arxiv: 2605.12436 · v1 · submitted 2026-05-12 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction

Amine Trabelsi, Islam Eldifrawi, Shengrui Wang

Pith reviewed 2026-05-13 04:38 UTC · model grok-4.3

classification 💻 cs.AI

keywords automated fact-checkinghallucination detectionmisinformationfact correctionknowledge base updatingconversational AI

0 comments

The pith

CAAFC detects and corrects factual errors and hallucinations in claims, conversations, and dialogues using primary sources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CAAFC, a framework for automated fact-checking that seeks to align more closely with professional practices than existing systems. It processes claims as well as conversations and dialogues to identify misinformation and AI hallucinations. The system not only detects these issues but also corrects them by supplying justifications drawn from primary information sources and can refresh its evidence and knowledge bases with recent contextual details. This matters because the volume of online and AI-generated content makes manual checking impractical, so an effective automated approach could help verify information more scalably and accurately.

Core claim

CAAFC surpasses state-of-the-art AFC and hallucination detection systems across multiple benchmark datasets. It operates on claims, conversations, and dialogues to detect factual errors and hallucinations, corrects them by providing actionable justifications supported by primary information sources, and updates evidence and knowledge bases by incorporating recent and contextual information when necessary.

What carries the argument

The CAAFC framework, which follows a chronological and actionable process for detecting, correcting, and updating facts based on primary sources.

If this is right

CAAFC applies to conversational formats such as dialogues in addition to isolated claims.
It delivers corrections accompanied by justifications from primary sources.
The framework can update its evidence and knowledge bases with new information.
This leads to enhanced reliability in automated fact verification processes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If integrated with generative AI tools, it could reduce the occurrence of hallucinations in real-time outputs.
Testing on live news streams with evolving facts would reveal how well it incorporates recent information.
Similar chronological updating could be applied to other verification tasks like checking scientific claims against latest papers.

Load-bearing premise

The framework can reliably access primary sources and add recent information without introducing new factual errors.

What would settle it

Compare CAAFC's outputs and corrections on a collection of recent claims against independent professional fact-checker results to see if they match in accuracy and source support.

Figures

Figures reproduced from arXiv: 2605.12436 by Amine Trabelsi, Islam Eldifrawi, Shengrui Wang.

**Figure 2.** Figure 2: CAAFC pipeline. Mainly, CAAFC uses the quantized Gemma3-27B as its backbone, however, it can also use any LLM like llama3.3-70B or GPT-OSS120B. The example here is simplified and for illustration purposes. More elaborated examples are in the Appendix 35 not enough evidence, and 38 conflicting evidence).We merged the two classes "not enough evidence" and "conflicting evidence" into a single class labeled "… view at source ↗

**Figure 3.** Figure 3: Density for actionability scores by FinGrAct [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: prompt used for extracting claims and seg [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Prompt used for extracting primary sources. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

**Figure 6.** Figure 6: Prompt used by the fact-checker module [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

**Figure 7.** Figure 7: Prompt used by the actionable justifier module [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Prompt for justification revisory A.4 Automation of evidence update To fully automate the evidence inspection methodology, we zero-shot prompt LLAMA3.3-70B, Gemma3-27B, and GPT-OSS-120B using the prompt shown in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt for evidence comparison to the financial information of a certain company, making it unverifiable through publicly accessible sources such as news articles or web-based documentation. The coverbench evidence: On April 19, 2018, we took delivery of Norwegian Bliss. To finance the payment due upon delivery , we had export financing in place for 80% ( 80 % ) of the contract price .the associated $ 85… view at source ↗

**Figure 10.** Figure 10: Instructions provided to the human annota [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗

read the original abstract

With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-checkers to manually verify the sheer volume of information generated online. Professional fact-checkers have identified several gaps in existing AFC systems, noting a misalignment between how these systems operate and how fact-checking is performed in practice. In this paper, we introduce CAAFC (Chronological Actionable Automated Fact-Checker), a frame-work designed to bridge these gaps. It surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets. CAAFC operates on claims, conversations, and dialogues, enabling it not only to detect factual errors and hallucinations, but also to correct them by providing actionable justifications supported by primary information sources. Furthermore, CAAFC can update evidence and knowledge bases by incorporating recent and contextual information when necessary, thereby enhancing the reliability of fact verification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CAAFC describes a framework for detecting and correcting hallucinations plus misinformation with source updates, but the abstract states superiority claims without any metrics, methods, or evidence.

