DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Authors: Tobias Braun
Mark Rothermel
Marcus Rohrbach
Anna Rohrbach
Venue: ICML 2025 poster (2025)
Keywords: fact-checking
multimodal
claim-verification
MLLM
Primary area: applications->everything_else
TL;DR: We introduce DEFAME, a modular, training-free fact-checking system that verifies open-domain text-image claims end-to-end, dynamically retrieving and integrating textual and visual evidence.

Abstract: The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present **D**ynamic **E**vidence-based **FA**ct-checking with **M**ultimodal **E**xperts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image claim verification. DEFAME operates in a six-stage process, dynamically selecting the tools and search depth to extract and evaluate textual and visual evidence. Unlike prior approaches that are text-only, lack explainability, or rely solely on parametric knowledge, DEFAME performs end-to-end verification, accounting for images in claims *and* evidence while generating structured, multimodal reports. Evaluation on the popular benchmarks VERITE, AVeriTeC, and MOCHEG shows that DEFAME surpasses all previous methods, establishing itself as the new general state-of-the-art fact-checking system for uni- and multimodal fact-checking. Moreover, we introduce a new multimodal benchmark, ClaimReview2024+, featuring claims after the knowledge cutoff of GPT-4o, avoiding data leakage. Here, DEFAME drastically outperforms the GPT-4o baselines, showing temporal generalizability and the potential for real-time fact-checking.

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Tobias Braun
Mark Rothermel"
Mareus Rohrbach
Anna Rohrbach
Abstract
Fact-Check Report
The proliferation disinformation demands re- liable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), modu- lar, zero-shot MLLM pipeline for open-domain, text-image claim verification. DEFAME oper- ates in a six-stage process, dynamically selecting the tools and search depth to extract and evalu- ate textual and visual evidence. Unlike prior ap- proaches that are text-only, lack explainability, or rely solely on parametric knowledge, DEFAME performs end-to-end verification, accounting for images in claims and evidence while generating structured, multimodal reports. Evaluation on the popular benchmarks VERITE, AVERITEC, and MOCHEG shows that DEFAME surpasses all previous methods, establishing itself as the new general state-of-the-arr fact-checking system for uni- and multimodal fact-checking. Moreover, introduce new multimodal benchmark, CLAIM- 2024+, featuring claims after the knowl- edge cutoff GPT-40, avoiding data leakage. Here, DEFAME drastically outperforms the GPT- 40 baselines, showing temporal generalizability and the potential for real-time fact-checking"
Cisim
Tmaage stowe
Multimodal Claim Image(s)
with partage shar buy Musio Festivol
Actions
6g Roverse Image Search, Q, Search
Evidence
DEFAME 6-etage MILMM pipeline
Glesinbury
I..3 Aradle dP ingion E-J]
Multimodal External Knowledge Web Search
Elaboration
Actions
Verdict
Justification
image accuratlly depicts
Glastonbur
Festiva in 5, conl rmed by
Supported
image inclading stoc
dible
Nevos dP nutiington
Figure 1: DEFAME nutshell: It fact-checks multimodal claims using multimodal evidence and returns detailed, human-friendly report document.
reaches a broader audience, and causes greater harm
Li &
Xie 2020 Zannettou et al.) 2018: Wang et al. 2020 Chen
& Shu 2024). Humans perceive multimodal information as more credible (Newman et al. 2012 Hameleers et al. 2020).
1. Introduction
often interpreting visuals as "evidence" (Greifeneder et al..
202T), making particularly persuasive. Approximately
In recent years, misinformation has been growing in scale and quality (Chen Shu 2024) beyond human capacity to fact-check. "Fake news" has evolved from a lighthearted into a serious global threat (World Economic Forum 2024). Driven by higher engagement rates social media and the increase in Al use, misinformation spreads faster,
80% of the claims checked by professional fact-checkers
are multimodal (Dufour et al. 2024 signifyingggggg strong priority for checking multimodal content.
Unfortunately, Automated Fact-Checking (AFC) systems are mostly text-only (Dmonte et al.) 2024 Vykopal et al., 2024). Only a few works venture into visual claim verifica- tion (Khaliq et al.) 2024 Shao et al. 2023). but none can handle both multimodal claims and multimodal evidence once. Most multimodal claim verification systems cannot retrieve the evidence needed to verify claim (Fu et al., 2024 Vo-Hoang et al. 2024 Tang et al. (024). The major- ity of AFC works focus on a specific aspect of AFC such as evidence retrieval (Cheung & Lam 2023), evidence summa- rization (Chen et al. 2024). or evidence ranking (Tahmasebi
Equal contribution Technical University of Darmstadt & Aes Germany. Correspondence to: Mark Rothermel <mark.rothermellc tu-darmstadt.dee.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025, Copyright 2025 by the author(s).
We released the code and benchmark dataset publicly at: https: httpt//gttbbbbbu com/mu t moda -a---allba
DEFAME/t hte/i/teeeiccoo

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DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
at.l.
This specialization has created a scattered land- scape where individual approaches address isolated aspects of complex problem. AFC systems that target the overall task of fact-checking typically are text-only (Zhao et al.
2024).
only specific domain, e.g., charts (Akhtar al.
2023a
2024 social media (Wang et al. 2018), politics Khaliq
et al. 2024), or news (Xu et al. 2024] Shao et al. 2023).
Others investigate the incorporation of new architectures like a knowledge graph (Cao et al. 2024) or address the problem of evidence ambiguity (Glockner et al. 2024). In contrast to all these methods, DEFAME combines the frag- mented landscape of AFC work into one end-to-end solu- tion not restricted to only one modality, domain, or sub- task. Tablee[componee DEFAME to prior work.
2024
[i et al. 2024c), lack performance (Chen et al.) 2024 2023 Papadopoulos et al. 2024a), do not involve
Cao et al.
a flexible planning module (Khaliq et al. |024] Tahmasebi
al.
).24). or are not explainable (Shao et al. 2023 Xu
et al.
2024
Therefore, as the main contribution of this paper, we intro- duce DEFAME, a straightforward end-to-end AFC frame- work, unifying the advancements in the field of AFC into one single system. As the first of its kind, it is able to na- tively process multimodal claims and evidence, the latter of which it retrieves dynamically as needed. DEFAME is designed with transparency in mind, imitating human fact-checking process and returning detailed fact-check report to the user (cf. Figure 1.
Text-only fact-checking: The vast majority of proposed AFC systems is purely text-based (Hassan et al. 2018
2017
Thore et al. et al. 2024 Vykopal et al. 2024 Yang & Rocha 2024
Schlichtkrull et al. 2024b
Dmonte
Zhao al. 2024 Wang et al. 2024b Li et al. 2024b Pan
et al.
2023 Schlichtkrull et al. 2024a Cheung & Lam
2023 The most popular benchmarks to evaluate these
methods are LIAR (Wang, 2017). FEVER (Thorne et al., 2018). and AVERITEC (Schlichtkrull et al. 2024b), the lat- ter of which mitigates important weaknesses of the previous ones, including reliance on artificial claims and evidence insufficiency. For this reason, we use AVERITEC in our evaluation. Most recently, FACTCHECK-BENCE (Wang) et al. 2024a) was introduced to evaluate the factuality of entire LLM responses.
We empirically demonstrate DEFAME's effectiveness and generality by establishing new state-of-the-art re- sults on three diverse and widely used benchmarks, surpassing even the most specialized methods. On AVERITEC (Schlichtkrull et al. 2024b), we improve ac- curacy from 65.6% to 70.5%; on MOCHEG (Yao et al. ) we achieve a 100.6% improvement in accuracy, and on VERITE (Papadopoulos et al. 2024b), we enhance True/False accuracy by +25.9%.
Multimodal fact-checking: Popular multimodal AFC benchmarks include works from [Xu et al. (2024); Nielsen & McConville (2022): Zlatkova al. (2019); Nakamura et al.
Additionally, we contribute a new benchmark, CLAIMRE- VIEW2024+, with claims that occurred after the knowledge cutoff of GPT-40 to mitigate the effects of data leakage. We show that DEFAME is superior to GPT-40 on these "unseen" statements. We show that the fact-checking reports generated by DEFAME are well-received by human evalu- ators, who prefer them over GPT-40's 's outputs. Finally, we make our code and benchmark publicly available"
2020 Aneja et al.
(2021): Yao et al. (2023): Jaiswal et al.
2017): ; Sabir et al.
(2018); MMüllerBBdade
al. (2020);
Luo et al. 2222 aa). Notably, MOCHEG (Yao et al. 2023
builds on real-world claims, requiring multimodal evidence retrieval, additionally incorporating the task of justification generation. Given these features, we use MOCHEG in our evaluation. A strong emphasis of multimodal AFC has been on Out-Of-Comtext (OOC) image detection. sometimes referred to "cheapfake" detection. Popular evaluation benchmarks include NEWSCLIPSINGS (Luo et al. 202 and, more recently, VERITE (Papadopoulos et al. 2024b).
2. Related Work
Automating the task of fact-checking is difficult problem. far from being solved (Akhtar et al. 2023b Dmonte et al. 2024 Yao et al. 2023; Schlichtkrull et al. 2024a). Due to its complexity, Akhtar et al. (2023b) subdivide AFC into three stages: (1) claim detection & extraction, (2) evidence retrieval, and (3) verdict prediction. Most approaches nar- row down their scope, either by focusing on sub-task like summarization (Chen et al.) (024)), justification generation (Atanasova et al. 2020), evidence retrieval (Samarinas et al. 2021). deepfake detection (Jia et al. 2024), 4) out-of-conteed of-context detection (Xu et al. 2024 Vo-Hoang et al., (024), image
In our experiments, we use VERITE because it improves over previous benchmarks by reducing unimodal bias and incorporating real-world samples.
Some multimodal AFC solutions utilize multimodal fu- sion (Papadopoulos et al.; 2023 2024a| Shao et al.) 2023 Zeng et al.) 2024b), leverage inter-, and cross-modal con- sistency (Abdelnabi et al. 2022 Fu et al. 2024), or apply conventional machine learning models (Papadopoulos et al.) 2024a| Wang et al. 2018). A strong disadvantage of these systems is the inability to produce human- uumaa-nnnerstandablee explanations of the predictions. Furthermore, in stark con- trast to DEFA ME, they often rely on superficial pattern matching or lexical/visual similarity, ignoring factuality and
contextualization (Tonglet et al. 2024), or by addressing
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2

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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Multimodal claims
Multimudal evid.
Evidence retrieval
Malti-hup
Planning
Reasoning
Explainable
Training- Free
Open-domain
Open source
Tools
Method
Backbone
RAGAR
Khalig et
2024
MLLM MLLM VLT & LLM VLT LLM & VLT
MMD-AGENT
SNIFFER
O1
RIMOOC-CHECKS
Xu
2024
Vo- To-HOANG AL.
Vo- Hoang
2024
ZENG ET AL.
Zeng ct
20240
MLLM CLIP & VLT VLT
CHASMA
Papadopoulos et al
20244
AITR
Papadoponios ct m
2124a
HAMMER
Shao et al 2023
BERT & VIT VLT (MILLM VLT
MELTIMD
Fu ct al 2024
LYL
Tahmasebi el
MOCHEG
METASUM
VLT & LLM
MDD
Tang
2024
VLT & GON
CHARTBERT
Akhtar
2133
BERT
PACAR
Zhhio et al
2024
LLM
HISS
Zhang & Gao
2023
LLM
PROGRAMFC
Pan et al
2023
LLM
CHEF-CHECKER
Li ct ml.
1744c
LLM
FACTLLAMA
Cheung Lam
2023
LLM
CFR
Srram et
2024
BERT & LLM L.M
DEBERTA
DEFAME (Ours)
MLLM
Table I: Overview of the most relevant and published AFC systems. We consider a method "Explainable" if it offers substantial (v) or some (0) human-rcadable information explaining the decision. Methods with "VLT" backbones employ a model from the Vision Language Transformer family.
logic, raising questions about their robustness and actuality.
some prior work (Yang & Rocha 2024 Tang et al. 2024). it does nor rely on benchmark-providee gold evidence, rein- forcing its adaptability to real-world misinformation.
(M)LLM-basedd fact-checking: With the rise of (Multi-
modal) Large Language Models or (M)LLMs, the AFC com-
munity increasingly explored prompt-driven solutions Yang
Rocha 2024| Khaliq et al., 2024 Tahmasebi et al.
2024
3. DEFAME Approach.
Chen et al. 2024 Pan et al.
2023 Cheung Lam 2023)
Large Language Model (L.LM) agents have become a pow- erful solution for commonsense reasoning, summarization, basic planning, and tool use (Davis) 2023 Li et al.) 2024d Zeng al. 2023 Surís et al. 2023). DEFAME (see Figure comprises a Multimodal L.L (MLLM), a suite of mul- timodal tools, and a structured fact-check report. Our pro- posed framework effectively operates as a dynamic, multi- step RAG system (Lewis et al., 2020), inspired by estab- lished fact-checking workflows (Moreno Gil al.. 2021). Each call the MLLM includes the current state of the fact-checking report contextual input, along with task- specific description. This approach emulates a form of context awareness, guiding the MLLM to focus on pertinent information at each stage of the fact-checking process and al- lowing for more intricate, multi-hop reasoning and evidence retrieval. Nonetheless, LLM's limitations like hallucina- tions, knowledge cutoff, and stochastic outputs (Maynez et al. 2020 Li & Flanigan 2024) necessitate careful man- agement. Accordingly, we decompose the fact-checking process into six manageable stages, five of which are sub- ject to MILMM prompting. The procedure mimics human
One of DEFAME's closest relatives, RAGAR (Khaliq
al. 2024), processes textual and visual claims but re- trieves only textual evidence. Furthermore, DEFAME di- rectly incorporates the claim image in its context while RAGAR converts it into a textual description, discarding critical visual information.
While there have been efforts to improve the performance of MLLM-basee approaches through training on synthetic data (Zeng et al. 2024a), most still rely on the parametric knowl- edge of the MLLMs (Beigi et al. 2024 Shao et al. 2023 Li et al. 2024e Geng et al., 2024). foregoing external evi- dence retrieval. This approach has three major drawbacks:
(1) MILM knowledge is static and fails on recent claims, as shown in this work; (2) predictions lack links to verifiable sources, reducing transparency; and (3) reliance on paramet- ric knowledge increases hallucination risks, making such Generation (RAG)-based approaches. In contrast, DEFAME uses inter- nal knowledge mostly for commonsense reasoning, retriev- ing evidence dynamically through external tools. Unlike
methods less reliable than
3

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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
It applies Google Image Search re- turn up to 3 URLs of web pages containing images, diagrams, and infographics that match given textual caption. These retrieved visuals can serve as evidence the report and/or as inputs for DEFAME's Geoloca- tion and RIS tools.
Image Search:
Always in Context
MLLM GPT 4o, GPT LLAVA, LLAMA
Claim
1. Plan
Actions
Summarize
Evidence
Report
Reverse Image Search (RIS): For given image, it re- trieves up 3 URLs of web pages containing the same image, using the Google Vision APp It serves real-time image knowledge base, enabling DEFAME to contextualize given image with information such as image sources, authors, prior uses, and approximate dates that a static trained MLLM, with fixed para- metric knowledge, cannot offer.
4. Develop
Elaboration
Judge
Verdict
6. Justify
Justification
4. Geolocation integrates GEOCLIP (Cepeda et al. specialized geolocation tool designed esti- mate the most probable countries from which image could originate. MLLMs lack the ability for this AFC- critical task (Roberts et al. 2024
2023
X External Tools
2. Execute
Web Search Google Search
Image Search
Googie image Search
Supported
Conflicting
RIS Google Vision
Geolocation GeoCLIP
Refuted
To prevent temporal leakage, all web- tools restrict search results to sources published before the claim's re- lease date (if known). Additionally, we exclude major fact- checking websites and any sites that disallow automated bot access. A list of excluded domains can be found in Appendix [ For each retrieved URL, we scrape the corre- sponding page using Firecraw Unlike previous work, we extend the scraper to identify and download any referenced image, ensuring a complete context for the fact-check.
NEI
Overview of DEFAME's dynamic six-stage pipeline involving three main components: MILLM, fact-checking report, and external tools.
Figure 2:
fact-checkers and is described in detail in the following.
Plan Actions. Upon receiving a claim, the MILM is prompted to suggest targeted action sequence to retrieve missing information. Since the action space is infinite for some tools (e.g.. web search allows arbitrary queries), the "planner" aims to minimize actions and cost. To prevent redundancy. DEFAME tracks previously exe- cuted actions and adapts it encounters a "dead end." In- context learning guides the module in deciding which of the specialized tools invoke: Web Search, Image Search, Re-
Stage 1:
Stage 3: Summarize Results. At this stage, the gathered evidence is integrated into the fact-checking report, which guides the MILM's reasoning through in in-context learning. The model generates an abstractive summary of key find- ings for each tool output, ensuring brevity and alignment with the existing report. Relevant images are retrieved and incorporated, while irrelevant results are filtered by instruct- ing the MLLM to return NONE if they do not contribute meaningfully to the verification process.
verse Image Search (RIS), or Geolocation. While RIS and Geolocation handle image inputs, Web Search and Image Search operate on dynamically generated text queries.
Stage 4: Develop the Fact-Check. Corresponding to
Stage in Moreno Gil et al. (022T). DEFAME brings claim and summarized evidence together. It directs the MLLM to discuss the claim's 's veracity step-by-step based on the evi- dence, flagging any gaps as "incomplete" if the information is missing. This stage offers room for intricate reasoning, de- ducing new insights through natural language inference. serves as an immediate preparation for the following stage.
Stage Execute Actions. Given set of actions, DE- FAME invokes the corresponding tool:
1. Web Search: Given a textual search query, it leverages Google Search via the Serper APf to provide the top 3 relevant web pages matching the query. This gives DE- FAME foundational access to the open web, enabling it to retrieve current or very domain-specific evidence that not encoded in the MLLM's parameters.
Stage 5: Predict Verdict. Next, DEFAME classifies the claim into one of the benchmark-specific categories by
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DEFAME: Dynamic Evidence-basee FAct-checking with Multimodal Experts
prompting the MILM summarize key findings and select verdict. If the model returns NEI (Not Enough Informa- tion), the system loops back to Stage to retrieve additional evidence, enabling deeper exploration of unresolved aspects. The process proceeds to the final stage once a definitive verdict is reached or three iterations have been completed, reflecting the iterative nature of human fact-checking.
2024
Rishi Sunak Sep 2024
account TTRobbin..
97% of all guns are in the red ferritory
Sep 16, 2024
Prime Minister riled faslest growing ecanomy in
Misleading
97% of all gun violence is blue
user B Aug 12, 2024
"That map shows areas of the United States with high rates gun violence vs. areas of the map with high rates gun owirership.
"Louis Armstrong drawing on fan's foreheed. !
Supported
Stage 6: Justify the Verdict. Human readability and ex- plainability are essential to effective fact-checking. While the fact-checking report provides a detailed and transparent account of the verification process, its length can become very ponn-potentially overwhelming users seeking quick understanding of the outcome. This final, post-prediction stage addresses that need by generating concise summary that distills the key findings and critical evidence, including hyperlinks. To this end, the MILMM is prompted with the
Refuted
Wesbo usens Jun 28, 2024
X
"A Ruasian lextbook clai- ming U.S. Presi- cent Denaid Trump lest the 2020 presidential elec- tion due voter fraud"
@ Dec 11, 2024
"S Moon landing is god.
Supported
Refuted
Jeftrey Goldborg Oct 25, 2024
Facebook users Ci Nov 10, 2024
complete report and tasked with producing a focused, read- able justification. The resulting summary is appended to the full report, serving both as an accessible explanation for end users and as support tool for further human verification.
"A Syrie's president
2020, Trump said two people private at the White House: 'I need the kind of ganarals Hitler
al-Assad Moscow Amarican conservative politicll commentatar Cartson."
"Impact of human-inda- ced CO2 iniggificant compared natural climale verietions. *
Refuted
NEI
Refuted
4. CLAIMREVIEW
Examples from our CLAIMREVIEW bench- mark, containing claimant, claim date, claim, and verdict.
Figure 3:
We introduce 2001 a novel benchmark designed to evaluate fact-checking models on claims that fall outside the pretraining scope of current MLLMs, avoiding the data leakage issue (see Appendix for more details). To this end, we curate set of 300 English claims from the Google FactCheck Claim Search APA which indexes professionally fact-checked claims published by organiza- tions such as Snopes, PolitiFact, and AFP via the Claim- Review markup standard All claims are sampled from fact-checking articles from the period between November 2023, and January 18, 2025-nntably, after the October to sim-
release), while ambiguous ones are manually labeled by a PhD--evel AFC researcher. The dataset is validated by a second annotator, comparing all instances with the original fact-checking articles directly.
To prevent leakage of veracity judgments, we manually edit claims that contain explicit fact-checking language (e.g.. rewriting "Fact Check: Image from Pakistan falsely claims..." to "Image from Pakistan shows..."". Since the ClaimReview API often returns teaser images with overlays or composites, all images are manually curated to reflect the original visuals referenced in the claims.
2023 knowledge cutoff of GPT-4o GPT-40-enabling ulate realistic, temporally cnttof-distribution evaluation setting, The dataset consists of 160 unimodal (text-only) and 140 multimodal (text-image) claims (cf. Fig. 3
The final label distribution is: 129 Refuted, 89 Supported, 61 O Misleading, and 21 ? NEI. While we ensure all claims are posted after the GPT-40 cutoff date, we note that some may represent earlier events re-evaluated by fact-checking organizations. Given its source diversity and topical focus on timely, high-impact misinformation, we believe CLAIMREVIEW2024 offers challenging, real- world testbed for current fact-checking models.
Claims are retrieved via keyword-based queries across ten broad topics (e.g., politics, health, climate) and de- duplicated based on review URLs. For each instance, we extract the date and the claimant for clearer claim con- text. If the claim date is not present in the markup, we use the review's release date instead. Labels are adapted from those provided by the fact-checking organizations mapped to a unified four-class taxonomy inspired by AVERITEC: Supported, Refuted, Misleading, and NEI see Appendix B for the specific class definitions. Label assign- ment follows two-stage process: Trivial cases are mapped automatically using an LLM-based script (included in the
5. Experiments
5.1. Datasets
Next to CLAIMREVIEW2024+, we evaluate DEFAME on three well-known fact-checking datasets, representative of distinct areas in fact-checking literature.
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5

