@inproceedings{kabra-etal-2026-evident,
title = "{E}viden{T}: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability",
author = "Kabra, Anubha and
Kim, Katie Jooyoung and
Kou, Colin Zhiwei and
Sajer, Helene L and
Fan, Yimei and
Cisar, Radomir and
Greenhalgh, Heather and
Martinez Vidiri, Gabriel",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.69/",
pages = "1149--1178",
ISBN = "979-8-89176-460-6",
abstract = "Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval-augmented generation (RAG) over heterogeneous enterprise data can suffer from citation drift, unsupported content, and weak source traceability. We present EvidenT (T = Trust + Transparency + Traceability), a lightweight pipeline that verifies extracted evidence against retrieved documents before answer generation, without model retraining. EvidenT combines structured passage extraction with deterministic lexical alignment to filter unsupported content, correct citation drift, and preserve source-span traceability. On approximately 500 real enterprise queries, EvidenT improves gold-source hit rate by an average of 29{\%} over prompting baselines, produces no citations to non-retrieved urls, and achieves near-saturated answer-to-source lexical coverage."
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<abstract>Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval-augmented generation (RAG) over heterogeneous enterprise data can suffer from citation drift, unsupported content, and weak source traceability. We present EvidenT (T = Trust + Transparency + Traceability), a lightweight pipeline that verifies extracted evidence against retrieved documents before answer generation, without model retraining. EvidenT combines structured passage extraction with deterministic lexical alignment to filter unsupported content, correct citation drift, and preserve source-span traceability. On approximately 500 real enterprise queries, EvidenT improves gold-source hit rate by an average of 29% over prompting baselines, produces no citations to non-retrieved urls, and achieves near-saturated answer-to-source lexical coverage.</abstract>
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%0 Conference Proceedings
%T EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability
%A Kabra, Anubha
%A Kim, Katie Jooyoung
%A Kou, Colin Zhiwei
%A Sajer, Helene L.
%A Fan, Yimei
%A Cisar, Radomir
%A Greenhalgh, Heather
%A Martinez Vidiri, Gabriel
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F kabra-etal-2026-evident
%X Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval-augmented generation (RAG) over heterogeneous enterprise data can suffer from citation drift, unsupported content, and weak source traceability. We present EvidenT (T = Trust + Transparency + Traceability), a lightweight pipeline that verifies extracted evidence against retrieved documents before answer generation, without model retraining. EvidenT combines structured passage extraction with deterministic lexical alignment to filter unsupported content, correct citation drift, and preserve source-span traceability. On approximately 500 real enterprise queries, EvidenT improves gold-source hit rate by an average of 29% over prompting baselines, produces no citations to non-retrieved urls, and achieves near-saturated answer-to-source lexical coverage.
%U https://aclanthology.org/2026.emnlp-industry.69/
%P 1149-1178
Markdown (Informal)
[EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability](https://aclanthology.org/2026.emnlp-industry.69/) (Kabra et al., EMNLP 2026)
ACL
- Anubha Kabra, Katie Jooyoung Kim, Colin Zhiwei Kou, Helene L Sajer, Yimei Fan, Radomir Cisar, Heather Greenhalgh, and Gabriel Martinez Vidiri. 2026. EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1149–1178, Budapest, Hungary. Association for Computational Linguistics.