@inproceedings{weidinger-etal-2025-aquaechr,
title = "{AQ}u{AECHR}: Attributed Question Answering for {E}uropean Court of Human Rights",
author = "Weidinger, Korbinian Q. and
T.y.s.s, Santosh and
Ichim, Oana and
Grabmair, Matthias",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.74/",
doi = "10.18653/v1/2025.findings-acl.74",
pages = "1418--1447",
ISBN = "979-8-89176-256-5",
abstract = "LLMs have become prevalent tools for information seeking across various fields, including law. However, their generated responses often suffer from hallucinations, hindering their widespread adoption in high stakes domains such as law, which can potentially mislead experts and propagate societal harms. To enhance trustworthiness in these systems, one promising approach is to attribute the answer to an actual source, thereby improving the factuality and verifiability of the response. In pursuit of advancing attributed legal question answering, we introduce AQuAECHR, a benchmark comprising information-seeking questions from ECHR jurisprudence along with attributions to relevant judgments. We present strategies to automatically curate this dataset from ECHR case law guides and utilize an LLM-based filtering pipeline to improve dataset quality, as validated by legal experts. Additionally, we assess several LLMs, including those trained on legal corpora, on this dataset to underscore significant challenges with the current models and strategies dealing with attributed QA, both quantitatively and qualitatively."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="weidinger-etal-2025-aquaechr">
<titleInfo>
<title>AQuAECHR: Attributed Question Answering for European Court of Human Rights</title>
</titleInfo>
<name type="personal">
<namePart type="given">Korbinian</namePart>
<namePart type="given">Q</namePart>
<namePart type="family">Weidinger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Santosh</namePart>
<namePart type="family">T.y.s.s</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oana</namePart>
<namePart type="family">Ichim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Matthias</namePart>
<namePart type="family">Grabmair</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Nabende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Taher</namePart>
<namePart type="family">Pilehvar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-256-5</identifier>
</relatedItem>
<abstract>LLMs have become prevalent tools for information seeking across various fields, including law. However, their generated responses often suffer from hallucinations, hindering their widespread adoption in high stakes domains such as law, which can potentially mislead experts and propagate societal harms. To enhance trustworthiness in these systems, one promising approach is to attribute the answer to an actual source, thereby improving the factuality and verifiability of the response. In pursuit of advancing attributed legal question answering, we introduce AQuAECHR, a benchmark comprising information-seeking questions from ECHR jurisprudence along with attributions to relevant judgments. We present strategies to automatically curate this dataset from ECHR case law guides and utilize an LLM-based filtering pipeline to improve dataset quality, as validated by legal experts. Additionally, we assess several LLMs, including those trained on legal corpora, on this dataset to underscore significant challenges with the current models and strategies dealing with attributed QA, both quantitatively and qualitatively.</abstract>
<identifier type="citekey">weidinger-etal-2025-aquaechr</identifier>
<identifier type="doi">10.18653/v1/2025.findings-acl.74</identifier>
<location>
<url>https://aclanthology.org/2025.findings-acl.74/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>1418</start>
<end>1447</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T AQuAECHR: Attributed Question Answering for European Court of Human Rights
%A Weidinger, Korbinian Q.
%A T.y.s.s, Santosh
%A Ichim, Oana
%A Grabmair, Matthias
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F weidinger-etal-2025-aquaechr
%X LLMs have become prevalent tools for information seeking across various fields, including law. However, their generated responses often suffer from hallucinations, hindering their widespread adoption in high stakes domains such as law, which can potentially mislead experts and propagate societal harms. To enhance trustworthiness in these systems, one promising approach is to attribute the answer to an actual source, thereby improving the factuality and verifiability of the response. In pursuit of advancing attributed legal question answering, we introduce AQuAECHR, a benchmark comprising information-seeking questions from ECHR jurisprudence along with attributions to relevant judgments. We present strategies to automatically curate this dataset from ECHR case law guides and utilize an LLM-based filtering pipeline to improve dataset quality, as validated by legal experts. Additionally, we assess several LLMs, including those trained on legal corpora, on this dataset to underscore significant challenges with the current models and strategies dealing with attributed QA, both quantitatively and qualitatively.
%R 10.18653/v1/2025.findings-acl.74
%U https://aclanthology.org/2025.findings-acl.74/
%U https://doi.org/10.18653/v1/2025.findings-acl.74
%P 1418-1447
Markdown (Informal)
[AQuAECHR: Attributed Question Answering for European Court of Human Rights](https://aclanthology.org/2025.findings-acl.74/) (Weidinger et al., Findings 2025)
ACL