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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
DEFAME backbone
MLLMs. To account for open-source MLLMs, we also test LLAVA-ONEVISION (1V) (7B) (Li et al. 2024a), and LLAMA 4 SCOUT (Meta AI, 2025). DEFAME includes the MLLM without any fine-tuning, with temperature set to 0.01 and top-p to 0.9 to control response diversity. We limit the number of images per scraped web page to a maximum of 32 to avoid an excessive flood of images. DEFAME pro- cesses interleaved text-image inputs, preserving the original position of images within the text context, but any input exceeding the MLLM's maximum context window is trun- cated accordingly. Table 2 shows the performance of the different backbones. with formatting outputs consistently for downstream use, leading to a substantial performance drop. While LLAMA SCoUT addressed this issue and achieved results comparable to GPT-40 MIN--uuggetinng that open-source models are gradually closing the Sap-GPT-40 still outperformed all other backhones by wide margin, particularly on CLAIM- REVIEW2024+ and VERITE.
GPT-40 MINI
LLAVA-IV
GPT-40
LLAMA
Dataset
AVERITEC
70.5
68.8
49.3
67.0
MOCHEG
59.2
55.5
42.1
55.0
VERITE
83.9
67.1
59.3
72.3
CLA1MREVIEW2024+
69.7
47.7
32.6
48.8
Table 2: DEFAME accuracy with different backbone MLLMs (columns) on the four benchmarks (rows).
(7B) struggled
AVERITEC (Schlichtkruil et al. 2024b) is a popular text- only real-world-based benchmark. The development set consists of 500 claims: 305 Refuted, 122 Supported, 35 NEI (Not Enough Information), and 38 claims with the label that designates claims with conflicting evidence or claims that are techni- cally true but lack context. We retrieve evidence from the benchmark- beechmark-complemeettrr Knowledge Base (KB), which contains the necessary evidence along with approximately 1, 000 unrelated resources to simulate open web search. Thus, for AVERITEC, the Web Search Tool does not uti- lize the Serper API but rather a semantic search, yielding 5 results for each search. Each query to the KB is encoded using sate-base-en-- vl.5 5 AAiibab-NLLP 2024); the clos- est documents to the search query are retrieved via k-nearest neighbor. We report the accuracy over all 4 classes.
+ C/CP
5.3. Comparison to the State-of-the-Art
In Table 3 we present our evaluation results compared to State-of-the-At (SOTA) methods and two baselines: GPT- 40, which directly generates a verdict ("Determine the claim's veracity by picking one of the following decision options...") and GPT-40 Chain-of-Thouuge (CoT), which also relies solely on parametric knowledge but is instructed to perform step-by-step reasoning before generating the ver- dict. For robust comparison, we run both baselines and our main variant three times on each dataset and report the mean performance. DEFAME achieves an accuracy of 70.5% on the AVERITEC benchmark and surpasses the previous SOTA (Cao et al., 2023) that deploys a CoT LLM approach. With an overall True vs. False" accuracy of 83.9% on VERITE, DEFAME ranks 25.9 percentage points above prior best result (Papadopoulos et al., 2024a), similar for "O True vs. Miscaptioned". It performs competitively on the True vs. OOC" accuracy. On MOCHEG, our framework achieves an average accuracy of 59.2% 99,2, replac- ing the METASUMMERCEIVERR (Chen et al. 2024) as the new SOTA fact-checking system.
MOCHEG (Yao et al. 2023) features textual claims paired with text-image evidence. Its multimodal nature qualifies it as benchmark for DEFAME. Out of the 2, 001 unique claims in the test set, we choose the 1, 689 claims that have a final ruling, useful to assess the quality of generated justifi- cations (Appendix M) That subset includes 667 Refuted, 522 NEI, and 500 Supported claims. We evaluate our performance using eccuracy-eqiivale to micro-F1 (Appendix
VERITE (Papadopoulos et al. 2024b is an image- text verification benchmark focused on Out-Or-Context (OOC) scenarios. After removing 13 incomplete instances, VERITE comprises 1, 001 samples, sourced partly from fact-checking platforms and partly generated by swapping images or altering captions. The dataset includes 338 True, 325 OOC. and 338 Miscaptioned claims (OOC and miscaptioned claims differ in construction but both involve out-of-context imagery). Following Pa- padopoulos et al. (2024a), we report accuracy for True vs. OOC and True vs. Miscaptioned,"" as well as a merged True vs. False" setup.
Importantly, prior work has demonstrated the ability to simultaneously address such diverse tasks as we do here- making DEFAME the most general AFC method as of now Unlike prior methods that are limited to specific subtasks or modalities as OOC detection, text-only verification, or reliance on gold evidence-DEFA ME performs robustly across all benchmarks without task-specific tuning or train- ing data. This benchmark-spanning performance is made possible by DEFAME's 's zero-shot, retrieval-based design, which does not depend on gold-labeled evidence, modality constraints, or dataset-specific preprocessing.
5.2. Model and Configuration
We chose GPT-40 and GPT-40 MINI as the backbone of DEFAME since they are the current state-of-the-art
6

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DEFAME: Dynamic Evidence-basel FAct-checking with Multimodal Experts
Method
AVERITEC
MOCHEG
VERITE T/MC
CR+
Acc
Acc
T/OOC
T/F
Acc
CFR
Sriram et al.
2024
60.0
DEBERTA
Cao et al.
2023
65.6
LVLM4FV
Tahmasebi et al.
2024
45.1
METASUM
(Chen ct al.
2024
48.6
CHASMA
PPaaadopuuoos al.
2024b
74.4
59.3*
52.1
AITR
Papadopoulos et al.
2024a
82.7
51.8
58.0*
GPT-40
62.1 104
53.7 10,
70.4 114
72.0 2088
78.7 10.8
35.2 10.9
GPT-40 COT
61.0 +04
49.7
74.1 #09
76.5 =0.4
80.0 +0.6
31.4 #4.5
DEFAME (Ours)
70.5 20.6
59.2
78.4 110
83.3 =111
83.9 20.5
69.7 12.5
Table 3: Comparison of our method with prior state-of-the-arr methods and baselines across datasets. Best scores are in bold, second- best are underlined. For DEFAME and the GPT-4o baselines, we report the mean over three runs along with the standard deviation. Values marked with * were derived from reported numbers. Blank cells indicate that the respective method could not be evaluated on the corresponding benchmark-eithe due unavailability of publicly released code or incompatibility with the benchmark setup. CR+ CLAIMREVIEW2224:
Predicted
Predicted
Predicted
30
7
46
27
2
13
47
69
10
4
54
75
2
26
36
72
5
113
10
Actual
Adbual
34
3
20
28
0
15
1
3
1
15
2
3
10
(a) GPT-40
(b) GPT-40 CoT
(c) DEFAME
Figure 4: Confusion matrices on the CLAIMREVIEW2024+ dataset for GPT-40, GPT-40 COT, and DEFAME.
The results on our new CLAIMREVIEW dataset chal- lenge the notion that vanilla LLMs are reliable fact-checking systems. While performance on established benchmarks is strong, CLAIMREVVEW22224+ reveals drastic drop for the two GPT-40 baselines, whereas DEFAME maintains accuracy comparable to its performance
Figure exemplarily shows the claim "Slovakian Prime Minister Robert Fico being dragged into a car after being shot." The GPT-40 CoT baseline refutes it due to missing evidence. contrast, DEFAME. retrieves supporting arti- cles from CNN, Vatican News, and Al Jazeera via reverse image and web search, confirming that Fico was indeed shot on May 15, 2024, in Handlová, Slovakia, and subse- quently moved into car. Although the geolocation sug- gested Poland or the Czech Republic, DEFAME correctly marginalized this noise and predicted the correct verdict.
AVERITES This suggests that DEFAME: evidence retrieval mitigates the temporal dependence of its backbone model. The con- fusion matrices (Figure illustrate distinct error patterns across models on the CLAIMREVIEW2024+ dataset. Pure GPT-40 overpredicts NEI but outperforms its Chain- of-Thought counterpart in identifying Refuted claims. GPT-40 CoT also acknowledges the lack of evidence but shows a stronger tendency to commit to O Misleading. In contrast, DEFAME exhibits more balanced confusion matrix, with its primary challenge lying in differentiating O Misleading and Refuted relatively uncritical am- biguity among the possible misclassifications. Its result on the Supported class is far superior the two baselines. Refer Appendix
5.4. Ablation Study
We conduct an ablation study with reduced DEFAME vari- ants to investigate the contributions of various components and capabilities of DEFAME its overall performance:
Tool Ablations: We assess the contribution of each of the four tools by individually removing them from the tool pool.
G for further confusion analysis.

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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Claim
DEFAME INFACT
GPT-4o CoT
DEFAME
HERO
67%
50%
Verdict
Verdict
PAPELO-TEN
"Slowakan Prima Minister Fizo dragged after
Refuted
Supported
UNH
45%
SYMAaSe
FACTCHECNFUSION
Justification
Justification
[-]] There are na credible news sources or reports confiring such incident involvng Robert Flco. The lack of evidence reliable sourbes suggests that the daim false misleading.
[-]] On May 15, 2024, Fico was indeed shot Handicva, Slovakin, subsequenty moved confimed muliple sources cluding @e CNN, @ Valliann News, and @ Al Jazoann, Despite geolocation suggesting the image might be from another country. this does not contradidt the claim, the depicts the aftermath the shouling.
00%
AveniTeC Soore
into
Figure Top 8 (of 21) systems on the AVERITEC Chal- lenge test set, ranked by AVERITEC score.
Figure 5: Example claim from CLAIMREVIEW with verdict and justification by GPT-4o CoT and DEFAME.
TLAMMREVIEW2024:: likely because both contain only or many text-only claims, respectively. Geolocation and Reverse Image Search prove more useful for VERITE, where images are central and can be directly examined.
MOCHEG
VERITE T/F (Acc.)
CR+ Acc.
Model Variant
Acc.
The single-turn variant of DEFAME performs notably worse than the multi-turn version, especially on MOCHEG, underscoring the importance of follow-up retrievals. The De- velop Stage and the integration of visual evidence from tool outputs into the report yield clear performance gain. Fi- nally, removing the Planning Stage leads not only to reduced accuracy but also to significantly higher computational costs (see Appendix C
DEFAME
59.2
83.9
69.7
w/o Geolocation
58.3
80.6
65.7
w/o Reverse Search
58.2
73.7
64.0
Image Search
57.8
81.4
63.7
w/o Web Search
42.0
81.8
59.7
Single Turn
47.7
82.8
63.3
w/o Planning
58.7
83.0
68.0
w/o Develop
57.4
83.8
67.0
Unimodal Develop
56.1
82.0
65.7
5.5. Adaptation to the AVERITEC Challenge
Table 4: Ablation study results for different model variants on the MOCHEG, VERITE and CLAIMREVIEW20244 datasets. True vs. False" accuracy is reported for VERITE. Best scores are marked in bold, second best are underlined.
The AVERITEC authors recently hosted shared task (Schlichtkrull et al., (022a)), where systems competed based the AVERITEC Score (Schliehtkrull al. 2024b). metric that evaluates both claim accuracy and evidence correctness. To participate, adapted our framework to produce question- -nnwer (QA) pairs, as the metric relies on the similarity between generated and reference QA pairs. Our adapted method, submitted under the name INFACT, achieved best performance the AVERITEC Challenge. as shown in Figure 6 (see also Appendix and Rotherme] et al. (2024) for details). Notably, both the original DE- FAME and the adapted INFACT variant achieve comparable accuracy on the development set, demonstrating that our system is not only robust across evaluation protocols but also flexible enough to support output formats required by the downstream tasks.
Single Turn: This version is restricted to pass all stages only once, i.., no ability to delve deeper into findings.
W/o Planning: This variant fixes all actions into a static action schedule. Each tool is executed exactly once, bypassing the dynamic planning stage.
W/o Develop Stage: Is intermediate reasoning useful? This variant excludes the Develop Stage, jumping from evidence retrieval immediately to judgment.
5.6. Explainability Quality and Human Evaluation
Unimodal Develop Stage: textual reasoning suffi- cient? This part-unimodal variant can retrieve evidence images but cannot pass them to future stages.
To further measure the quality of gathered evidence and to assess our framework's vulnerability to hallucinations, we conducted human evaluation. focusing two aspects:
preset4 the results of our ablation study. We observe that DEFAME benefits from all four tools wwith varying contributions: Web Search is critical for MOCHEG and
Coherence: The fact-check maintains a logical and meaningful flow. There are no contradictions or gaps
8

