@inproceedings{wang-etal-2025-cstree,
title = "{CST}ree-{SRI}: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts",
author = "Wang, Zhaowen and
Wei, Xiang and
Du, Kangshao and
Zhang, Yiting and
Qin, Libo and
Xia, Yingjie and
Kuang, Li",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1286/",
doi = "10.18653/v1/2025.acl-long.1286",
pages = "26502--26525",
ISBN = "979-8-89176-251-0",
abstract = "Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. However, their performance significantly declines when applied to multi-turn QA over extra-long context (ELC), as they struggle to capture the logical correlations across multiple chunks of ELC and maintain the coherence of multi-turn Questions. To address the challenges, we propose the CSTree-SRI framework (Cognitive Semantic Tree through Summarization, Retrieval, and Introspection). CSTree-SRI dynamically constructs the CSTree to preserve logical coherence within ELC through hierarchical synthesis and introspective validation. Then a logic-driven traversal strategy on CSTree is designed to provide efficient information retrieval for question answering. Additionally, we construct a suite of multi-turn QA datasets and an evaluation benchmark tailored for ELC tasks, and comprehensive experiments demonstrate the framework{'}s superiority in addressing the challenges of multi-turn QA over ELC."
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<abstract>Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. However, their performance significantly declines when applied to multi-turn QA over extra-long context (ELC), as they struggle to capture the logical correlations across multiple chunks of ELC and maintain the coherence of multi-turn Questions. To address the challenges, we propose the CSTree-SRI framework (Cognitive Semantic Tree through Summarization, Retrieval, and Introspection). CSTree-SRI dynamically constructs the CSTree to preserve logical coherence within ELC through hierarchical synthesis and introspective validation. Then a logic-driven traversal strategy on CSTree is designed to provide efficient information retrieval for question answering. Additionally, we construct a suite of multi-turn QA datasets and an evaluation benchmark tailored for ELC tasks, and comprehensive experiments demonstrate the framework’s superiority in addressing the challenges of multi-turn QA over ELC.</abstract>
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%0 Conference Proceedings
%T CSTree-SRI: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts
%A Wang, Zhaowen
%A Wei, Xiang
%A Du, Kangshao
%A Zhang, Yiting
%A Qin, Libo
%A Xia, Yingjie
%A Kuang, Li
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-cstree
%X Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. However, their performance significantly declines when applied to multi-turn QA over extra-long context (ELC), as they struggle to capture the logical correlations across multiple chunks of ELC and maintain the coherence of multi-turn Questions. To address the challenges, we propose the CSTree-SRI framework (Cognitive Semantic Tree through Summarization, Retrieval, and Introspection). CSTree-SRI dynamically constructs the CSTree to preserve logical coherence within ELC through hierarchical synthesis and introspective validation. Then a logic-driven traversal strategy on CSTree is designed to provide efficient information retrieval for question answering. Additionally, we construct a suite of multi-turn QA datasets and an evaluation benchmark tailored for ELC tasks, and comprehensive experiments demonstrate the framework’s superiority in addressing the challenges of multi-turn QA over ELC.
%R 10.18653/v1/2025.acl-long.1286
%U https://aclanthology.org/2025.acl-long.1286/
%U https://doi.org/10.18653/v1/2025.acl-long.1286
%P 26502-26525
Markdown (Informal)
[CSTree-SRI: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts](https://aclanthology.org/2025.acl-long.1286/) (Wang et al., ACL 2025)
ACL