<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hoblitzell-2026-beyond">
    <titleInfo>
        <title>Beyond Hallucination: Reframing LLM Quality Assessment as Task-Output Alignment</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Andrew</namePart>
        <namePart type="family">Hoblitzell</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of The Big Picture v2: Crafting a Research Narrative</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yanai</namePart>
            <namePart type="family">Elazar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Allyson</namePart>
            <namePart type="family">Ettinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nora</namePart>
            <namePart type="family">Kassner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Ruder</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, CA, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-416-3</identifier>
    </relatedItem>
    <abstract>Current hallucination detection systems operate under a flawed assumption: that model outputs deviating from factual grounding are uniformly problematic regardless of task context, modality, or cultural setting. Through analysis of computational humor as a motivating case study, we demonstrate that identical model behaviors require radically different evaluations depending on context. We propose reframing hallucination detection as task-output alignment assessment, introducing a three-dimensional framework spanning factual grounding requirements, novelty requirements, and risk tolerance.</abstract>
    <identifier type="citekey">hoblitzell-2026-beyond</identifier>
    <identifier type="doi">10.18653/v1/2026.bigpicture-main.3</identifier>
    <location>
        <url>https://aclanthology.org/2026.bigpicture-main.3/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>22</start>
            <end>30</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Beyond Hallucination: Reframing LLM Quality Assessment as Task-Output Alignment
%A Hoblitzell, Andrew
%Y Elazar, Yanai
%Y Ettinger, Allyson
%Y Kassner, Nora
%Y Ruder, Sebastian
%S Proceedings of The Big Picture v2: Crafting a Research Narrative
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA, USA
%@ 979-8-89176-416-3
%F hoblitzell-2026-beyond
%X Current hallucination detection systems operate under a flawed assumption: that model outputs deviating from factual grounding are uniformly problematic regardless of task context, modality, or cultural setting. Through analysis of computational humor as a motivating case study, we demonstrate that identical model behaviors require radically different evaluations depending on context. We propose reframing hallucination detection as task-output alignment assessment, introducing a three-dimensional framework spanning factual grounding requirements, novelty requirements, and risk tolerance.
%R 10.18653/v1/2026.bigpicture-main.3
%U https://aclanthology.org/2026.bigpicture-main.3/
%U https://doi.org/10.18653/v1/2026.bigpicture-main.3
%P 22-30

Markdown (Informal)

[Beyond Hallucination: Reframing LLM Quality Assessment as Task-Output Alignment](https://aclanthology.org/2026.bigpicture-main.3/) (Hoblitzell, BigPicture 2026)

• Beyond Hallucination: Reframing LLM Quality Assessment as Task-Output Alignment (Hoblitzell, BigPicture 2026)

ACL

• Andrew Hoblitzell. 2026. Beyond Hallucination: Reframing LLM Quality Assessment as Task-Output Alignment. In Proceedings of The Big Picture v2: Crafting a Research Narrative, pages 22–30, San Diego, CA, USA. Association for Computational Linguistics.