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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Choughol-Thougt
DEFAVE
6. Discussion
Coherence
While DEFAME works with any MLLM backbone, the backbone's 's capabilities directly influence the quality of the resulting fact-check. As MLLMs continue to evolve, DE- FAME'S performance is expected improve correspond- ingly. Moreover, its modular, in-context-learming based design enables DEFAME to incorporate further tools be- yond those demonstrated in this work. Still, DEFAME has several limitations.
Completaness
3
4
Figure 7: Human assessment of Coherence and Complete- ness of DEFAME's s vs. GPT-40 CoT's fact-checking re- ports on 36 claims sampled from three benchmarks.
Credibility of External Evidence: Our reliance on search engines introduces the risk of incorporating unreliable in- formation, as search results may include leaked or biased content (Chrysidis et al. 2024). While some approaches assess source credibility using third-party ratings (Zhou et al. 2024), our framework relies on diversification and the search engine's 's internal trustworthiness checks) Introduc- ing external ratings could strengthen verification rigor.
that disrupt the overall coherence of the report.
Completeness: The verdict is sufficiently justified: he included evidence allows one to derive the verdict.
Participants were asked to rate the fact-checking reports w.r.t the above criteria on a Likert scale from to 5. In total, we collect 185 ratings over 36 claims sampled randomly but balanced from VERITE, MOCHEG, and AVERITEC. Please refer to Appendix D for further details on the exper- iment. The results are shown in Fig. 7 The CoT baseline and DEFAME show no significant difference in output co- herence, i.e., both produce mostly logical reports. This is expected, considering the current state of zero-shot L.LMs in text generation. However, the two differ significantly in their ability to justify their verdicts. The results imply that DEFAME provides better justifications compared to bare MILM prompting. This finding further challenges recent studies (Geng et al.] 2024: Beigi et al.) 2024 Shao et al. 2023 Li et al. 2024e) that suggest MLLMs can perform fact-checking without retrieving external evidence.
System Stability and Sensitivity: Our web scraping pro- cess is prone to instability due to restricted access and large document size. Moreover, open-source models like LLAVA exhibit formatting sensitivity, where minor prompt varia- tions impact response quality. Ensuring robust scraping and prompt formatting stability is crucial for reliable outputs.
Hallucination: We acknowledge that hallucinations are a risk in DEFAME they are inherent to LLMs. However, both qualitative analysis and human evaluation do not indi- cate substantial amounts of hallucinations. Still, we agree that the role of hallucination in DEFAME must be analyzed more closely in future work.
7. Conclusion
5.7. Failure Analysis
We presented DEFAME, comprehensive zero-shot frame- work for multimodal fact-checking that integrates MI.LMMs with external tools to address the limitations of traditional automated fact-checking approaches. Our framework ex- plainably grounds its analysis in verifiable data by com- bining MI...Mdriven reasoning with external multimodal evidence sources, geolocation, reverse image search, and dynamic web searches. We demonstrated that DEFAME achieves new state-of-the-ar art results across multiple diverse benchmarks, including an own leakage-free benchmark, highlighting its generality and capability to navigate unseen real-world claims. While limitations remain, our system represents a significant advancement in the field, providing a versatile foundation for future developments in automated multimodal fact-checking.
We analyzed 119 VERITE and CLAIMREVIEW2024+ in- stances that were mislabeled by DEFAME and identified five common failure modes. (1) Label Ambiguity, the most frequent one, occurs when DEFAME mixes up sim- ilar labels like Refuted and O Misleading. (2) Missed Evidence accounts for errors where DEFAME exhausts all three retrieval attempts but fails to find enough evi- dence. This happens often when evidence is inaccessible (c.g.. when proprietary API access is required) or embedded in a video. (3) Reasoning Errors are mistakes that happen during natural language inference, e.g., mixing up numbers. (4) Premature Judgment occurs when DEFAME com- mits a verdict before gathering sufficient evidence. For example, it predicts O Supported as soon as it finds any image matching the claim text, even if it is not the one in the claim. (5) Wrong Ground Truth: In several cases, our inspection revealed annotation errors, meaning DEFAME'S prediction was actually correct. Appendix shows instances with wrong ground truth labels.
delivers
deLivera-zellable zeliaple informat.on search/
9

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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Impact Statement
The research was partially funded by LOEWE- Start-Professur (LOEWE/4b//519005 0002-(0000)/944)
To scalably debunk the masses of mis- and disinformation- which indisputably pose a substantial threat to social cohesion-autommatin fact-checking is inevitable. Al- though the results do not sufficiently warrant real-world application yet, DEFAME's introduction is step in ad- dressing that challenge.
NDEWE--pitzen-PPofeseee (LOEWE/44//5190)) 19/55.00000000000
(0010)/93) and an Alexander von Humboldt Professor- ship in Multimodal Reliable AI, sponsored by Germany's Federal Ministry for Education and Research.
For compute, we gratefully acknowledge support from the hessian.AIßSeriiee Center (funded by the Federal Ministry of Education and Research, BMBF, grant no. 01IS22091) and the hessian.Al Innovation Lab (funded by the Hessian Ministry for Digital Strategy and Innovation, grant no. S- DIW04/0013/033.
DEFAME contributes to shifting the paradigm toward ex- plainable and verifiable Al systems. Its ability to provide structured, evidence-backed justifications ensures that AFC does not become black-box authority but rather an aug- mentation tool that strengthens public resilience against misinformation. By embedding transparency and iterative verification into its core process, DEFAME helps counter the skepticism toward Al-driven moderation while simulta- neously reducing the burden on human fact-checkers.
References
Abdelnabi, S., Hasan, R., and Fritz, M. Open-domain, content-based, multi-modal fact-checking of out-of- context images via online resources. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14940-14400 14949, 2022.
However, the widespread deployment of such systems also raises ethical and societal concerns. Recently, in public dis- cussions, professional fact-checkers have widely faced accu- sations of being biased (CNN) 2025) and, therefore, unreli- able. Automated fact-checking, even when well-calibrated, can also shape narratives by prioritizing certain sources or by amplifying institutional biases inherent in retrieval mechanisms or parametric knowledge. Current research efforts strive to de-bias L.LSs (Gallegos et al. 22024), which
Akhtar, M., Cocarascu, O., and Simperl, E. Reading and reasoning over chart images for evidence-based automated fact-checking. In Vlachos, A. and Au- genstein, I. (eds.), Findings of the Association for Computational Linguistics: FACI. 2023, pp. 399-414, Dubrovnik, Croatia, May 2023a. Association for Compu- tational Linguistics. doi: 10.18653w1122223mmminnn.... 30. URL httpp::/accaaatthoooo.ooos/ Findings-each 30
DEFAME will automatically benefit from thanks to its backbone-agnostic design. Within the strongly polarized public discussions, AFC systems may emerge as nonparti- san information source. Their reliability inherently depends on the quality of the backbone, which difficult to rigor- ously analyze.
Akhtar, M., Schlichtkrull, M., Guo, Z., Cocarascu, O., Sim- perl, E., and Vlachos, A. Multimodal Automated Fact- Checking: A Survey. In The 2023 Conference on Empiri- cal Methods in Natural Language Processing (EMNLP): Findings, 2023b. URL http://arxiv.org /bs/
There is also risk that governments platforms might misuse systems like DEFAME for overreach in content moderation, potentially stifling dissent or critical journal- ism under the guise of misinformation control. To mitigate this, it is crucial to ensure accountability, source diversity, users to challenge and scruti- nize automated decisions. Additionally, while DEFAME ME may improve trust in digital information, its success will ultimately depend on how it is integrated into broader fact- checking ecosystems and content moderation, where human oversight remains essential. The current debate strikingly shows that fact-checking (especially of social media con- tent) sits on the verge between public safety and perceived censorship of free speech. Therefore, the real impact of DEFAME lies not just in its technical contributions but in how humans will ultimately use it.
and contestability-allowe
Akhtar, M., Subedi, N., Gupta, V., Tahmasebi, S., Co- caraseu, O., and Simperl, E. Chartcheck: Explainable fact-checking over real-world chart images. In Findings of the Association for Computational Linguisties ACL 2024, pp. 13921-13937, 2024.
Alibaba-NLP.
Gte base en v1.5, 2024. URL
https::/nugginggaaee co A1 lbabb- hhe/gte-base-ee
NLP/ NLP/gte basbbaaaeeeee en en-se-ee-vl... v1.5 Accessed: 2024-08-14,
Aneja, S., Bregler, C., and Nießner, M. Cosmos: Catch- ing out-of-context misinformation with self-supervised learning. arXiv preprint 100.::20000.027.... 2021.
Acknowledgments
Atanasova, P., Simonsen, J. G., Lioma, C., and Augen- stein, I. Generating fact checking explanations. In Ju- rafsky, D., Chai, J., Schluter, N., and Tetreault, J. (eds.),
We thank Marcus Kornmann for his diligent assistance in conducting this research.
10

---

DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Proceedings of the 58th Annual Meeting of the Associa- tion for Computational Linguistics, pp. 7352-7364, On- line, July 2020. Association for Computational Linguis- tics. doi: 10. (0.186633/1/20222acllmm. ma6..656. https: 1/aclanthie www.y.orgg/20.... ac1 ma in. 656
CNN.
Fact-checkers, targeted by MAGA loyalists, blast Zuckerberg's is assertion their work 'biased',
2025. URL https///edition.cmn..
2025/
zuckerberg-
Accessed: Jun 04,
2025.
Beigi, A., Jiang, B., Li, D., Kumarage, T., Tan, Z., Shaeri, P., and Liu, H. Lrq-fact: Lim-generated relevant ques- tions for multimodal fact-checking. arXiv preprint arXiv:2410.04616 2024.
Davis, E. Benchmarks for automated commonsense soning: A survey. ACM Compul. Surv,, 56(4), October 2023. ISSN 0360-0300. doi: 10. 10.1145/3615355. URL
ht
Cao, H., Wei, I.., Chen, M., Zhou, W., and Hu, S. Are large language models good fact checkers: A preliminary study. arXiv preprint arXiv:2311.17383 2023.
Dmonte, A., Oruche, R., Zampieri, M., Calyam, P., and Au- genstein, I. Claim verification in the age of large language models: A survey. arXiv preprint arXiv:2408.14333 14317, 2024.
Cao, H., Wei, L., Zhou, W., and Hu, S. Multi-source knowl- edge enhanced graph attention networks for multimodal fact verification. In 2024 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6. IEEE, 2024.
Dufour, N., Pathak, A., Samangouci, P., Hariri, N., Deshetti, S., Dudfield, A., Guess, C., Escayola, H., Tran, B., Babakar, M., et al. Ammeba: A large-scale survey and dataset of media-based misinformation in-the-wild. arXiv preprint arXiv:2405.
Cepeda, V. V., Nayak, G. K., and Shah, M. GcoCLIP: Clip-inspired alignment between locations and images for effective worldwide rco-localization. In seventh Conference on Neural Information Processing Systems, 2023. URL httpttts//ppeerreteteee wttpi//wwwee/ffooreeeeee Id=I18BXotQ77
2024.
Fu, Z., Wang. K., Xin, W., Zhou, L., Chen, S., Ge, Y., Janies, D., and Zhang, D. Detecting misinformation in multime- dia content through cross-modal entity consistency: A dual learning approach. wwXiv2240900002 2024.
Chen, C. and Shu, K. Can LLM-generated misinformation be detected? In The Twelfth International Conference on Learning Representations, 2024. URL httpp::7 http://wwnee//rrumddd-ccccmm
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, K. Bias and fairness in large language models: survey. Computational Linguistics. 1097):1097-,179, 09
2024. ISSN 0891-2017, doi: 10.1166/coli URL coli 00524
Chen, T.-C., Tang, C.-W., and Thomas, C. MetaSumPer- ceiver: Multimodal multi-document evidence summariza- tion for fact-checking. In Ku, L.-W., Martins, A., and Srikumar, V. (eds.), Proceedings of the 62nd Annual Meet- ing of the Association for Computational Linguistics (Vol- ume 1: Long Papers). pp. 8742-8757, Bangkok, Thailand, August 2024. Association for Computational Linguis- URL https: 7/acl 7/aclantho antho wwwethoooogy...oo ogg. ac1 1aal--oongg ong 474]
attps://doi.org/
Geng, J., Kementchedjhiiva, Y., Nakov, P., and Gurevych, I.
Multimodal large language models to support real-world fact-checking. arXiv preprint wxXv:2243.003627 2024.
Glockner, M., Staliunaité, I., Thome,, J., Vallejo, G., Vla- chos, A., and Gurevych, I. AmbiFC: Fact-checking ambiguous claims with evidence. Transactions of the Association for Computational Linguistics, 12:1-18, 2024. doi: 1 00629. URL https:// aclanthology.oom wwt.:/o0oy.r.g/222..... wtt../00.222..11....... tacl-1.1
tics. doi: 10. 10.18653/w1/20244cclloooo
Cheung. T.-H. and Lam, K.-M. Factllama: Optimiz- ing instraction-followiing language models with external knowledge for automated fact-checking. In 2023 Asia Pucific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 846- 853. IEFE, 2023.
Greifeneder, R., Jaffe, M., Newman, E.,, and Schwarz, N.
The Psychology of Fake News: Accepting, Sharing, and Correcting Misinformation. Routledge, edition, 2021. ISBN 978-0-429-299377.9 doi: 10.4324/9788429295379
Chrysidis, Z., Papadopoulos, S.-I., Papadopoulos, S., and Petrantonakis, P. Credible, unreliable or leaked?: Evi- dence verification for enhanced automated fhec-ch, In Proceedings of the 3rd ACM International Workshop on Multimedia Al against Disinformation, pp. 73-81, 2024.
Hameleers, M., Powell, T. E., Van Der Meer, T. G. L. A., and Bos, L. A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Political Communication, 37, 2020. doi: 10. 00.10800/08844692219999
URL. https

---

DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Li, B., Zhang, Y., Guo, D., Zhang, R., Li, F,, Zhang,
a27
H., Zhang, K., Li, Y., Liu, Z., and Li, C. Llava- onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326 2024a.
Hassan, N., Li, C.. and Tremayne, M. Detect- ing Check-worthy Factual Claims in Presidential De- bates. Proceedings of the 24th ACM International on Conference on Information and Knowledge Manage- ment, pp. 1835-1838, 2015. doi: 10.1114/2806416. 2806652. URL 1145/280 6. 2806652
Li, C. and Flanigan, J. Task contamination: Language models may not be few-shot anymore. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16): 18471-18480, Mar. 2024. doi: 10.1609/aaai.v38i16
29808. URL ht / /ttt:::://jjs.aae orgg index php/ AAAI AAAA/aattiiooooaaa htw.A//aaticeeeeeeeeeee.
Hassan, N., Zhang, G., Arslan, F., Caraballo, J., Jimenez, D., Gawsane, S., Hasan, S., Joseph, M., Kulkarni, A., Nayak, A. K., Sable, V., Li, C., and Tremayne, M. Claimbuster: the first-ever end-to-end fact-checking Au- gust 2017. ISSN 2150-8097, doi: 10.14778/313775. wttp:////oo....1111110
Li, G., Lu, w., Zhang, W., Lian, D., Lu, K., Mao, R., Shu, K., and Liao, H. Re-search for the truth: Multi-round eetrieaal-augmene large language models are strong fake news detectors. arXiv preprint arX4472403.09747, 2024b.
system. Proc. VLDB Endow.,
3137815. URL
3137765. 3137815
Li, M., Peng, B., Galley, M., Gao, J., and Zhang, Z. Self- checker: Plug-and-play modules for fact-checking with large language models. In Duh, K., Gomez, H., and Bethard, S. (eds.), Findings of the Association for Com- putational Linguistics: NAACL 2024, pp. 163-181, Mex- ico City, Mexico, June 2024c. Association for Computa- tional Linguistics. doi: 10. (0..86633//222444nnnnnnggg.. 12. URL htt.ttpss//ccaaatoooooooooo///// Findings nww.s-aaac... 12/
Jaiswal, A., Sabir, E., AbdAlmageed, W., and Natarajan, P. Multimedia semantic integrity assessment using joint embedding of images and text. In Proceedings of the 25th ACM International Conference on Multimedia, MM '17, pp. 1465-1471, New York, NY, USA, 2017. Associa- tion for Computing Machinery. ISBN 9781450349062. URL lattps://dct
doi: 10. 10.11455/123266.31233333 org/ 11211111115550 311123366666666388888883 3123385
Li, T., Li, Z., and Zhang, Y. Improving faithfulness of large language models in summarization via sliding generation and sonficonnist In Calzolari, N., Kan, M.-Y., Hoste, V., Lenei, A., Sakti, S., and Xue, N. (eds.), Proceed- ings of the 2024 Joint International Conference on Com- putational Linguistics, Language Resources and Evalu- ation (LREC-COLIN)
Jia, S., Lyu, R., Zhao, K., Chen, Y., Yan, Z., Ju, Y., Hu, C., Li, X., Wu, B., and Lyu, S. Can chatgpt detect deepfakes? study of using multimodal large language models for media forensics. In Proceedings of the IEEE/VVF Con- ference on Computer Vision and Pattern Recognition, pp. 4324 4333, 2024.
2024), pp. 8804-8811 17, Torino, Italia, May 2024d. ELRA and ICCL. URL https weaclannhooogy...o wwg.yy..rg//222 Brer-eemm -ma mnaain.. 771
Khaliq. M. A., Chang, P. Y.-C., Ma, M., Pflugfelder, B., and Miletić, F. RAGAR, your falsehood radar: RAG- augmented reasoning for political fact-checking using multimodal large language models. In Schlichtkrull, M., Chen, Y., Whitchouse, C., Deng, Z., Akhtar, M., Aly, R., Guo, Z., Christodoulopoulos, C., Cocarascu, O., Mittal, A., Thorne, J., and Vlachos, A. (eds.), Proceed- ings of the Seventh Fact Extraction and VERification Workshop (FEVER), pp. 280-296, Miami, Florida, USA, November 2024. Association for Computational Linguis- wrw.tthhoolooy...o//000
Li, X., Zhang, Y., and Malthouse, E. C. Large language model agent for fake news detection. arXiv preprint wrXiv:2405.01593, 2024e.
Li, Y. and Xie, Y. Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement. Journal of Marketing Research, 57(1):1-19, 2020.
ISSN 0022-2437,
tics. URL
1547-7193. doi: 10. 10.1177000224311988111
URL
Eever- 1.29
htt:::/ wttpsrraaaasss..psu.saaeaa 1177/0022247118888 wwtpsaab.c......p.e/ooo/eaasseee com//oi/10..
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, genera-
Liu, Xi, Li, Z., Li, P., Xia, S., Cui, X., Huang, L., Huang. H., Deng, W., and He, Z. MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs. In The Thirteenth International Conference on Learning Representations, 2025. URL https:// 8 arxiv.org/abee www.:ooggbbbs/...o2 08772
T., Riedel, S., and Kiela, D. Retrieval-augmentt inteeevveee nlp tasks. In Proceedings
tion for of the 34th International Conference on Neural Informa- tion Processing Systems, NIPS '20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546
12

