@inproceedings{kapoor-etal-2026-dqa,
title = "{DQA}: Diagnostic Question Answering for {IT} Support",
author = "Kapoor, Vishaal and
Dundua, Mariam and
Yortucboylu, Evren and
Ahuja, Sarthak and
Kordjazi, Neda and
Li, Yiming and
padala, Vaibhavi and
Ho, Derek and
Whitted, Jennifer and
Steinert, Rebecca",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.79/",
doi = "10.18653/v1/2026.acl-industry.79",
pages = "1128--1135",
ISBN = "979-8-89176-394-4",
abstract = "Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG systems lack explicit diagnostic state and therefore struggle to accumulate evidence and resolve competing hypotheses across turns.We introduce DQA, a diagnostic question-answering framework that maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. DQA combines conversational query rewriting, retrieval aggregation, and state-conditioned response generation to support systematic troubleshooting under enterprise latency and context constraints.We evaluate DQA on 150 anonymized enterprise IT support scenarios using a replay-based protocol. Averaged over three independent runs, DQA achieves a 78.7{\%} success rate under a trajectory-level success criterion, compared to 41.3{\%} for a multi-turn RAG baseline, while reducing average turns from 8.4 to 3.9. This improvement reflects the benefit of explicitly representing competing explanations and aggregating evidence across turns in unscripted troubleshooting."
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<abstract>Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG systems lack explicit diagnostic state and therefore struggle to accumulate evidence and resolve competing hypotheses across turns.We introduce DQA, a diagnostic question-answering framework that maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. DQA combines conversational query rewriting, retrieval aggregation, and state-conditioned response generation to support systematic troubleshooting under enterprise latency and context constraints.We evaluate DQA on 150 anonymized enterprise IT support scenarios using a replay-based protocol. Averaged over three independent runs, DQA achieves a 78.7% success rate under a trajectory-level success criterion, compared to 41.3% for a multi-turn RAG baseline, while reducing average turns from 8.4 to 3.9. This improvement reflects the benefit of explicitly representing competing explanations and aggregating evidence across turns in unscripted troubleshooting.</abstract>
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%0 Conference Proceedings
%T DQA: Diagnostic Question Answering for IT Support
%A Kapoor, Vishaal
%A Dundua, Mariam
%A Yortucboylu, Evren
%A Ahuja, Sarthak
%A Kordjazi, Neda
%A Li, Yiming
%A padala, Vaibhavi
%A Ho, Derek
%A Whitted, Jennifer
%A Steinert, Rebecca
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F kapoor-etal-2026-dqa
%X Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG systems lack explicit diagnostic state and therefore struggle to accumulate evidence and resolve competing hypotheses across turns.We introduce DQA, a diagnostic question-answering framework that maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. DQA combines conversational query rewriting, retrieval aggregation, and state-conditioned response generation to support systematic troubleshooting under enterprise latency and context constraints.We evaluate DQA on 150 anonymized enterprise IT support scenarios using a replay-based protocol. Averaged over three independent runs, DQA achieves a 78.7% success rate under a trajectory-level success criterion, compared to 41.3% for a multi-turn RAG baseline, while reducing average turns from 8.4 to 3.9. This improvement reflects the benefit of explicitly representing competing explanations and aggregating evidence across turns in unscripted troubleshooting.
%R 10.18653/v1/2026.acl-industry.79
%U https://aclanthology.org/2026.acl-industry.79/
%U https://doi.org/10.18653/v1/2026.acl-industry.79
%P 1128-1135
Markdown (Informal)
[DQA: Diagnostic Question Answering for IT Support](https://aclanthology.org/2026.acl-industry.79/) (Kapoor et al., ACL 2026)
ACL
- Vishaal Kapoor, Mariam Dundua, Evren Yortucboylu, Sarthak Ahuja, Neda Kordjazi, Yiming Li, Vaibhavi padala, Derek Ho, Jennifer Whitted, and Rebecca Steinert. 2026. DQA: Diagnostic Question Answering for IT Support. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1128–1135, San Diego, California, USA. Association for Computational Linguistics.