@inproceedings{xu-etal-2026-igtf,
title = "{IGTF}: Intent-Guided, Game-Inspired Structured Intent Calibration for {LLM}-Assisted Fake News Detection",
author = "Xu, Kun and
Mou, Zongyuan and
Rao, Yuan and
Xin, Yufei and
Xin, Tingting",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1433/",
pages = "30548--30575",
ISBN = "979-8-89176-462-0",
abstract = "Fake news detection increasingly relies on LLMs to extract semantic and pragmatic signals. We study a narrower problem than factual hallucination: when an LLM scores multiple intent dimensions, the resulting vector may contain internally inconsistent labels. We present IGTF, an intent-guided, game-inspired structured intent calibration framework for LLM-assisted fake news detection. IGTF defines an expert-validated nine-dimensional intent taxonomy, extracts intent-aware text representations with a Mixture-of-Experts encoder, and calibrates LLM-produced intent vectors through a fixed graph of cooperative and conflicting intent relations. The refinement is motivated by a potential-game formulation, whereas the deployed algorithm is a differentiable fixed-point update rather than an exact Nash-equilibrium solver. We prove convergence to a unique fixed point under a contraction condition and evaluate the refinement empirically. Across four benchmark datasets, IGTF improves over supervised GPT-4+BERT fusion baselines by 4.07 F1-macro points on average, with the largest gains when raw intent vectors show stronger relation-level inconsistency. Human evaluation and graph-perturbation controls suggest that these gains come from structured intent calibration rather than LLM features alone."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-etal-2026-igtf">
<titleInfo>
<title>IGTF: Intent-Guided, Game-Inspired Structured Intent Calibration for LLM-Assisted Fake News Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kun</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zongyuan</namePart>
<namePart type="family">Mou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuan</namePart>
<namePart type="family">Rao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yufei</namePart>
<namePart type="family">Xin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tingting</namePart>
<namePart type="family">Xin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Fake news detection increasingly relies on LLMs to extract semantic and pragmatic signals. We study a narrower problem than factual hallucination: when an LLM scores multiple intent dimensions, the resulting vector may contain internally inconsistent labels. We present IGTF, an intent-guided, game-inspired structured intent calibration framework for LLM-assisted fake news detection. IGTF defines an expert-validated nine-dimensional intent taxonomy, extracts intent-aware text representations with a Mixture-of-Experts encoder, and calibrates LLM-produced intent vectors through a fixed graph of cooperative and conflicting intent relations. The refinement is motivated by a potential-game formulation, whereas the deployed algorithm is a differentiable fixed-point update rather than an exact Nash-equilibrium solver. We prove convergence to a unique fixed point under a contraction condition and evaluate the refinement empirically. Across four benchmark datasets, IGTF improves over supervised GPT-4+BERT fusion baselines by 4.07 F1-macro points on average, with the largest gains when raw intent vectors show stronger relation-level inconsistency. Human evaluation and graph-perturbation controls suggest that these gains come from structured intent calibration rather than LLM features alone.</abstract>
<identifier type="citekey">xu-etal-2026-igtf</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.1433/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>30548</start>
<end>30575</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T IGTF: Intent-Guided, Game-Inspired Structured Intent Calibration for LLM-Assisted Fake News Detection
%A Xu, Kun
%A Mou, Zongyuan
%A Rao, Yuan
%A Xin, Yufei
%A Xin, Tingting
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-igtf
%X Fake news detection increasingly relies on LLMs to extract semantic and pragmatic signals. We study a narrower problem than factual hallucination: when an LLM scores multiple intent dimensions, the resulting vector may contain internally inconsistent labels. We present IGTF, an intent-guided, game-inspired structured intent calibration framework for LLM-assisted fake news detection. IGTF defines an expert-validated nine-dimensional intent taxonomy, extracts intent-aware text representations with a Mixture-of-Experts encoder, and calibrates LLM-produced intent vectors through a fixed graph of cooperative and conflicting intent relations. The refinement is motivated by a potential-game formulation, whereas the deployed algorithm is a differentiable fixed-point update rather than an exact Nash-equilibrium solver. We prove convergence to a unique fixed point under a contraction condition and evaluate the refinement empirically. Across four benchmark datasets, IGTF improves over supervised GPT-4+BERT fusion baselines by 4.07 F1-macro points on average, with the largest gains when raw intent vectors show stronger relation-level inconsistency. Human evaluation and graph-perturbation controls suggest that these gains come from structured intent calibration rather than LLM features alone.
%U https://aclanthology.org/2026.findings-emnlp.1433/
%P 30548-30575
Markdown (Informal)
[IGTF: Intent-Guided, Game-Inspired Structured Intent Calibration for LLM-Assisted Fake News Detection](https://aclanthology.org/2026.findings-emnlp.1433/) (Xu et al., Findings 2026)
ACL