---

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Luo, G., Darrell, T., and Rohrbach, A. NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media. In Moens, M.-F., Huang, X., Specia, L., and Yih, S. Wt-t. (eds.), Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pp. 6801-6817, Online and Punta Cana, Domini- can Republic, November 202 a. Association for Com- putational Linguistics. doi: 10.18653w m4in.545. URL
H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., and Piperidis, S. (eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference, Pp. 6149-61611 6157, Marseille, France, May 2020. European Language Resources Association. ISBN 979-10-95546- wrtpl//ggg.rg///0/
34-4. URL
Irec 1. 755
Newman, E. J., Garry, M., Bernstein, D. M., Kant- ner, J., and Lindsay, D. S. Nonprobative pho- tographs (or words) inflate truthiness. Psychonomic Bulletin & Review, 19(5):969 9744,4 2012. ISSN 1069-9384, 1531-5320. doi: 10.3758//13423-012 0292-0. URL htttttt/i/ikk..... spr.nkr..pringgrr...mmm com/10 3758/ s 3423-3422-22220
2021 emn 1p maim....... 545
Luo, G., Darrell, T., and Rohrbach, A. NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media. Moens, M.-F., Huang. X., Specia, L.., and Yih, S. W.-t (eds.), Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pp. 6801-6817, Online and Punta Cana, Domini- can Republic, November 2021b. Association for Com- putational Linguistics. doi: 10. 10.18663/w1/0001eemmmp main.545. URL ltttp:://eeaanthoooog.. 2021 www.pmmmmi...memmmmmmmmmmmmm main.
Nielsen, D. S. and McConville, R. Mumin: A large-scale multilingual multimodal fact-checked misinformation so- cial network dataset. In Proceedings of the 45th interna- tional ACM SIGIR conference on research and develop- ment in information retrieval, pp. 3141-3153, 2022.
Maynez, J., Narayan, S., Bohnet, B., and McDonald, R. On faithfulness and factuality in abstractive summa- rization. In Jurafsky. D., Chai, J., Schluter, N., and Tetreault, J. (eds.), Proceedings of the 58th Anmual Meet- ing of the Association for Computational Linguistics, pp- 1906-1919, Online, July 2020. Association for Computa- tional Linguistics. doi: 10. /001:16653////0000ammmmmmm 173. URL
Pan, L., Wu, X., Lu, X., Luu, A. T., Wang, W. Y., Kan, M.-Y, and Nakov, P. Fact-checking complex claims with program program-guided reasoning. In Rogers, A., Boyd- Graber, J., and Okazaki. N. (eds.), Proceedings the the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pupers), pp. 6981-7004, Toronto, Canada, July 2023. Association for Computa- tional Linguistics. doi: 10. 10.186633//2023.aclloog...
17in.173
URL htttps///aclaa.hhooooooor/////// long 386
Meta AI, 2025. htt.ttssss//iim.eetaa http:://aaa.mettaaaommoo//og/// 1lama- 4- mult www.ttimmma-iiteeiiieee
Ac-
Papadopoulos, S.-I., Koutlis, C., Papadopoulos, S., and Petrantonakis, P. C. Red-dot: Multimodal fact- checking via relevant evidence detection. arXiv preprint arXiv:231 1.09939, 2023.
cessed: May 28, 2025.
Moreno Gil, V., Ramon, X., and rodriguez martinez, R. Fact-checking interventions as counteroffensives to dis- information growth: Standards, values, and practices in latin america and spain. Media and Communication, 9: 251-263, 03 2021. doi: 10.17645/mac. wwt.tmaaa.vv....... v911.3443.
Papadopoulos, S.-.,, Koutlis, C., Papadopoulos, S., and Pe- trantonakis, P. C. Similarity over factuality: Are we making progress on multimodal out-of-context misinfor- mation detection? arXiv preprint 14X1:22477.13888, 2024a.
Müller-Budcck, E., Theiner, J., Diering, S., Idahl, M., and Ewerth, R. Multimodal analytics for real-world news using measures of cross-modal entity consistency. In Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR '20, pp. 16-25, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450370875.
Papadopoulos, S.-L., Koutlis, C., Papadopoulos, S., and Petrantonakis, P. C. Verite: a robust benchmark for multi- modal misinformation detection accounting for unimodal bias. International Journal of Multimedia Information Retrieval, 13(1):4, 2024b.
doi: 145/ URL Inttps://dot org/10
3372278.3390670. 1145/3372278.1 .3906670
Qi, P., Yan, Z., Hsu, W., and Lee, M. L. SNIFFER: Mul- timodal Large Language Model for Explainable Out-of- Context Misinformation Detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. -3062, 2024.
Nakamura, K., Levy, S., and Wang, W. Y. Fakeddit: A new multimodal benchmark dataset for fine finennrained fake news detection. In Calzolari, N.. Béchet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara,
13

---

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Roberts, J., Lüddecke, T., Sheikh, R., Han, K., and Albanie, S. Charting new territories: Exploring the geographic and geospatial capabilittes of multimodal Ilms. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 554-563, 2024.
Shao, R., Wu, T., and Liu, Z. Detecting and grounding multi-modal media manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6904-6913 091,,913,222 2023.
doi: 10.1 1.111//CVYRW63322222200000
Sriram, A., Xu, F., Choi, E., and Durrett, G. Contrastive learning to improve retrieval for real-world fact checking. In Schlichtkrull, M., Chen, Y., Whitehouse, C., Deng, Z., Akhtar, M., Aly, R., Guo, Z.., Christodoulopoulos, C., Cocarascu, O., Mittal, A., Thorne, J., and Vlachos, A. (eds.), Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pp. 264-279, Miami, Florida, USA, November 2024. Association for Compu- tational Linguistics. URL (tKl[ttts//aclaathhol.gg org f.v.r....ee.l... 1.28
Rothermel, M., Braun, T., Rohrbach, M., and Rohrbach, A. InFact: A strong baseline for automated fact-checking. In Schlichtkrull, M., Chen, Y., Whitehouse, C., Deng, Z., Akhtar, M., Aly, R., Guo, Z., Christodoullpoullss, C.,, Cocarascu, O., Mittal, A., Thome, J., and Vlachos, A. (eds.). Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pp- 108- 1 12, Miami, Florida, USA, November 2024. Association for Com- putational Linguistics. doi: 10. 10.18653/w1/2222ffeeer 1.12. URL fever-1.121
Surís, D., Menon, S., and Vondrick, C. Vipergpt: Visual inference via python execution for reasoning. In Proceed- ings of the IEEE/VF International Conference on Com- puter Vision (ICCV), pp. 11888-11898, October 2023.
Sabir, E., AbdAlmageed, W., Wu, Y., and Natarajan, P. Deep multimodal image-repurposing detection. In Proceedings of the 26th ACM International Confer- ence on Multimedia, MM '18, pp. 1337-1345, New York, NY, USA, 2018. Association for Computing Machinery. ISBN 9781450356657. URL
Tahmasebi, S., Müller-Budack, E., and Ewerth, R. Mul- timodal misinformation detection using large vision- language models. In Proceedings of the 33rd ACM International Conference on Information and Knowl- edge Management, CIKM '24, pp. 2189-2199, New York, NY, USA, 2024. Association for Computing Machinery. ISBN 9798400704369. doi: 10.1145/ 3627673.3679822 URL https://doi.org//
doi: 145/ https::/doo..rggg/
3240508.3240707. 1145/324050 0000000008324477777 3240707
Samarinas, C., Hsu, W., and Lee, M. L. Improving evi- dence retrieval for automated explainable fact-checking. In Sil, A. and Lin, X. V. (eds.), Proceedings of the 2021 Conference of the North American Chapter of the Associ- ation for Computational Linguistics: Human Language Technologies: Demonstrations, pp. 84-91, Online, June 2021. Association for Computational Linguistics. doi:
Tang, C.-W., Chen, T.-C., Nguyen, K. A.. Mchrab, K. S., Ishmam, A. M., and Thomas, C. M3D: MultiModal MultiDocument fine-grained inconsistency detection. In Al-Onaizan, Y., Bansal, M., and Chen. Y.-N. (eds.), Pro- ceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 12270-22299 Miami, Florida, USA, November 2024. Association for Com- putational Linguistics. doi: 10. 10.118653/111222.mmmm main. 1243. URL http:h/ssss/cccaattoooooo 2024 www maammppmminn111 1243/
http://2211.aaal-lmmmoooo 10. URL https:// 10.1 00111653/v1/200 wttpo///////....e nwacc -oaccl-demmo demos. 10 acl
Schlichtkrull, M., Chen, Y., Whitehouse, C., Deng, Z., Akhtar, M., Aly, R., Guo, Z., Christodoulopoulos, C., Cocarascu, O., Mittal, A., Thorne, J., and Vla- chos, A. The automated verification of textual claims (AVeriTeC) shared task. In Schlichtkrull, M., Chen, Y., Whitchouse, C. Deng, Z., Akhtar, M., Aly, R., Guo, Z., Christodoulopoullss, C., Cocarascu, O., Mit- tal. A., Thome, J., and Vlachos, A. (eds.), Proceed- ings of the Seventh Fact Extraction and VERification Workshop (FEVER), pp. 1-26, Miami, Florida, USA, November 2024a. Association for Computational Linguis- tics. URL https://aaatt http://ccanntooooygyooooo oorttholooy.org/2222 Fever-1.1
Thome, J., Vlachos, A., Christodoulopoulos, C., and Mit- tal, A. FEVER: a large-scale dataset for fact extrac- tion and VERification. In Walker, M., Ji, H., and Stent, A. (eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume I (Long Papers), pp. 809-819, New Or- leans, Louisiana, June 2018. Association for Computa- tional Linguistics. doi: 10. 10.11865//1//18-10000 URL https://aclanthhrn
org//024
NN8 1074
Schlichtkrull, M., Guo, Z., and Vlachos, A. Averitec: A dataset for real-world claim verification with evidence from the web. In Advances in Neural Information Pro- cessing Systems, volume 36, 2024b.
Tonglet, J., Moens, M.-F., and Gurevych, I. "Image, tell me your story!" Predicting the original meta- context of visual misinformation. In Proceedings of the
14

---

DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
2024 Conference on Empirical Methods in Natural Lan- guage Processing, pp. 7845-7864, Miami, Florida, USA, November 2024, Association for Computational Linguis- tics. URL https:://///.. http://////ccaaaoooooooooooo
WEF TGF__hee_s Tame__Glbball Gwl.aallloossaaaae Page 18.
pdf
Xu, Q., Du, H., Chen, H., Liu, B., and Yu, X. Mmooe: A multimodal misinformation dataset for out-of-context news analysis. In Australasian Conference on Informa- tion Security and Privacy, pp. 444-459, Springer, 2024.
emn 1 p-ma in. 448
Vo-Hoang, H.-V., Pham, L..K.,, and Dao, M.-S. Detecting out-of-context media with Ilama-adapte v2 and roberta: An effective method for cheapfakes detection. In Proceed- ings of the 2024 International Conference on Multimedia Retrieval, pp. 1287,-287, 2024.
Yang, J. and Rocha, A. Take it easy: Label- -abel-adaptive self- rationalization for fact verification and explanation gener- ation. arXiv preprint arXiv:2410.04000
2024.
Vykopal, I., Pikuliak, M., Ostermann, S., and Simko, M. Generative large language models in automated fact- checking: A survey. arXiv preprint arXiv:2407.02351, 2024.
Yao, B. M., Shah, A., Sun, L.,, Cho, J.-H., and Huang, L. End-to-end multimodal fact-checking and expla- nation generation: A challenging dataset and mod- els. In Proceedings of the 46th International ACM SI- GIR Conference on Research and Development in In- formation Retrieval, SIGIR '23, pp. 2733-2743, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9781450394086. doi: 10.1145/ 3539618.3591879. URL https://doi. org/10 1 1145/333918. 3591879
Wang, W. Y. "liar, liar pants on fire": A new bench- mark dataset for fake news detection. In Barzilay, R. and Kan, M.-Y. (eds.), Proceedings of the 55th An- nual Meeting of the Association for Computational Lin- guistics (Volume 2: Short Papers), pp. 422-426, Van- couver, Canada, July 2017. Association for Computa- tional Linguistics. doi: 10.18655//1//11-22267. URL https: / /acl paachhlloo..org/PP1/.
Zannettou, S., Caulfield, T., Blackbumn J., De Cristofaro, E., Sirivianos, M., Stringhini, G., and Suarez- Suarez-Tangil, G. On the Origins of Memes by Means of Fringe Web Commu- nities, 2018.
Wang, Y. Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., and Gao, J. Eann: Event adversarial neural networks for fake news detection. In Proceedings of the 24th aem sigkdd international conference on knowledge discovery & data mining, pp. 849-857, 2018
12512
Zeng, A., Attarian, M., brian ichter, Choromanski, K. M., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M. S., Sindhwani, V., Lee, J., Vanhoucke, V., and Flo- rence, P. Socratic models: Composing zero-shot mul- timodal reasoning with language. In The Eleventh In- ternational Conference on Learning Representations, net/
Wang, Y., Tahmasbi, F., Blackburn, J., Bradlyn, B., De Cristofaro, E., Magerman, D., Zannettou, S., and Stringhini, G. Understanding the Use of Fauxtography on Social Media, 2020. URL http::
2023. URL httpthttt/////peeeeee Gd-2222M3avoo
abs/2009. 11792
Wang, Y., Gangi Reddy, R., Mujahid, Z. M., Arora, A., Rubashevskii, A., Geng, J., Mohammed Afzal, O., Pan, L., Borenstein, N., Pillai, A., Augenstein, I., Gurevych, I., and Nakov, P. Factcheck-bench: Fine-grained eval- uation benchmark for automatic fact-checkers. In Al- Onaizan, Y., Bansal, M., and Chen, Y.-N. (eds.), Find- ings of the Association for Computational Linguistics: EMNLP 2024, pp. 11990,14230, Miami, Florida, USA, November 2024a. Association for Computational Linguis- tics. URL https:ttpp.. / / mantollogy.org/// Eindings emm 830
Zeng, F., Li, W., Gao, W., and Pang, Y. Multimodal misinformation detection by learning from synthetic data with multimodal LLMs. In Al-Onaizan, Y.. Bansal, M., and Chen, Y.-N. (eds.), Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 10467-10484, Miami, Florida, USA, Novem- ber 2024a. Association for Computational Linguis- tics. URL Findings-emnlp..r 513
Zeng, F., Li, W., Gao, W., and Pang, Y. Mul- timodal misinformation detection by learning from synthetic data with multimodal LLMs. In Al- Onaizan, Y., Bansal, M., and Chen, Y.-N. (eds.), Find- ings of the Association for Computational Linguis- tics: EMNLP 2024, pp. 10467-10484, Miami, Florida, USA, November 2024b. Association for Computational Linguistics. doi: 10.18653/v h/t.l1..202222112 findings-emmllp
Wang, Y., Wang, M., Iqbal, H., Georgiev, G., Geng, J., and Nakov, Openfactcheck: A unified framework for factu- ality evaluation of Ils. arXiv preprint arXiv:2405.05588. 2024b.
World Economic Forum. The global risks report 2024, 2024. URL https: //ww33. weforumm wwwp/org/..o..oooo..oo /
15

