboard
Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu
| Challenge: | Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. |
| Approach: | They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory. |
| Outcome: | The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence. |
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. |
Efficient and Robust Knowledge Graph Construction (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | Knowledge graph construction has appealed to the NLP community but has encountered similar issues such as efficiency and robustness. |
| Approach: | They propose to introduce efficient and robust knowledge graph construction techniques and discuss their results. |
| Outcome: | This tutorial will provide an overview of the latest and ongoing techniques for efficient and robust knowledge graph construction. |