Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910975219728384 |
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| author | Zhang, Yueqi Yuan, Peiwen Feng, Shaoxiong Li, Yiwei Wang, Xinglin Shi, Jiayi Tan, Chuyi Pan, Boyuan Hu, Yao Li, Kan |
| author_facet | Zhang, Yueqi Yuan, Peiwen Feng, Shaoxiong Li, Yiwei Wang, Xinglin Shi, Jiayi Tan, Chuyi Pan, Boyuan Hu, Yao Li, Kan |
| contents | Human-AI conversation frequently relies on quoting earlier text-"check it with the formula I just highlighted"-yet today's large language models (LLMs) lack an explicit mechanism for locating and exploiting such spans. We formalise the challenge as span-conditioned generation, decomposing each turn into the dialogue history, a set of token-offset quotation spans, and an intent utterance. Building on this abstraction, we introduce a quotation-centric data pipeline that automatically synthesises task-specific dialogues, verifies answer correctness through multi-stage consistency checks, and yields both a heterogeneous training corpus and the first benchmark covering five representative scenarios. To meet the benchmark's zero-overhead and parameter-efficiency requirements, we propose QuAda, a lightweight training-based method that attaches two bottleneck projections to every attention head, dynamically amplifying or suppressing attention to quoted spans at inference time while leaving the prompt unchanged and updating < 2.8% of backbone weights. Experiments across models show that QuAda is suitable for all scenarios and generalises to unseen topics, offering an effective, plug-and-play solution for quotation-aware dialogue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules Zhang, Yueqi Yuan, Peiwen Feng, Shaoxiong Li, Yiwei Wang, Xinglin Shi, Jiayi Tan, Chuyi Pan, Boyuan Hu, Yao Li, Kan Artificial Intelligence Computation and Language Human-AI conversation frequently relies on quoting earlier text-"check it with the formula I just highlighted"-yet today's large language models (LLMs) lack an explicit mechanism for locating and exploiting such spans. We formalise the challenge as span-conditioned generation, decomposing each turn into the dialogue history, a set of token-offset quotation spans, and an intent utterance. Building on this abstraction, we introduce a quotation-centric data pipeline that automatically synthesises task-specific dialogues, verifies answer correctness through multi-stage consistency checks, and yields both a heterogeneous training corpus and the first benchmark covering five representative scenarios. To meet the benchmark's zero-overhead and parameter-efficiency requirements, we propose QuAda, a lightweight training-based method that attaches two bottleneck projections to every attention head, dynamically amplifying or suppressing attention to quoted spans at inference time while leaving the prompt unchanged and updating < 2.8% of backbone weights. Experiments across models show that QuAda is suitable for all scenarios and generalises to unseen topics, offering an effective, plug-and-play solution for quotation-aware dialogue. |
| title | Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.24292 |