---

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
613. URL https://act Findings-emn p. 613/
Zhang, X. and Gao, W. Towards LLM-based fact ver- ification on news claims with a hierarchical step-by- step prompting method. In Park, J. C., Arase, Y., Hu, B., Lu, W., Wijaya, D., Purwarianti, A., and Kris- nadhi, A. A. (eds.), Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Pupers), pp. 996-1011, Nusa Dua, Bali, Novem- ber 2023. Association for Computational Linguistics. doi: 10.116655h112033..jjnnmmmmmif. Utthttss::/ ablaattaloooy.orr//2033...10.. www.eenlppmmmi.66mmmm
Zhao, X., Wang, L., Wang, Z., Cheng, H., Zhang, R., and Wong, K.-F. PACAR: Automated fact-checking with planning and customized action reasoning using large language models. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenic, A., Sakti, S., and Xue, N. (eds)), Proceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalua- tion (LREC-COLING 2024), pp. 12564-12573, Torino, Italia, May 2024. ELRA and ICCL. URL https:// 1rec main 1099
acl selanthology. org
Zhou, X., Sharma, A., Zhang, A. X., and Althoff, T. Correct- ing misinformation on social media with a large language model. arXiv preprint arXiv:2403.I 1699, 2024.
Zlatkova, D., Nakov, P., and Koychev, I. Fact-checking meets fauxtography: Verifying claims about images. In Inui, K., Jiang, J., Ng, V., and Wan, X. (eds.), Pro- ceedings of the 2019 Conference on Empirical Meth- ods in Natural Language Processing and the 9th In- ternational Joint Conference Natural Language Pro- cessing (EMNLP-IJCNLP), pp. 2099-2108, Hong Kong, China, November 2019. Associition for Computational Linguisties. doi: 10. 10.18653////DD19-2216 URL ht hts //aclantholog... //aalaantthologg.oorr//111-11111
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Appendix
B. C1AIMREVIEW2024+ Class Definitions
The appendix provides an in-depth extension of the main pa- per, showcasing additional examples, analyses, and details that complement the findings discussed. The data leakage problem of existing AFC benchmarks is discussed in more detail in Section A In Section B the definitions of the four CLAIMREVIEW 2024+ classes are presented. We include information on compute and cost in Section Section D outlines excluded domains to ensure a fair and realistic evaluation setting. Details about our adaptations for the AVERITEC challenge are given in Section B To enhance clarity, we formalize the iterative structure and processing stages of DEFAME in Appendix + A short analysis of the confusions committed by the CoT variant and DEFAME is shown in Section G Section includes the prompts used in DEFAME. Section|] I Lidetifiess wrongly annotated samples in the VERITE dataset. Section ! provides a high-level derivation showing the equivalence of micro-F1 and accu- racy in single-label multiclass settings, clarifying common source of confusion in prior evaluations. Section K discusses cases with outdated ground truth in the MOCHEG dataset and their implications. Section D |seecifies our human evalu- ation of the fact-checking report. In Section M we analyze the justifications generated by DEFAME and scrutinize cur- rent evaluation metrics. Section NiiioNilllusssss Nillustrates example fact- checking reports produced by DEFAME. Lastly, Section [ delves into failure cases, highlighting common pitfalls and their underlying causes.
CLAIMREVIEW2024+ uses four classes, defined as follows:
"A claim is considered refuted when the evidence contradicts the claim."
Refuted:
Supported: "The claim is accurate based on evi- dence."
"The claim is misleading or requires additional context."
Misleading:
"The claim does not have enough information to be verified."
NEI:
For all datasets, the class definitions were provided to DE- FAME at the judgment stage.
C. Details on Compute and Cost
As the GPT models are available only via OpenAl's API, most of our computation happens externally. On our end, we employed four NVIDIA A100-80GB GPUs in order to execute LLAVA- V and GEOCLIP. All other processing was performed on 32 AMD EPYC 7313 16-core CPUs. Since fact-checks on VERITE claims are multimodal from the start, we chose VERITE for the setting of multimodal AFC. We thus report usage statistics on VERITE in Table
representative surrogate
A. The Data Leakage Issue
As expected, the naive baselines (Pure GPT-40 and CoT) require the least amount of resources. But, as discussed in Section 5 this due to the absence of any external evidence and comes at the cost of lower accuracy. DEFAME with GPT-40 MINI and LLAVA- IV the backbone MLLM runs faster and cheaper than DEFAME with GPT-40, but yields decreased accuracy as well. Surprisingly. LLAVA- V fact-checks faster than the GPT models. We found the reason to lie in LLAVA- V's inability to correctly format the proposed actions, yielding a smaller number of executed actions and, thus, shorter fact-checks overall. LLAMA SCOUT masters action formatting and is cheaper than the drastically more
Most AFC benchmarks, particularly AVERITEC, VERITE, and MOCHEG, are (largely) composed of real-world claims sourced from actual fact-checking articles the web. The covered time spans vary:
AVERITEC claims stem from the time until December 2021.
VERITE covers claims between January 2001 and January 2023.
GPT-40 models sut-surprisnngly--tae time. We have not taken any acceleration measures beyond basic LLM installation, which may leave room for time improvement.
MOCHEG contains claims up until May 2022.
Importantly, all time ranges lie before GPT-40's knowl- edge cutoff date, which is October 2023. Since the claims are publicly fact-checked, it is highly likely that GPT-40 was pretrained on large portions of the claims and verdicts contained in these benchmarks, a matter of data leakage. Therefore, we introduced CLAIMREVIEW2024+ which consists of claims, the fact-checks of which were published only after the knowledge cutoff date.
Compared to human fact-checking experts- who invest about an entire working day debunk claim and write a corresponding article (Hassan et al. 2015) DEFAME's fee of $0.13 per claim is cheap. However, DEFAME'S out- put quality does not match human fact-checkers yet. Thus, DEFAME could serve as a cost-effective assistant to aid human fact-checkers. Theoretically, social media platforms could also use DEFAME to cheaply mass-check larger
who
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DEFAME: Dynamic Evidence-basee based FAct-checking with Multimodal Experts
LLM API Cost
Search API Cost
# Search Queries
Searches Cllim
# Imput Tokens
Tokens / Claim
#utput Tokens
Tokens Claim
min/Cluin Claim
Processed.
Generated
/ Claim
e Claim
Time
Method
GPT-40 GPT-40 MINI LLAVA-1V
4:14
13
A A
2.2
48K
921
3.1
54K
1300
DEFAME
1:23
0.9
14K
794
LLAMA
18:02
<1
2.8
21K
1325
w/o Geolocation
3:12
11
<1
2.3
40K
910
w/ Reverse Search
2:37
8
2.3
28K
828
w/o Image Scarch
2:57
X1
2.2
40K
909
DEFAME Ablations
w/o Web Search
2:30
2.2
37K
761
Single Tum
2:47
10
<1
2.0
38K
858
w/o Planning w/o Develop Unimodal Develop
5:11
14
5.2
70K
1100
1:56
42K
710
3:15
10
27
2.3
41K
865
GPT-40
0:06
2.2K
Baselines
GPT-4o CoT
0:09
0.0
2.3K
177
Table 5: Usage and computational cost statisties of DEFAME and considered variants (ablations and baselines) on VERITE. Time values rough estimates and are subject to high variance due to hardware usage by other processes.
amounts of claims posted online. However, depending social media, arguably the number of day-DEFAME could become expen- exceed millions sive. Claim filtering approaches would be needed to narrow down the claims to the most -oothy ones in order scale DEFAME up to larger amounts.
Excluded Fact- Fact-Checking URLs
snopes.com
D. Excluded Domains
politifact.com
factcheck.org
To maintain a realistic and fair setting, we exclude all major fact-checking organizations we know from all web search results. Table showw the corresponding list domains. Additionally, several platforms forbid (direct) automatic access to their web pages, cf. Table 7 Any URL with sub-string matching the domains and URLs in the Tables mentioned earlier is removed from the search results and, hence, ignored by the fact-check.
truthorfiction.com
fullfact.org
leadstories.co
hoax-slayer.net
checkyourfact.com
reuters.com//fattcde
reuters.com/artin cuuters.com/arrole//cccteeeee
apnews. wwtp:/sscoo/APPaCCCCCaaa
factcheck.afp.com
poynter.org
factchock, go
vishvasnews.com
E. Adaptation to the AVERITEC Challenge
boomlive.in
altnews.in
The AVERITEC challenge evaluates fact-checking quality using the AVERITEC Score that compares model-generated question-answer (QA) pairs with gold QA pairs provided in the benchmark (Schlichtkrull et al. 2024b). To perform well, method must effectively identify and address the same (or very similar) questions and answers posed by the benchmark annotators.
thequint.comen wwtt://s///www/ooooooooooccco
factcheck.kz
Table 6: List of excluded URLs to maintain a fair and realis- tie fact-checking setting.
To align with this evaluation, we introduce an extension called INFACT. This adaptation begins by generating 10 key
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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Unsupported Domains
1. Planning (S1): Select actions A A based on the current report R(
facebook.com com
twitter.com
2. Execution (S2): Retrieve evidence :
:= (T(a)
x.com
instagram.com
A}, where executes the tool action a.
youtube.com
tiktok.com
3. Summarization (Sa): R( a (FE, R()), where summarizes E' in context and appends it to the report.
reddit.com
ebay.com
microsoft.com
4. Develop (S.): R == (R where S₁ generates structured reasoning and expands the report.
researchhub.com
pinterest.com
irs.gov
Verdict Prediction (St): (Rg y))) == Sc(Re re- turning updated report Rg" E - and verdict y E EV.
Table List of excluded domains due to bot traffic restric- tions.
Let 1 == minfi S N y + NEI or = N). then the final outputs are yout == y( and Row: := So( ())))), where Sg denotes the justification stage that appends rationale to the final report.
questions designed to probe the claim's 's veracity. The Plan- ner then proposes targeted search queries for each question and applies the Web Search tool to retrieve up 5 rele- vant search results. Using the retrieved evidence, the LLM backbone attempts to answer the questions systematically. Finally, the system synthesizes the answers into a coherent verdict, ensuring the reasoning is grounded in the collected evidence. The resulting QA pairs are evaluated using the AVERITEC Score, showcasing INFACT 's alignment with the benchmark's evaluation criteria while maintaining its structured, evidence-driven methodology.
This formal view captures the iterative and modular nature of DEFAME, highlighting how evidence is retrieved, pro- cessed, and transformed into a final verdict and explanation.
G. DEFAME Confusions
Figure g shows the confusion matrices for all three back- bones on the four benchmarks, respectively.
F. Formal Representation of DEFAME
A closer look at Figures 8g 8h and 8i reveals that GPT-40 with and without Chain-of Thought overpredicts ooc while DEFAME's confusions are more balanced. Surpris- ingly, incorporating Chain-of-Thought prompting hurts the performance on MOCHEG (see Figures [d Se and Table According to the confusion matrices the introduction of Chain-of- Mfainoo--hhouhht makes the model more unsure, leaning towards ? NEI compared to the Pure GPT-40. In contrast, DEFAME predicts too confidently even when the ground truth implies insufficient information. qualitative analysis of the failure cases reveals that, in several cases, the ground truth explanation is longer up-to-date (see Sections 5.7 and Appendix DD.
We include a formalization of the DEFAME pipeline to clarify the role of each stage and its iterative structure.
Let T and I denote the spaces of text and images, respec- tively. Define M == (T Z)) as the space of multimodal sequences, and let ) denote the space of verdict labels. Then, DEFAME is a function
F:M+MX> F(c) =(Pott Yout)
where, given a claim c E .M, the output consists of a report Reat containing the full fact-check and predicted verdict Youu- DEFAME proceeds iteratively up N steps. We can denote each iteration with Fier MM- + M D, so that
On AVERITEC (Figures Sa 8b and 8c we observe the opposite hehavior with the GPT-40 variants overpredicting NEI compared to our framework. Lastly, DEFAME's dataset ap- pear balanced in Figure while the baselines lack the nec- essary evidence make correct veracity predictions, very often defaulting to ? NEI.
(R(++), ((((t)) = (n)(
false predictions on the CLAIMREVIEW2022 wwile
i.e., an (incomplete) report R()) MM gets extended with new actions, evidence, and elaboration, resulting in report
R((+1) and intermediate verdict y (1+1), We can decompose Fire into the five individual pipeline stages
H. Prompts Used in DEFAME
Fiter : Sr S4 o S3 S2 o Si,
Each stage of the DEFAME framework is guided by a tai- lored prompt. These prompts are constructed from prompt
where each stage can be described as follows:
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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Predicted
Predicted
Predicted
86
18
9
10
50
206
29
13
64
198
259
13
10
3
16
9
9
2
15
12
10
21
6
(a) AVERITEC-GPTE GPT-40
(b) AVERITEC- GPT-40
(c) AVERITEC- DEFAME
268
178
54
174
311
15
326
67
107
Adtel
75
87
51
2.02
414
19
351
297
40
35
592
(d) MOCHEG GPT-40
(c) MOCHEG GPT-40 CoT
(f) MOCHEG DEFAME
Predicted
Predicted
Predicted
160
178
205
254
84
42
621
70
593
71
591
VERITE GPT-40
(h) VERITE GPT-40 CoT
(i) VERITE DEFAME
Figure 8: Confusion matrices for GPT-40, GPT-40 COT, and DEFAME, across the AVERITEC, MOCHEG, and VERITE datasets.
templates, where values enclosed [] serve as placchold- ers. During execution, these placcholders are dynamically replaced with the corresponding variables. This process ensures that the prompt specific the current task and context, illustrated in and H.2
Some prompts require specific values to be returned by the LLM, such as the verdict the Judge Prompt or the pro- posed actions in the Plan Prompt. In these cases, both the expected value and its format explicitly defined within the prompt to guide the LLM's response. Value-specific fallback mechanisms are employed to enhance robustness.
In subsequent sections, we present the templates for the remaining four stages of the DEFAME framework. Each template includes detailed explanations its placeholders. key placeholder is [Record], which present in every prompt. This generic placeholder provides the current state of the fact-checking report, consolidating the claim, evi- dence, and findings gathered so far, ensuring that the LLM operates within the relevant context.
These mechanisms, primarily based on regular expressions tailored to observed failure modes, ensure that the required values can still be reliably extracted, even the LLM devi- ates from the expected format or introduces minor inconsis- tencies.
For multimodal LLM inputs and outputs, images are inte- grated into prompts through referencing system. When an image reference (c.g., image: :k) is encountered, the
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DEFAME: Dynamic Evidencc- FAct-checking with Multimodal Experts
system inserts corresponding image block, including the Base64-encoded representation of the image. The LLM ref- erences these images using the same identifier, maintaining consistent link between visual and textual elements.
[Examples]: Provides in-context examples that demonstrate how actions with the correct for- mat. These examples illustrate the structure and logic behind action proposals to guide the Planner.
The prompt templates in DEFAME are dataset-agnostic, enabling use across benchmarks with minimal adaptations. Dataset-specific changes are limited [Extra Rules] in the Plan and Judge Prompts. MOCHEG requires addi- tional rules, while AVERITEC includes guideline to avoid the "argument from ignorance" fallacy, ensuring unsup- ported claims are labeled as Not Enough Information. In VERITE, detailed instructions are needed for the OOC class, which includes samples generated in two ways: true images with altered captions (formerly Miscaptioned) and true captions with unrelated images. These rules ad- dress dataset-specifie nuances while maintaining consis- tent framework.
H.1. Plan Prompt Template
Instructions
The available knowledge is insufficient assess the Claim. Therefore, propose set of actions to retrieve new and helpful evidence. Adhere to the following rules:
The actions available are listed under Valid Ac- tions, including a short description for each action. No other actions are possible at this moment.
For each action, use the formatting specified in Valid Actions.
Include all actions in single Markdown code block at the end of your answer.
Propose as few actions as possible but many needed. Do not propose similar or previously used actions.
[Extra Rules]
[Valid Actions]
[Examples]
[Record]
Your Actions:
[Extra Rules]: Contains benchmark-speciin plan- ning guidelines that the Planner must follow when selecting actions. These rules are tailored to the re- quirements of individual datasets or evaluation scenar- ios, ensuring that the Planner adheres task-specific constraints or priorities.
[Valid Actionsl: Represents the set of actions available to the Planner at a given stage. The list of valid actions is dynamically adapted to avoid reusing the same action unnecessarily.
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DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
H.2. Finalized Plan Prompt
H.3. Summarize Prompt Template
Instructions
Instructions
The available knowledge is insufficiett to assess the Claim. Therefore, propose set of actions to retrieve new and helpful evidence. Adhere to the following rules:
In order to find evidence that helps your fact-check, you just ran a web search, which yielded a Search Result. Your task right now is to summarize the Search Result. What include:
The actions available are listed under Valid Ac- tions, including a short description for each action. No other actions are possible at this moment.
Information that might be useful for the fact-check (see Record).
For each action, use the formatting specified in Valid Actions.
Relevant images (refer to images by inserting their reference in the format <image :k>).
Include all actions in single Markdown code block at the end of your answer.
If available: the release date well the author the publisher (e.g., the media company) the search result.
Propose as few actions as possible but much needed. Do propose similar or previously used actions.
Do NOT include:
Advertisements.
Consider Both Modalities Equally: Avoid focus- ing too much on one modality at the expense of the other, but always check whether the text claim is true or false.
Any other information unrelated to the Record or the Claim.
Additional Rules:
Compare Image and Caption: Verify the context of the image and caption.
Do not add any additional information besides the information in the Search Result.
Valid Actions:
If the Search Result doesn't contain any relevant information for the fact- fact-checking work, simply print one word in capital letters: NONE.
geolocate: Determine the country where an im- age was taken by providing an image ID.
Keep your writing style consistent with the provided Examples.
reverss searchh Perform reverse image search the web for similar images.
Try to filter out relevant information even if the Search Result is in different language.
web_search: Run open web search for related webpages.
image.search: Retrieve related images for given query.
[Examples]
[Record]
[Search [errch]
Examples:
Your Summary:
geolocate (<image:k>)
(<
web search ("New Zea land Food Bil 2020")
(Examples): Provides in-context examples that demonstrate how to write concise summaries, incor- porating relevant images, key insights, and links to sources using Markdown notation. One of the exam- ples shows case where the search result is irrelevant, guiding the model to return NONE instead of summary when no useful information is found.
image_search ("China officials white suits carry people")
Record:
Claim: "<image:1222>> Image of bus pow- ered by compressed natural gas, bursting into flames in Italy."
CtearchRReslle Refers the search result re- trieved by Search tool. This includes the text content scraped from the webpage using the Firecrawl web scraper, the title of the page, any hyperlinks found within the content, images included on the page, and the source URL
Your Actions:
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DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
H.4. Develop Prompt Template
a short description of each label. These descriptions provide additional context help the model accurately differentiate between the available options.
Instructions
You just retrieved new Evidence. Now, analyze the Claim's veracity using the evidence. Always adhere to the following rules:
H.6. Justify Prompt Template
Focus on developing new insights. Do not repeat larger parts from the Record. Do not restate the Claim.
Instructions
You are provided with the record of a fact-check. It con- tains the Claim to be verified and documentation of all the fact-checking work along with the gathered evidence. Your task is to summarize the fact-check. That is, you provide a concise, one-paragraph justification for the fi- nal VERDICT based on the knowledge from the Record. Note:
Write down your thoughts step-by-step. Whenever necessary, you may elaborate in more detail.
Depending on the topic's complexity, invest one to three paragraphs. The fewer, the better.
If you find that there is insufficient information to verify the Claim, explicitly state what information is missing.
Be truthful, brief, and do not add any additional information besides the information given in the Record.
you cite web sources, always refer them by including their URL as a Markdown hyperlink.
Link key sources in your summary. Use Markdown notation for that. You may link them in-line.
Use information only from the recorded evi- dence: Avoid inserting information that is not im- plied by the evidence. You may use commonsense knowledge, though.
Don't state the Claim again. Rather focus on the key insights of the fact-check.
Simply print just the summary.
[Record]
[Record]
Your Analysis:
Summary:
H.S. Judge Prompt Template
I. Wrongly Annotated VERITE Samples
Instructions
During the qualitative analysis of 20 mispredicted VERITE instances, we encountered cases we argue to be wrongly annotated. Figure shows the corresponding claims and (wrong) annotations. The VERITE annotation classifies the first claim (753) as Supported. However, DEFAME found the image to be captured not in September but in July 2018, citing fact-check by USA Today from May 2022. Manually investigating this case further, we find that USA Today refers to an article on Mediaite from Sept 6th, 2018, indeed stating that "in July, Clanton shared photo Instagram of herself, Thomas, and Ginni Thomas having 'great weekend' together," showing the screenshot of the respective Instagram post. Hence, the actual correct label for this claim should be OOC.
Determine the Claim's veracity by following these steps:
1. Briefly summarize the key insights from the fact- check (see Record) in most one paragraph.
