Papers by Biao Qin
Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering (2025.findings-emnlp)
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| Challenge: | a new metric is developed to pinpoint the moment of invocation when hallucinations arise in small LMs. |
| Approach: | They propose a metric that measures hallucinations during the generation process of small LMs. |
| Outcome: | The proposed metric outperforms baselines in hallucination detection across multiple QA datasets. |
Synchronized Video Storytelling: Generating Video Narrations with Structured Storyline (2024.acl-long)
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| Challenge: | Existing studies on dense video captioning and video story generation have made some progress, but in practical applications, we typically require synchronized narrations for ongoing visual scenes. |
| Approach: | They propose a task of Synchronized Video Storytelling to generate synchronized narrations for videos using a benchmark dataset with rich annotations. |
| Outcome: | The proposed framework can generate narrations with the guidance of the generated or predefined storyline and human evaluations validate the effectiveness. |
No Need for Large-Scale Search: Exploring Large Language Models in Complex Knowledge Base Question Answering (2024.lrec-main)
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| Challenge: | Knowledge Base Question Answering (KBQA) systems are a key research area in the field of natural language processing and information retrieval (IR). |
| Approach: | They propose to use large language models to convert natural language questions to structured knowledge representations by using a three-step fine-tune strategy to implement the KBQA system. |
| Outcome: | The proposed method achieves state-of-the-art performance across three datasets with a 79.9% F1 score. |
VC4VG: Optimizing Video Captions for Text-to-Video Generation (2025.emnlp-main)
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| Challenge: | Recent advances in text-to-video generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. |
| Approach: | They propose a caption optimization framework tailored to the needs of T2V models. |
| Outcome: | The proposed framework improves video caption quality and video generation performance. |