2. Write one paragraph about which one of the De- cision Options applies best. Include the most appropriate decision option at the end and enclose in backticks like 'this'.
[Extra Rules]
[Decision Options]
[Record]
Your Judgement:
The wrong annotation can seen even more clearly for the claims 249 and 250. DEFAME's reverse search yielded multiple credible sources, including an article by CBS News consistently reporting about gathering of 75 peo-
[Extra Rules]: Contains speecfi rules or additional constraints that guide the judgment pro- cess. These rules are designed to increase the model's
understanding of the different classes present in the cor- responding benchmark, ensuring consistent verdicts.
2018/993009000 o.
[Decision Opns] Lists the possible labels or verdicts that can be assigned to the claim, along with
www.
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DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
ple the "Area 51" military airbase in Sept 2019. The sources the claim's photo along with other similar im- ages showing the apparently same event. According to the VERITE annotation, the photo shows a completely different event, contradicting the evidence. Consequently, the pro- vided labels for both claims are clearly "switched." Since we analyzed only 20 samples, there are likely more such wrongly annotated samples, penalizing DEFAME's accu- racy where it should not be. Hence, the actual accuracy of DEFAME is slightly higher than measured.
Claim # 753
"An image of Supreme Court Justice Clarence Thomas with his wite, Ginni Thomas, holding a bottle of wine was captured in September 2018 after hiring Crystal Clanton to assist her me- dia ventures."
J. MOCHEG Metric Clarification
Equivalence of Micro-F1 and Accuracy in Multi-Class Settings. MOCHEG's single-label, multi-class setting, miero-F1 score and accuracy are mathematically equivalent.
To see this, recall that the micro-Fl score is defined as
Annotation Supported
Actually ooc
2 precision recall
2 TP
Fl
precision + recall
TP FP FN
Now observe that since each incorrect prediction contributes exactly false positive and one false negative, and all correct predictions are true positives, we can write
Claim # 250
"Image shows a crowd of people at the 'Area 51 Raid' in September 2019."
TP (FP FN) Total # of predictions.
Therefore, the micro-Fl score becomes
Annotation ooc
Actually
Supported
TP
Correct pred.
F1 micro
Accuracy.
TP + } (FP FN)
All pred.
This identity holds in any multiclass, single-label classi- fication setting. We include this clarification to prevent confusion caused by legacy naming conventions in prior work MOCHEG.
Claim # 249
"Image shows crowd of people during 'crusade' by the religious group 'In His Name Ministries' in Novem- ber 2014 in Nikomazi, South Africa."
K. Outdated Ground Truths and Temporal Dependence in MOCHEG
Annotation Supported
Actually ooc
A qualitative analysis of failure cases in the MOCHEG dataset reveals that some ground truth explanations are no longer accurate or up-to-date, potentially affecting model evaluations (see Section (.7). This issue arises when real- world developments render previously valid ground truths obsolete.
Figure 9: Three faulty VERITE instances identified during qualitative analysis.
For instance, consider the claim:
"A company is developing lab lab-grown chicken nugget made from feathers."
wow. wtwpseww...o//eew////.e. wwwp:///////w///......c..o area 51 hoax drews- hundreds- www.-rveeetttttteeteeeeee secret base------eeee waw.--tddyy-2111111 39- 20- Tvveupdttss
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DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
This classified as Not Enough Information by Snopes, with the explanation that it is undetermined when these lab-grown nuggets will hit store shelves [ However, since the creation of the benchmark, lab-grown meat has become commercially available in parts of the world [3]
and CoT LLM groups using the Mann- Gann-Whitney U Test. This non-parametric test chosen due the non-normal dis- tribution of completeness scores. The Mann-Whitney U Test yields p-value of approximately 9.1 x 10-9, deeming the findings statistically significant. All Participants have a higher degree in education.
Other examples of claims with temporal dependence in- clude:
To provide further insights, we include a direct comparison of a fact-checking report generated by DEFAME and one from the Chain-of- Chain-of-Thought Thought baseline for the same claim (see Figures T0 and [ This example illustrates the key differences observed during the evaluation, particularly in the Completeness dimension. While both reports maintain coherence in structure and logical flow, the DEFAME report explicitly links its verdict to multiple pieces of evidence, providing a clear justification. In contrast, the CoT report relies heavily on parametric reasoning, lacking grounded evidence to support its conclusions.
Claim: "Al Gore's residence uses considerably more energy than the average American home."
Claim: "Oreos have a new Lady Gaga-themes cookie." "
Claim: "Says Anthony Fauci will make millions off new book."
Such claims rely on a specific temporal context that may no longer be accurate as time progresses. Including such claims without temporal markers risks introducing inaccura- cies into evaluation. To address the challenge of temporal dependence in fact-checking benchmarks, we propose the following alternatives:
While our human evaluation highlights DEFAME's strengths, we acknowledge certain limitations, such as the relatively small number of claims evaluated.
M. Automatic Justification Evaluation
1. Include Timestamps: Ensure that datasets include clear timestamps for both the claim and the associated ground truth explanation, allowing systems to account for the temporal context.
We also compare the generated justifications on the MOCHEG dataset to the gold rulings in Table g Note that our competitors here are fine-tuned on the gold rul- ings (except the top row and CoT). The vanilla zero-shot performance of DEFA DEFAME trails behind the other methods. We experimented by providing a single in-context example to convey the expected style of the rulings. It leads significant improvement in all metrics, even compared to the state- of off-th--rt fine-tuned models. Nonetheless, rather than speaking for much improved explanatory power, this shows the shortcomings of the automatic metrics, e.g., their sensitivity to specific n-grams and wording.
2. Filter Out Time-Sensitive Claims: Exclude claims with high temporal sensitivity from the dataset to avoid potential inconsistencies over time.
3. Periodic Updates: Regularly update benchmarks to reflect evolving ground truths, ensuring their continued relevance.
4. Temporal Validity Check:
Integrate a rre-proeessing step to verify whether the ground truth explanations remain consistent with current knowledge before eval- uation.
Model
ROUGE
BLEU
BERTScore
Best w/o FT Yao al 2023
17.18
7.38
83.95
FT Chen all. 2014
24.60
11.40
88.10
L. Details on the Human Evaluation
FT
Yao et 2025
24.83
10.08
86.95
DEFAME DEFAME 1-shot
18.72
3.20
85.89
We ensure a minimum number of evaluations per claim and include random samples from the Chain-of-Thought baseline for comparison. (We post-process the baseline out- puts to match the format of DEFAME's output to "disguise" it.) In total, 154 of the submissions assess the outputs from DEFAME while 31 assess the baseline. An example of the report format is included in Appendix N We assess the difference in Completeness scores between the DEFAME
25.37
7.31
87.42
Table 8: Performance Comparison of Explanation Genera- tion (in %). Best scores are marked in bold, second best are underlined.
N. Example Fact-Checking Reports
Figures 12 to 15 display two fact-checking reports as re- turned by DEFAME, including a correct veracity prediction. We rendered the Markdown output into a PDF, including the referenced images. These and all following reports also
httpp: /ttw://wwps....../t////om// snopes. http:///a//ccccccc/ccccccccaaaca
Peather-cal
lattps: / http:///dwaa..aeeeeee///////////
conn-chiikenn cken- tastes-
eot 1 werinng-tt--e 1opg---t-1------ waw--is--ooooommmm ooated
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DEFAME: Dynamic Evidence- FAct-checking with Multimodal Experts
Claim
photograph of a police officer with boot on the shoulder a protester 2010
during protests G20 summit Toronto."
Actions
reverse eeverse_searcheee search(<<magge :130)>):173>> 1732>)
web_ (rbrseerc(( (e ("G20 summit Toronto 2010 police protest image")
Evidence
Evidence from reverse_searee
The image question is confirmed to from the 2010 G20 summit protests in Toronto, where police clashed with protesters. The Toronto Star reports
From Source:
$16.5 million thousand individuals arrested during
settlement between the Toronto police board and the event, highlighting the image of police and protesters. Socialist Action discusses the settlement and criticizes ongoing police misconduct, using the same image. Analysis News also confimm the image's origin, discussing the aggressive police tactics and civil rights violations during the protests
Evidence from web_search
The Getty Images page contains multiple images related to the G20 summit protests in Toronto in 2010, showing police and protesters clashing. There are no specific details about the image in question, but the context aligns with the claim of police- protester interactions during the summit.
From Source: Content:
Elaboration
The evidence confirms that the image is from the 2010 G20 summit protests in Toronto. Multiple sources, including the Toronto Star, Socialist Action, and The Analysis News, verify the context of the image, showing police clashing with protesters. The Toronto Star article mentions a settlement related to mass arrests during the event, further supporting the claim's context. Additionally, Getty Images provides a collection similar images from the protests, reinforcing the setting and time frame.
Therefore, the claim about the photograph depicting police officer with boot on the shoulder of protester during the 2010 G20 summit in Toronto is verified accurate.
Final Judgement
The fact-check confirms that the image is indeed from the 2010 G20 summit protests in Toronto, Multiple sources, including the Toronto Star and Socialist Action, verify the context image, showing police clashing with protesters. The Toronto Star article mentions a settlement related mass arrests during the event, further supporting the claim's context. Getty Images also provides a collection similar images from the protests, reinforcing the setting and time frame. The claim accurately describes the origin, content, and context of the image, as verified by multiple sources. Therefore, the most appropriate decision option is supported ted.
Verdict: SUPPORTED
Justification
The image confirmed to be from the 2010 G20 summit protests in Toronto, depicting police clashing with protesters. Multiple sources, including the Toronto Star, Socialist Action and The Analysis News verify the context and authenticity of the image. The Toronto Star article also mentions settlement related mass arrests during the event, supporting the claim's context. Getty Images provides additional images from the protests, reinforcing the setting and time frame. The claim is thus supported.
Figure 10: Fact-checking report by DEFAME, presented in the Human Evaluation.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Claim
"A photograph riot police officer with his boot on the shoulder of protester in 2010 during protests over G20 summit Toronto."
Final Judgement
The image shows a police officer with his boot on the shoulder protester, which aligns with the claim that was taken during protests over a G20 summit in Toronto in 2010.
This event marked significant clashes between police and protesters, and the image accurately reflects tense and confrontational atmosphere of those protests.
Therefore, the claim accurately describes the origin, content, and context of the image. The decision option that applies best is 'supported'.
Verdict: SUPPORTED
Justification
claim that image depicts police officer with his on protester's shoulder during the 2010 G20 summit protests in Toronto is supported. The image accurately reflects the confrontational atmosphere of the event, aligning with the described context and content.
Figure 11: Fact-checking report by the Chain-of-Thoughh baseline, presented in the Human Evaluation.
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DEFAME: Dynamic Evidence-baset FAct-checking with Multimodal Experts
include hyperlinks referencing the used resources.
O. Examples of Failure Cases
The qualitative analysis of mispredicted VERITE instances uncovered two common failure modes attributed to DE- FAME: premature judgment and failed evidence retrieval. See Figure 16 for an example of a premature judgment. While it is true that there exists photo showing the "Air Force Officers' Spouses' Club" taken April 24, 2018 it is not the photo depicted in the claim. DEFAME missed comparing the claim photo with evidence photos before judging the veracity.
18 show a case where the re- trieval of evidence (from either (reverse) image search or web search) was unsuccessful, resulting in wrong predic- tion. Manual inspection reveals that according to Snope the origin is a news video accessible on YouTube. However, both Snopes and YouTube are excluded from DEFAME's search results. Apart from that, mamually using all three web search tools yields only more excluded or unrelated results.
Additionally, Figures 17
Forr technical reasons, the hyperlinks are not preserved in this
PDF.
attpo: http://hhaaaaaoooooo////////aaaa
//taskandpurpose com/mi http:///ww.a..aaaaaa/a//////aaaa
spouse: Lweet photo/
Hing- hana-hurricaaa//
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Claim
"A drunken man in a basket. Bars in Turkey employed basket man who's job was to take patrons home who too drunk stand up. lstanbul 1960s."
Actions
geolocate(! [image 245017] web www.serrrhh"aaaaee Istanbul 1960s Turkey") image_searche ("basket man Istanbul 1960s Turkey")
Evidence
Evidence from geolocate
The most likely countries where the image was taken are: ("Turkey': 0.26, 'Albania': 0.22, 'Bulgaria': 0.18, 'Serbia': 0.06, 'Hungary': 0.05, 'Palestine:: 0.05, 'Montenegro': 0.04,
'Macedonia:: 0.03, 'Portugal:: 0.02, 'Andorra': 0.01)
Evidence from image_search
The concept of "basket men" in Istanbul during the 1960s involved individuals employed to carry home patrons who carrying another basket, similar to the one described claim, without additional context. Paintvine History in Memes provides further discussion on this practice, confirming its existence during that time period.
too drunk stand. An image from Paintvine shows
Figure 12: Exemplary fact-check report with a correct prediction, page of 2.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Elaboration
The evidence supports the claim that "basket men" were employed in lstanbul during the 1960s to carry home patrons who were too drunk to stand. The image search confirms the existence this practice, showing similar images providing context about the role of these individuals. The geolocation analysis suggests Turkey as a likely location for the image, aligning with the claim's mention of Istanbul. However, the specific details about the individuals the image, such whether the person in the basket indeed drunk, cannot be confirmed solely from the evidence provided. Overall, the claim about the practice and its location is supported by the evidence.
Final Judgement
The fact-check reveals that the practice of employing "basket men' in Istanbul during the 1960s carry home patrons were too drunk stand is supported by historical context and similar images. The geolocation analysis also suggests Turkey as a likely location, aligning with the claim. However, specific details about the individuals in the image, such as the state of the person in the basket, cannot be confirmed.
Given the evidence, the claim about the practice and its location is supported. Therefore, the most appropriate decision option supported.
Verdict: SUPPORTED
Justification
The fact-check confirms that the practice of employing "basket men" in Istanbul during the 1960s to carry home patrons were too drunk stand is supported by historical context and similar images. Geolocation analysis suggests Turkey as a likely location, aligning with the claim. However, specific details about the individuals the image, such as the state of person basket, cannot confirmed. Overall, the claim about the practice its location is supported. Paintvine History in Memes.
Figure 13: Exemplary fact-check report with a correct prediction, page of 2.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Claim
"Image of Joe Biden kissing his 19- year-old granddaughter, Finnegan Biden."
Actions
reverse reverse_searrch 1328>) web_search(" Biden kissing granddaughter Finnegan Biden")
Evidence
Evidence from reverse_search
Found exact image on website with title: Joe Biden kisses granddaughter on lips during lowa rally Content: The New York Post reports that Joe Biden kissed his 19-year- old granddaughter, Finnegan Biden, the lips during campaign rally in lowa. The event took place at Clarke University in Dubuque. The article includes images kiss:
From Source:
IOW!
The incident prompted mixed reactions on social media. The article also mentions past criticisms of Biden's behavior with women.
Figure 14: Exemplary fact-check report with a correct prediction, page of 2.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Evidence from web_search
The photo in question shows Joe Biden kissing his 19-year-old granddaughter, Finnegan Biden, during campaign stop in lowa. This event took place February Clarke University in Dubuque. The claim that he was kissing a 15-year-old is false. The gesture sparked mixed reactions, with some finding unsettling and others viewing as normal display of affection.
For more details, you refer to the articles from AP News and NZ Herald.
Elaboration
The evidence confirms that the image shows Joe Biden kissing his granddaughter, Finnegan Biden, during a campaign rally in lowa. The event took place at Clarke University in Dubuque. The New York Post and other sources, such as AP News and NZ Herald, corroborate this information. The claim that she is 19 years old also supported by these sources. Therefore, claim verified as true.
Final Judgement
The fact-check confirms that the image shows Joe Biden kissing his granddaughter, Finnegan Biden, during campaign rally in lowa. The event took place at Clarke University in Dubuque, and multiple sources, including the New York Post, AP News, and Herald, verify this information. The claim that sh 19 years old is also supported by tousesources. confirming the afcuracy the claim.
The caption is factually correct, and the image aligns with the claim. There is indication that the image is used out of context, as accurately represents the described event. Therefore, the claim is supported by the evidence provided.
supported
Verdict: SUPPORTED
Justification
The fact-check confirms that the image shows Joe Biden kissing his granddaughter, Finnegan Biden, during a campaign rally at Clarke University in Dubuque, lowa. Multiple sources, including the New York Post, AP News, and NZ Herald, verify this information and confirm that is 19 years old. The claim accurate and supported by the evidence.
Figure 15: Exemplary fact-check report with a correct prediction, page of 2.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Claim
"Image depicts members of the Air Force Officers' Spouses' Club taken at the annual Joint Armed oorces Washington Luncheon (JAFOWL) on April 24."
Actions
reverse_ (errreessssrrccccaeeeeeeeeee reb_search("Air Force Officers' Spouses' Club annual Joint Armed Forces of Washington Luncheon April 24")
Evidence
Evidence from
From Source: Content: The photo in question was taken at the annual Joint Armed Forces of Washington Luncheon (JAFOWL) April 24, not at the White House. depicts members of Air Force Officers' Spouses' Club. The event is criticized for potentially reinforcing hierarchy among military spouses. The image was mistakenly associated with a White House event by Ronald Klain in a viral tweet.
Elaboration
The evidence confirms that the image was taken at the annual Joint Armed Forces of Washington Luncheon (JAFOWL) on April 24 and depicts members the Air Force Officers' Spouses' Club. This aligns with the claim, verifying its accuracy. The image was mistakenly associated with White House event viral tweet, but this does not affect the claim's validity regarding the event and group depicted. Therefore, the claim true based on the provided evidence.
Final Judgement
The fact-check confirms that the image was taken at the annual Joint Armed Forces Washington Luncheon (JAFOWL) on April 24 and depicts members the Air Force Officers' Spouses' Club. This aligns with the claim, verifying its accuracy. Although the image was mistakenly associated with White House event in a viral tweet, this does not affect the claim's validity regarding the event and group depicted.
The claim accurately describes the origin, content, and context of the image. Therefore, the most appropriate decision option is supported.
Verdict: SUPPORTED
Justification
The fact-check confirms that the image was taken the annual Joint Armed Forces Force Officers' Spouses' Club, as claimed. Despite viral tweet mistakenly associating the image with White House event, the evidence from Task & Purpose supports the claim's accuracy regarding the event and group depicted. Therefore, the claim supported.
Washington Luncheon (JAFOWL) on April 24 depicts members
Figure 16: Report of fact-check which ended a wrong prediction due to a premature judgment.
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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Claim
"Image shows portions of fishnet as well as several fish being stuck up on power lines after traffic accident with fisherman boat, August 2020."
Actions
severssesearch<<iimeee
web_search ("fishnet ("rssscccc""iisnet power lines traffic accident fisherman boat August 2020")
Evidence
new evidence found.
Elaboration
The claim suggests that traffic accident involving fisherman's boat resulted in fishnet and several fish being stuck on power lines. However, there no new evidence to support or refute this claim. The image alone does not provide enough context or details to verify the incident or its cause.
To the claim's veracity, additional information needed, such as reports or eyewitness accounts from August 2020 that describe a similar incident. Without such evidence, the claim remains unverified.
Actions
image_ serre_sserch_eeeee ("fishnet power lines traffic accident August 2020") web_ search ("fishnet power lines accident fisherman boat August 2020")
Evidence
No new evidence found.
Elaboration
The claim remains unverified due a lack supporting evidence. The image alone does not provide sufficient context to confirm the incident described. No additional information, such as news reports eyewitness accounts from August 2020, has been found to substantiate the claim. Therefore, without further evidence, the claim cannot be confirmed refuted.
Figure 17: Report of a fact-check (page of 2), which ended in a wrong prediction due to the failed retrieval of evidence.
34

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DEFAME: Dynamic Evidence- based FAct-checking with Multimodal Experts
Actions
srrr__seacchhhaa power lines accident August 2020") web search (efearcc""fssnttt ("fishnet power lines traffic accident August 2020")
Evidence
No new evidence found.
Elaboration
The claim remains unverified due to insufficient evidence. Despite multiple searches, no supporting information such as news articles or eyewitness accounts from August 2020 has been found confirm the incident described. The image alone lacks context and details necessary substantiate the claim. Without additional evidence, the claim cannot be confirmed or refuted. Further investigation or credible sources are needed to verify the event.
Final Judgement
The claim suggests that traffic accident involving fisherman's boat resulted in fishnet and several fish being stuck on power lines. Despite multiple searches, no supporting evidence such as news reports or eyewitness accounts from August 2020 has been found to confirm this incident. The image alone does not provide enough context or details to verify the claim.
Given the lack of evidence and context, the claim appears construct narrative around the image without substantiation. Therefore, the claim best categorized of context.
Verdict: OUT OUT_OF_CONTEX_ CONTEXT
Justification
The claim that traffic accident involving a fisherman's boat resulted in fishnet and several fish being stuck on power lines remains unverified to lack of supporting evidence. Despite multiple searches, no news reports or eyewitness accounts from August 2020 have been found to confirm the incident. The image alone lacks sufficient context to substantiate the claim, leading to the conclusion that the claim is out of context.
Figure 18: Report of a fact-check (page of 2), which ended in a wrong prediction due to the failed retrieval of evidence.
35


Reviews:
[{"review_id": "TFUJES3d9X", "reviewer": "Reviewer_qLGa", "summary": "This paper introduces DEFAME, a multimodal pipeline for open-domain text-image claim verification. DEFAME operates as a six-stage process that handles both multimodal claims and evidence while generating structured reports. It dynamically selects appropriate tools to extract and evaluate both textual and visual evidence, using web search, image search, reverse image search, and geolocation tools.\nThe authors evaluated DEFAME on three available benchmarks (VERITE, AVERITEC, and MOCHEG) where it surpassed previous state-of-the-art methods. They also introduced CR+, a benchmark containing claims after GPT-4o's knowledge cutoff, where DEFAME significantly outperformed GPT-4o and GPT-4o CoT. Ablation studies and human evaluations confirmed DEFAME's components each contribute to its performance and that it provides better justifications than baseline LLMs.\nThe authors address an important issue in the proliferation of misinformation and identify critical limitations of current MLLMs, particularly their reliance on static parametric knowledge and inability to access up-to-date evidence.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank for highlighting that the “paper has great potential” and are grateful for pointing out the usefulness of the information provided in the appendix. We appreciate the time invested into the review and are happy to address the concerns in the following.\n\n### Summarize, Develop, and Justify Stages\n- The summarize stage serves several roles. Efficiency and Comprehensiveness: Without it, retrieved web evidence (entire web articles, PDFs, etc.) would go directly into the report, resulting in an excessively long document containing unfiltered raw information, incl. ads and other irrelevant information. Report documents have length of typically a few thousand tokens, but raw evidence can go well beyond hundreds of thousands. This would increase the required computational cost by orders of magnitude and decrease the human readability of the report. Performance: Upon your request, we compared standard DEFAME against simple truncation at 1000 characters and observed a drop from 68.7% to 63.2% accuracy on CR+, confirming its practical necessity.\n- The develop stage mainly targets to determine how well the claim is supported by the gathered evidence. This is done by contrasting the claim with the evidence and performing Natural Language Inference (NLI) through Chain-of-Thought (CoT). We expect from the NLI to deduce facts helpful for the fact-check. Moreover, we expect that gaps of missing evidence become evident during that process. This stage prepares the judge stage where the actual classification happens.\n- The justify stage aims to produce a comprehensive summary of the report to serve the user as a quick explanation of the decision. It is purely explanatory and does not affect label prediction—ablating it would not change performance metrics. \n\n### GPT-4o Comparison (Single-turn vs. Multi-turn)\nWe conducted an additional experiment using multi-turn GPT-4o with CoT prompting leveraging the same reiteration criterion as in DEFAME. It achieved 40.7% accuracy on CR+, only slightly improving over the single-turn variant. The primary limitation remains GPT-4o’s lack of access to external evidence, leading to overuse of NEI.\n\n### Human Evaluation\nAs detailed in Appendix H, evaluators saw complete reports (claim, evidence, verdict, justification); baseline outputs were reformatted for fairness (Appendix K). While not trained fact-checkers, all evaluators had higher education and familiarity with MLLMs.\n\n### Other Models on CR+\nPrior SOTA methods weren’t included on CR+ due to multiple reasons. Please refer to our response to Reviewer 3bbT (*“‘Incomplete’ Table 3”*) for more details.\n\n### Ablation Scope\nThank you for recommending additional ablations, which we now extended to CR+, see the results in the table below.\n\n|Variant|CR+ Acc.|\n|-|-|\n|DEFAME|**68.7**|\n|w/o Geolocation| 65.7|\n|w/o Reverse Search| 64.0|\n|w/o Image Search| 63.7|\n|w/o Web Search| 59.7|\n|Single Turn| 63.3|\n|Static Actions| *68.0*|\n|w/o Develop|67.0|\n|w/o Summarize|63.2|\n|Unimodal Develop| 65.7|\n\nThe values confirm that all components contribute meaningfully to DEFAME’s performance - removing any would hurt the method. We intentionally did not extend ablations to AVeriTeC due to its unimodal nature, which renders many multimodal ablations inapplicable.\n\n### Component Contributions\nWe observe distinct tool contributions across datasets due to the fundamental differences in the tasks: VERITE targets Out Of Context (OOC) detection which (as already shown by previous work) benefits highly from applying Reverse Image Search to the input image. MOCHEG benefits from Web Search strongly as, in contrast to VERITE, it is constructed from real-world claims only.\n\nWe performed an extra ablation DEFAME with GPT-4o + Web Search (GPT WS) on CR+, where GPT WS was allowed to perform a single round of evidence retrieval and to apply CoT. GPT WS achieved an accuracy of 54.7% which is clearly better than the native baselines but still strongly worse than DEFAME (accuracy 68.7%). Would you like to see additional ablations on MOCHEG and VERITE?\n\n### NEI Confusions in MOCHEG\nYes, many NEI errors result from outdated or time-sensitive labels, as discussed in Appendix G. Others stem from DEFAME’s current limitation of not assessing source credibility. For example, in one case, DEFAME classified the claim \"Former President Jimmy Carter said 'America has no functioning democracy at this moment.’” as Supported based on an article from truthout.org. However, Snopes labeled the same claim as Not Enough Information, arguing that further verification from more authoritative or corroborated sources was necessary. This illustrates how differences in evidence sufficiency thresholds—and subjective judgments about source credibility—can lead to apparent disagreement.\n\n### Effect of Planning\nTable 4 and the additional ablations on CR+ in the table above already include the removal of planning, referred to as “Static Actions” (all tools used every time).", "claims_and_evidence": "Please see other sections for my comments.", "methods_and_evaluation_criteria": "1. The paper needs a clearer explanation of the \"summarize,\" \"develop,\" and \"justify\" stages. While prompts for each stage are included in the appendix, their specific goals remain unclear. The ablation study should also evaluate performance without these steps (currently only includes w/o Develop). Consider adding ablations for w/o summarize and w/o justify as well.\n2. The comparison with GPT-4o requires more methodological consistency. Currently, GPT-4o appears to be used in a single-turn manner. More meaningful comparisons would be between GPT-4o multi-turn (prompting again when the result is NEI) with DEFAME multi-turn, and between GPT-4o single-turn (current setup) with DEFAME single-turn.\n3. The human evaluation section needs more detail. Information should be provided about the evaluators' backgrounds and relevant experience in fact-checking. The paper should clarify what content was shown to evaluators (claims, actions, evidence, elaboration, final judgment, justification?) and which specific components were evaluated. The claim that DEFAME \"provides better justification compared to base MLLM\" suggests component-specific evaluation, but it would be more reasonable to evaluate only the evaluation, judgment, and justification parts of the report.\n4. In Table 3, should also include performance of other models on CR+", "theoretical_claims": "No proofs in this paper.", "experimental_designs_or_analyses": "1. The ablation study should be extended to include the AVeriTec and CR+ datasets.\n\n2. The paper misses an opportunity to discuss in depth how each component of the agent system complements a baseline MLLM. While section 4.3 provides examples of how DEFAME outperforms GPT-4o, it lacks systematic analysis beyond these examples. Several questions remain unanswered: Why does Web Search significantly help MOCHEG in the ablation table? Why does VERITE benefit substantially from reverse search? The ablation should be compared alongside baseline GPTs—since GPT-4o outperforms DEFAME without Web Search, comparisons between GPT-4o + websearch and DEFAME would help determine whether the other components truly add value. Without these detailed analyses, the paper lacks convincing evidence that all components should be included.\n3. The confusion matrix for MOCHEG-DEFAME in Appendix Figure 7 reveals that DEFAME struggles with classifying NEIs. The authors should confirm whether this is due to the outdated information issue mentioned in Appendix G, or provide alternative explanations.\n4. The effectiveness of the planning phase requires further examination. How would performance change if all actions were taken for every example instead of being selectively planned?", "relation_to_broader_scientific_literature": "Other works largely focused on isolated aspects of fact-checking—such as text-only verification, evidence retrieval, or uni-modal approaches—DEFAME integrates these perspectives into a comprehensive end-to-end solution. The authors position their work within the growing body of research on multimodal reasoning, retrieval-augmented generation, and explainable AI systems.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Other Strength: \n1. The paper compares carefully about time and computational cost of DEFAME and different ablations \n2. Appendix includes very useful information that did not make to the main paper", "other_comments_or_suggestions": "I think this paper has great potential, with improved discussion and a more detailed ablation (or reasonable explanation) I am willing to increase my score to accept.", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uCidFK2hrN", "reviewer": "Reviewer_K7Ta", "summary": "The paper  presents a novel approach to automated fact-checking designed to address the growing problem of disinformation. The authors introduce DEFAME, a modular system that uses a six-stage pipeline to dynamically select and use various tools for retrieving and evaluating both textual and visual evidence. This system is capable of handling multimodal claims (text and images) and generating detailed, explainable reports of its fact-checking process. Unlike previous approaches that were often limited to text-only analysis or lacked transparency, DEFAME integrates multiple evidence sources, including web searches, reverse image searches, and geolocation tools, to verify claims. The system was evaluated on several established benchmarks (VERITE, AVERITEC, MOCHEG) and demonstrated superior performance compared to existing methods, establishing new state-of-the-art results. Additionally, the authors created a new benchmark, CLAIMREVIEW2024+, featuring claims that occurred after the knowledge cutoff of GPT-4O to ensure more realistic evaluation scenarios. The results show that DEFAME significantly outperforms GPT-4O baselines on this new dataset, demonstrating its potential for real-time fact-checking in dynamic information environments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We value the time invested in the review and thank for the feedback and suggestions! Please find our response to your concerns below.\n\n### Comparison to Prior Work\nThanks for pointing out **SNIFFER**, a related work that was unintentionally omitted. Critically, unlike DEFAME, SNIFFER is **incapable of retrieving multimodal evidence** (“we input both the news caption and the text from webpages retrieved [...] into the LLM”, p. 5). It performs no planning. SNIFFER has only one tool (entity extraction) that is always executed. It cannot dynamically decide to retrieve additional evidence. SNIFFER is limited to news content and does not generalize beyond OOC detection. It is inapplicable to text-only inputs. Additionally, it requires finetuning - which DEFAME does not.\n\n**MMD-Agent** was missing from our initial submission as it was not peer-reviewed back then, but we are happy to integrate it now. MMD-Agent **does not incorporate visual evidence**. Specifically, Figure 4 and the [published code](https://github.com/liuxuannan/MMFakeBench/tree/main/eval/prompt_template/MMD_Agent) indicate that the gathered evidence is text-only. The paper only mentions Wikipedia as the source of external evidence (pp. 7 and 10). Additionally, their pipeline is static and does not allow for follow-up evidence retrieval. It does not include systematic planning. MMD-Agent is restricted to news. Finally, the outputs lack justifications and, therefore, the overall explainability of MMD-Agent is limited.\n\nYou mentioned the work by Tahmasebi et al. (CIKM 2024). The method introduced there is referred to as **LVLM4F**, which our paper covers in detail, most notably in the prior work overview (Table 1) and as a baseline in Table 3. Critically, LVLM4FV **is not designed to handle multimodal claims**: “Given a **textual** claim 𝑄 and a corpus of evidences C as input [...]”, p. 3.\n\nFinally, we did not include **LEMMA** for the following reasons. First, it is not peer-reviewed. Second, LEMMA **only retrieves textual evidence**: The \"Vision Evidence\" in Figure 4 of the LEMMA paper refers to \"a list of web page's title\" (p. 6). These web pages were retrieved via reverse image search using the input image - which is probably why they call it “vision evidence.” Third, it remains unclear if LEMMA applies to text-only inputs. Finally, it cannot retrieve further evidence after the first pass, does not involve any efficient tool use planning, and does not provide any comprehensible justification generation, limiting its explainability.\n\nConsidering all four references, our claim remains valid that no prior published MAFC method can handle both multimodal claims **and** multimodal evidence.\n\n### ClaimReview2024+ Details\nComplementary to Appendix J, we are happy to add more details on CR+ in the following: Claims were collected via the Google Fact-Check API by issuing queries across ten broad topics (climate change, politics, health, …). We deduplicated results based on the review URL to avoid overlap. For each claim, we also collected the date and author (claimant) to preserve context. Label assignment was handled in two stages: Trivial labels were automatically mapped using an LLM-based script (code included in the release), while non-trivial cases were manually annotated by a PhD-level MAFC researcher. A full validation pass was conducted by a student who compared extracted content (text, label, date, claimant, image) with the original fact-check articles to ensure accuracy. Images were manually curated since the Google API only returns teaser images, which often contain overlays or composites. Manual curation ensured that claim images are as close as possible to the original ones referenced in the fact-check. Because the Google Fact-Check API aggregates claims from leading organizations such as Snopes, PolitiFact, and AFP—whose focus is on timely and harmful misinformation—we believe CR+ reflects real-world misinformation trends. We will clarify these details in the main paper for greater transparency. Thank you for the feedback.\n\n### AVeriTeC metric\nWe used accuracy as the main metric for AVeriTeC to align with recent follow-up work, where accuracy has become a common evaluation metric for veracity prediction [Singhal et al., 2024; Cao et al., 2023]. The original AVeriTeC paper conditions veracity scoring on alignment between retrieved and gold evidence, which can confound evaluation of general-purpose systems. Following subsequent work, we omit this step to enable broader comparability and support systems that retrieve evidence freely.\n\n### Evaluation “Not Comprehensive”\nYou point out that “evaluation of the proposed method is also not comprehensive” and refer to section \"Claims and Evidence.\" There, however, you write that “the claims made in the submission are supported by clear and convincing evidence.” Please clarify.", "claims_and_evidence": "The paper has several concerns regarding the experimental setup and comparisons made. \n1. The results reported on the AveriTec dataset were not obtained using the default settings; instead, the paper solely relied on accuracy as the evaluation metric. \n2. The comparison with GPT-4o seems unfair. A fair comparison would involve contrasting GPT-4o in a multi-turn setup with DEFAME in a multi-turn setup, as well as comparing GPT-4o in its current single-turn setup with DEFAME in a single-turn setup. \n3. Lastly, the paper failed to compare its approach with existing efforts that combine Large Vision-Language Models (LVLMs) and RAG (see section Essential References Not Discussed).", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-suited for the problem of automated fact-checking, particularly in handling multimodal claims and evidence.", "theoretical_claims": "This paper did not provide any proofs for theoretical claims.", "experimental_designs_or_analyses": "See the section \"Claims And Evidence\" above.", "relation_to_broader_scientific_literature": "In summary, DEFAME's contributions build upon and advance the state of the art in automated fact-checking。", "essential_references_not_discussed": "It appears that this paper has overlooked several important related works. The paper claims that DEFAME is the first multimodal Automated Fact-Checking system capable of handling multimodal claims while also retrieving and processing multimodal evidence. However, there are many existing works that employ Large Vision-Language Models (LVLMs) with knowledge retrieval for multimodal misinformation detection.\n\n[1] SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection. CVPR 2024.\n[2] MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs. ICLR 2025 (but arXiv on 13 Jun 2024)\n[3] LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. arXiv 2024\n[4] Multimodal Misinformation Detection using Large Vision-Language Models. CIKM 2024", "other_strengths_and_weaknesses": "1. While DEFAME represents an advancement in multimodal fact-checking, its contributions primarily lie in the workflow and prompt engineering. However, I did not see significant differences between the proposed framework and existing works that combine LVLMs and RAG. The only additions are reverse image search and geolocation, but these two actions only provide limited improvements.\n\n2. The paper claims that DEFAME is the first multimodal Automated Fact-Checking system capable of handling multimodal claims while also retrieving and processing multimodal evidence. However, there are many existing works that employ LVLMs with knowledge retrieval for multimodal misinformation detection.\n\n3. Additionally, many details of the proposed dataset ClaimReview 2024+ are missing. The evaluation of the proposed method is also not comprehensive, as discussed in the section \"Claims and Evidence\" above.", "other_comments_or_suggestions": "See above.", "questions_for_authors": "No", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sRNxYbGoiR", "reviewer": "Reviewer_bvvH", "summary": "This paper tackles the challenge of scalable and explainable fact-checking in the presence of disinformation, particularly in multimodal contexts. The authors propose DEFAME, a modular, zero-shot multimodal large language model (MLLM) pipeline for open-domain claim verification. Unlike prior methods that are either text-only or overly reliant on parametric knowledge, DEFAME operates through a six-stage dynamic tool selection process, incorporating both textual and visual evidence to generate structured, explainable verification reports. Extensive evaluation on VERITE, AVERITEC, and MOCHEG demonstrates DEFAME’s superiority over existing fact-checking models, setting a new state-of-the-art for uni- and multimodal fact-checking. Furthermore, the authors introduce CLAIMREVIEW2024+, a new benchmark that ensures post-GPT-4O knowledge cutoff validity, highlighting DEFAME’s temporal generalizability and real-time fact-checking capabilities, significantly outperforming the GPT-4O Chain-of-Thought baseline.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Claim on Novelty / Related Work\nThanks for pointing out SNIFFER, MMD-Agent (from the MMFakeBench paper), and LEMMA. Please refer to our response to Reviewer K7Ta.\n### No Theoretical Claims\nWe believe that absence of theoretical claims is not unusual for papers under “Application-Driven Machine Learning”, the ICML topic that we have submitted to. At the same time, we offer a more formal mathematical representation of the framework below.\n\n### No Supplementary Material, Details for CR+\nPlease refer to Appendix Sections A–L (pp. 16 ff.), which contain extensive additional material. Some of your questions are addressed in Appendix J. We also point you to our response to Reviewer K7Ta on “ClaimReview2024+ Details”, where these details are further elaborated.\n### Originality and Theoretical Contribution\nDEFAME’s originality lies in its dynamic, modular integration of retrieval tools for handling both multimodal claims **and** multimodal evidence—capabilities not jointly supported by prior work. While it builds on established components like CoT prompting and tool use, DEFAME combines them into a unified framework that supports zero-shot fact-checking across a diverse set of benchmarks: AVeriTeC (text claims & text evidence), MOCHEG (text claims and multimodal evidence), VERITE (multimodal claims and potentially multimodal evidence), ClaimReview2024+ (both uni- and multimodal claims with potentially multimodal evidence). To our knowledge, it is the only system capable of operating across all these scenarios, while previous methods are specialized and typically incompatible with at least one modality setting (e.g., requiring only text, only images, or static corpora). We will make this contribution more explicit.\n\n### Formal Representation of DEFAME\n\nWe appreciate the suggestion to formalize the DEFAME pipeline. We agree that a formal view helps clarify the role of each stage.\n\nLet $\\mathcal{T}$ and $\\mathcal{I}$ be the spaces of text and images. Define $\\mathcal{M} := (\\mathcal{T} \\cup \\mathcal{I})^*$ as the space of multimodal sequences, and $\\mathcal{Y}$ the space of verdict labels. DEFAME is a function:\n\n$$\n\\mathcal{F} : \\mathcal{M} \\rightarrow \\mathcal{M} \\times \\mathcal{Y}, \\quad \\mathcal{F}(c) = (R_\\text{out}, y_\\text{out}),\n$$\n\nwhere, given a claim $c \\in \\mathcal{M}$, the output consists of a report $R_\\text{out}$ containing the fact-check, and a predicted verdict $y_\\text{out}$. DEFAME proceeds iteratively up to $N$ steps as follows:\n\n- $(R^{(i+1)}, y^{(i+1)}) := \\mathcal{F}_\\text{iter}(R^{(i)})$\n- $i^* := \\min \\\\{ i \\leq N \\mid y^{(i)} \\ne \\text{NEI} \\text{ or } i = N \\\\}$\n\nFinal outputs:\n\n- $R_\\text{out} := \\mathcal{S}_6(R^{(i^*)})$ (justification)\n- $y_\\text{out} := y^{(i^*)}$\nThe justification stage $\\mathcal{S}_6$ appends a rationale to the final report.\n\nEach iteration\n$$\\mathcal{F}_\\text{iter} = \\mathcal{S}_5 \\circ \\mathcal{S}_4 \\circ \\mathcal{S}_3 \\circ \\mathcal{S}_2 \\circ \\mathcal{S}_1$$\nconsists of:\n1. Planning ($\\mathcal{S}_1$): Select actions $A \\subseteq \\mathcal{A}$ based on $R^{(i)}$\n2. Execution ($\\mathcal{S}_2$): Retrieve evidence $E := \\\\{ \\tau(a) \\mid a \\in A \\\\}$, where $\\tau$ is a tool executing corresponding action $a$.\n3. Summarization ($\\mathcal{S}_3$): $R_1^{(i)} := \\sigma(E, R^{(i)})$, where $\\sigma$ summarizes evidence $E$ conditioned on the current report $R^{(i)}$ and appends it to the report.\n4. Develop ($\\mathcal{S}_4$): $R_2^{(i)} := \\mathcal{S}_4(R_1^{(i)})$, where $\\mathcal{S}_4$ is a generative model that performs structured reasoning and expands the report with the generated NLI sequence.\n5. Verdict Prediction ($\\mathcal{S}_5$): $(R_3^{(i)}, y^{(i)}) := \\mathcal{S}_5(R_2^{(i)})$, where $\\mathcal{S}_5$ is a classifier over multimodal sequences, returning a verdict $y^{(i)} \\in \\mathcal{Y}$ and an updated report $R_3^{(i)} \\in \\mathcal{M}$ which is the input report $R_2^{(i)}$ expanded by a summary of the key takeaways from the report alongside with the verdict.\n\nWe welcome feedback on whether including this formalization (or an extended/abbreviated version) in the main paper would be helpful.", "claims_and_evidence": "Claim: The paper claims that DEFAME is the first multimodal Automated Fact-Checking (AFC) system capable of handling multimodal claims while also retrieving and processing multimodal evidence. \nQuestion: However, based on my literature review using Google Scholar, there are existing studies [r1], [r2], [r3] that employ Large Vision-Language Models (LVLMs) with knowledge retrieval for multimodal misinformation detection.\n\n[r1] Qi P, et al. SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection. CVPR, 2024.\n[r2] Liu X, et al. Mmfakebench: A mixed-source multimodal misinformation detection benchmark for lvlms. ICLR 2025.\n[r3] Xuan K, et al. LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. 2024.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well-aligned with the problem of multimodal fact-checking and effectively demonstrate the capabilities of DEFAME in real-world misinformation detection.\n\n1.\tDiverse Benchmark Selection. The authors evaluate DEFAME on text-only (AVERITEC), image-text (VERITE), and multimodal (MOCHEG) datasets, covering varied fact-checking challenges.\n2.\tThe introduction of CLAIMREVIEW2024+ is commendable, as it tests DEFAME’s ability to verify claims beyond the GPT-4O knowledge cutoff, addressing potential data leakage issues.", "theoretical_claims": "The paper primarily focuses on the practical application of multimodal fact-checking rather than developing new theoretical foundations or formal proofs. As such, no explicit theoretical claims or mathematical proofs are presented that require verification. Therefore, this raises a potential concern regarding the lack of formal theoretical justification for some of the proposed design choices in DEFAME.", "experimental_designs_or_analyses": "Strengths of Experimental Design\n1.\tUse of Diverse and Established Benchmarks. The evaluation leverages three well-known datasets (VERITE, AVERITEC, MOCHEG) to assess DEFAME's text, image-text, and multimodal fact-checking capabilities.\n2.\tAblation Studies for Key Components. The six-stage verification process is systematically analyzed through component ablations (removal of Web Search, Image Search, Geolocation, Reverse Image Search).\n\nWeakness of Experimental Design:\n1.\tCLAIMREVIEW2024+ is an important contribution, but the dataset construction methodology lacks clarity. Unclear aspects: What criteria were used for claim selection? How were fact-checking labels assigned and validated? Does it truly reflect real-world misinformation trends? A more detailed dataset creation process would improve trust and reproducibility.", "relation_to_broader_scientific_literature": "The paper provides a clear review of the three key AFC components: Claim detection & extraction Evidence retrieval Verdict prediction\nIt references leading AFC models, such as: Text-only AFC systems (e.g., FEVER, AVERITEC, FACTCHECK-BENCH) Multimodal AFC systems (e.g., VERITE, MOCHEG, NEWSCLIPPINGS).\n\nDEFAME extends prior work: Unlike text-only AFC systems, DEFAME incorporates multimodal evidence retrieval and reasoning. Unlike previous multimodal AFC approaches, DEFAME performs dynamic evidence retrieval rather than relying solely on pre-annotated evidence.", "essential_references_not_discussed": "Two key related works—Sniffer (r1 SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection. CVPR, 2024.) and MMFakeBench (r2 Mmfakebench: A mixed-source multimodal misinformation detection benchmark for lvlms. ICLR 2025.)—are missing from the current discussion, and they are essential for understanding the context of multimodal fact-checking and misinformation detection.", "other_strengths_and_weaknesses": "The paper has notable strengths in terms of application significance, empirical rigor, and modular system design, but also has some weaknesses related to originality, clarity of contributions, and unexplored limitations.\nStrengths:\n1.\tStrong Empirical Validation. The evaluation is conducted on three well-established AFC benchmarks (VERITE, AVERITEC, MOCHEG) and a new dataset (CLAIMREVIEW2024+), providing comprehensive empirical insights.\n2.\tModular and Scalable System Design. DEFAME is modular, allowing flexibility in integrating different retrieval tools (Web Search, Image Search, Reverse Image Search, Geolocation).\n\nWeaknesses for Improvement\n1. Originality Could Be Better Clarified. While DEFAME combines existing techniques innovatively, it does not introduce a fundamentally new theoretical model. The paper does not sufficiently differentiate DEFAME’s retrieval mechanism from prior RAG-based AFC models\n2. If possible, provide a more formal description of the six-stage verification pipeline to enhance clarity.\n3. The mathematical formulation of DEFAME’s retrieval pipeline and reasoning steps could be clearer.\n4. The dataset construction details for CLAIMREVIEW2024+ are not fully transparent.", "other_comments_or_suggestions": "Line 48 (Most multimodal claim verification systems cannot even retrieve the evidence needed to verify a claim (Fu et al., 2024; Vo-Hoang et al., 2024; Tang et al., 2024),.) contains an extraneous comma that was mistakenly added.", "questions_for_authors": "Overall, this paper presents a well-structured approach to multimodal fact-checking with strong empirical validation. However, to align with ICML’s emphasis on theoretical rigor, I would like to ask the following critical question regarding the methodological formalization in Section 3:\n Can the authors introduce a more formal theoretical and mathematical representation of the method in Section 3?\n* The current presentation of DEFAME’s six-stage verification process is primarily descriptive and algorithmic, but lacks a formal mathematical framework. Incorporating a structured formalization would significantly enhance the paper’s impact.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k9RTQVfID2", "reviewer": "Reviewer_3bbT", "summary": "The authors introduce DEFAME, an automated fact-checking framework designed to process multimodal claims using multimodal evidence. DEFAME operates within a zero-shot MLLM pipeline structured into six stages: action planning, action execution (via multimodal web retrieval and GeoClip tool use), result summarization, reasoning about claim veracity, verdict prediction, and verdict justification. Evaluated on three established multimodal fact-checking datasets (AVERITEC, MOCHEG, VERITE) and a newly proposed dataset (ClaimReview2024+), DEFAME outperforms existing MLLMs and multimodal fact-checking approaches while generating higher-quality fact-checking reports compared to the base MLLM with CoT prompting.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank for the time invested into the review. Please find our response to your concerns below.\n### First to handle multimodal claims and evidence\nThe work by Tahmasebi et al. (CIKM 2024), i.e. LVLM4F, was covered in our paper, most notably in the prior work overview (Table 1) and as a baseline in Table 3. Critically, LVLM4FV **is not designed to handle multimodal claims**: “Given a **textual** claim 𝑄 and a corpus of evidences C as input [...]”, p. 3. \n### Key novelty\nPlease refer to our response to Reviewer bvvH (“Originality and Theoretical Contribution”) for more details on the novelty of DEFAME.\n### Prior work\nThanks for pointing out SNIFFER. Please refer to our response to Reviewer K7Ta (“Comparison to Prior Work”).\n### ”Incomplete” Table 3\nThe other methods in Table 3 have not been evaluated on all benchmarks for several methodological reasons. Most importantly, the methods have different task specializations, targeting only a particular subtask of fact-checking: CFR, GPT-CoT, LVLM4FV, and MetaSum are limited to text-only claims (not provided by VERITE and partially CR+). CHASMA and AITR focus solely on OOC detection and both require visual input, making them inapplicable to text-only claims (like in AVeriTeC, MOCHEG, and partially CR+). For CFR, the code and model weights have not been publicly released. GPT-CoT requires gold evidence as input to achieve the reported competitive numbers, not available for VERITE and CR+, where evidence retrieval is considered an integral part of the fact-checking task. Moreover, GPT-CoT mainly builds on GPT-3.5-Turbo, implying that the much stronger GPT-4o (CoT) baselines provide a better comparison than GPT-CoT. CHASMA lacks a publicly available, trained model. Furthermore, CHASMA and AITR both require training - which is impossible for CR+ as there is no training data available. LVLM4FV, MetaSum, and AITR require a predefined evidence corpus for retrieval, which is infeasible to create for CR+.\n\nThus, it is not possible to run the previous methods on the benchmarks they were not designed for (or if it is technically feasible, it will produce meaningless results). With DEFAME, we deliver a method that - despite its generality - is able to beat even the specialized methods.\n### Inconsistency in metrics\nMetrics (F1, accuracy) match those used in the original benchmarks and prior work for consistent intra-benchmark comparison. If the reviewer finds it helpful, we are open to adding F1 score as an additional metric to AVeriTeC and CR+ or, alternatively, add accuracy to MOCHEG.\n### Efficiency\nWe agree that DEFAME has high token usage and acknowledge that there is room to reduce it. As correctly anticipated, the high token consumption is due to the processing of external evidence. External evidence can incorporate entire webpages, full PDFs, and other long documents that Firecrawl can turn into Markdown representation. We truncate inputs only when they exceed the maximum context window of the MLLM, which is 128k tokens for the GPT models used. Since the GPT baselines do not process any external evidence, unsurprisingly, their token consumption is fairly low - *at the cost of random-like performance for unseen claims*. Table 7 also reveals that the integration of planning (DEFAME with GPT-4o) reduces token consumption by about 20K tokens (almost a third) compared to the planning-ablated variant “Static Actions,” which executes all available actions. Since efficiency was not a goal of DEFAME, we offer to add it as a future direction to the discussion section.\n\nIt is hard to compare DEFAME’s efficiency with the methods in Table 3 due to (a) no reported resource usage (e.g., tokens or FLOPs), and (b) significant differences in architecture; also many are specialized for sub-tasks like OOC detection or evidence summarization and do not perform retrieval. Thus, direct efficiency comparison is not meaningful.", "claims_and_evidence": "Certain claims made in the submission lack sufficient supporting evidence:\n\n1. Novelty of DEFAME: The authors claim that DEFAME is \"the first multimodal AFC system that can handle multimodal claims as well as retrieve and process multimodal evidence\" (Lines 72–74). However, this is fundamentally inaccurate, as prior work [1] has already implemented multimodal retrieval (both text and image) for multimodal misinformation detection and was evaluated on the same MOCHEG dataset. This undermines the claimed novelty of DEFAME.\n\n2. State-of-the-art results: The claim that DEFAME \"establishes new state-of-the-art results on three diverse and widely used benchmarks\" (Lines 78-80) is only partially supported by Table 3. The table presents incomplete comparisons, as only GPT-4o and GPT-4o with CoT are evaluated on all four datasets, while other baselines are tested on at most two datasets. This weakens the claim that DEFAME definitively outperforms prior approaches.\n\n\n[1] Tahmasebi et al., Multimodal Misinformation Detection using Large Vision-Language Models, arXiv:2407.14321, 2024. (Published at CIKM 2024)", "methods_and_evaluation_criteria": "The authors evaluate DEFAME on four datasets (three existing and one newly introduced), which represents a comprehensive selection. However, the inconsistency in evaluation metrics across datasets is not justified. Specifically, while Accuracy is reported for most datasets, F1 score is used for MOCHEG, without explanation.", "theoretical_claims": "N/A: the paper does not contain theoretical claims.", "experimental_designs_or_analyses": "While the authors claim that DEFAME achieves state-of-the-art performance across diverse benchmarks, the experimental results in Table 3 are incomplete. Specifically, apart from GPT-4o and GPT-4o with CoT, all other baselines are evaluated on at most two out of the four datasets, limiting the robustness of the comparison. Furthermore, on the newly introduced ClaimReview2024+ dataset, DEFAME is only compared against base MLLM approaches that lack task-specific framework design, web retrieval, and tool use, making its superiority expected. These gaps weaken the claim that DEFAME establishes a new state of the art.\n\nAdditionally, Table 7 reveals significant efficiency concerns. DEFAME with GPT-4o requires around 28× the time and 21× the input tokens compared to GPT-4o with CoT, and even its ablated variants remain highly resource-intensive. This raises serious questions about DEFAME’s practicality for real-world deployment.", "relation_to_broader_scientific_literature": "Compared to the broader scientific literature, the conceptual and technical contributions of this paper remain highly limited.\n\nThe claimed key novelty of DEFAME -- being \"the first multimodal AFC system that can handle multimodal claims as well as retrieve and process multimodal evidence\" (Lines 72–74) -- is inaccurate. Prior work [1] has already implemented multimodal evidence retrieval (text and image) for misinformation detection and was evaluated on the same MOCHEG dataset.\n\nDEFAME is a zero-shot MLLM pipeline for standard multimodal fact-checking, but its retrieval paradigm closely resembles existing retrieval-augmented MLLM approaches, such as [1] and MMD-Agent [2]. While DEFAME's integration of reverse image search and geolocation is novel in execution, these contributions are incremental given the lack of originality in problem formulation and overall framework design.\n\n[1] Tahmasebi et al., Multimodal Misinformation Detection using Large Vision-Language Models, arXiv:2407.14321, 2024. (Published at CIKM 2024)\n\n[2] Liu et al., MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs. arXiv: 2406.08772, 2024. (Published at ICLR 2025)", "essential_references_not_discussed": "Please refer to References [1] and [2] under \"Relation To Broader Scientific Literature\".\n\nAdditionally, the authors fail to discuss SNIFFER [3], a representative work that leverages MLLMs for explainable out-of-context misinformation detection. Given SNIFFER’s focus on MLLM-empowered explainable detection, its omission further weakens the discussion of related work.\n\n[3] Qi et al., SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection. CVPR 2024.", "other_strengths_and_weaknesses": "Please refer to comments under \"Relation To Broader Scientific Literature\", \"Methods And Evaluation Criteria\", and \"Experimental Designs Or Analyses\".", "other_comments_or_suggestions": "N/A", "questions_for_authors": "1. Completion of Table 3: The performance of existing approaches beyond GPT-4o and GPT-4o with CoT remains unclear across all four datasets, and the evaluation metrics are inconsistent (Accuracy vs. F1 score). Specifically, how do retrieval-augmented approaches perform on the newly constructed ClaimReview2024+ dataset compared to DEFAME? The lack of such comparisons weakens the claim of DEFAME’s superiority.\n\n2. Efficiency of Existing MLLM-Empowered Approaches: Table 7 shows that DEFAME consumes significantly more tokens and execution time than MLLM prompting, which is expected due to its evidence retrieval process. However, there is no direct comparison with other task-specific MLLM-empowered approaches that also utilize evidence retrieval. Without this, it remains unclear how DEFAME’s efficiency compares to competing baselines.\n\n3. Explanation Quality Comparison: In Section 4.6, DEFAME’s explanation quality is only compared against bare MLLM prompting (GPT-4o with CoT), which lacks external evidence access. Given this, DEFAME’s superior performance is expected rather than insightful. A missing analysis is how DEFAME’s generated explanations compare to those from other retrieval-augmented, task-specific MLLM approaches.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes a multi-stage pipeline for multimodal fact-checking and a benchmark of claims from after the GPT-4 training data cutoff that are less likely to be verified through memorization. There are no lingering reviewer concerns about whether the work is technically solid, and there is a comprehensive evaluation on existing multimodal fact-checking benchmarks that indicates respectable empirical improvements for this domain. However there were concerns over lack of innovation and similarity to existing RAG-LLM solutions. In their rebuttal the authors have somewhat convincingly differentiated their work, and I do agree this paper has potential for impact. I would suggest the authors include statistical significance testing for the main results before publication.
Decision: Accept (poster)