read the letter

Hi, This paper puts forward CAAFC, a framework meant to handle fact-checking on claims and dialogues by detecting errors or hallucinations, correcting them with source-backed justifications, and updating its knowledge base with new contextual info. The authors highlight gaps in existing automated systems compared to real professional practices. It does well in framing the need for something more actionable and dynamic than pure detection tools. Targeting both misinformation and AI-generated hallucinations shows awareness of current issues. The soft spots are clear though: the abstract asserts better performance than state-of-the-art systems on benchmarks and effective correction, but gives zero metrics, no baseline comparisons, and no outline of how the chronological aspects or source retrieval work. Without that, we can't tell if the central claims are supported or if the updates risk adding inaccuracies. The stress-test is accurate that there's no contradiction in the description itself. This kind of paper is for people building or studying tools for misinformation combat and LLM reliability. A reader focused on practical AI applications for verification could find the high-level ideas useful, provided the full paper has solid experiments. I'd recommend engaging with it through peer review. The topic matters and the authors seem to be addressing real limitations, so referees could help strengthen the evidence and methods if they are there in the manuscript.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces CAAFC (Chronological Actionable Automated Fact-Checker), a framework for detecting and correcting factual errors and hallucinations in claims, conversations, and dialogues. It claims to surpass state-of-the-art AFC and hallucination detection systems across multiple benchmark datasets, provide actionable justifications backed by primary sources, and dynamically update evidence and knowledge bases with recent contextual information to better align with professional fact-checking practices.

Significance. If the superiority claims and correction mechanisms hold under rigorous evaluation, the work could meaningfully advance automated fact-checking by addressing gaps between existing systems and real-world professional practices, particularly in handling dynamic information and AI-generated hallucinations through chronological processing and primary-source grounding.

major comments (2)

[Abstract] Abstract: The central claim that CAAFC 'surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets' is unsupported by any performance metrics, baseline comparisons, evaluation protocols, or results tables, rendering the superiority assertion impossible to assess.
[Abstract] Abstract: The description of CAAFC's ability to detect errors, correct them with 'actionable justifications supported by primary information sources,' and update knowledge bases lacks any methodological details, system architecture, retrieval mechanisms, or verification algorithms, which are load-bearing for evaluating the reliability of primary-source access and error-free updates.

minor comments (1)

[Abstract] The abstract contains a hyphenated 'frame-work' that should be corrected to 'framework' for standard spelling.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, agreeing where the abstract can be strengthened for clarity and self-containment while noting that the full manuscript provides the supporting details.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that CAAFC 'surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets' is unsupported by any performance metrics, baseline comparisons, evaluation protocols, or results tables, rendering the superiority assertion impossible to assess.

Authors: We agree that the abstract, being a high-level summary, does not embed the specific metrics, tables, or protocol details. The full manuscript includes these in the Experiments section, with quantitative comparisons against SOTA AFC and hallucination detection baselines across the cited benchmark datasets, along with the evaluation protocols used. To make the abstract more self-contained and directly address this concern, we will revise it to incorporate key performance highlights and a concise reference to the evaluation setup. revision: yes
Referee: [Abstract] Abstract: The description of CAAFC's ability to detect errors, correct them with 'actionable justifications supported by primary information sources,' and update knowledge bases lacks any methodological details, system architecture, retrieval mechanisms, or verification algorithms, which are load-bearing for evaluating the reliability of primary-source access and error-free updates.

Authors: The abstract is intentionally concise and focuses on capabilities rather than implementation specifics. The manuscript details the system architecture, chronological action processing, primary-source retrieval mechanisms, verification algorithms, and dynamic knowledge-base update procedures in the Methodology and Implementation sections. We acknowledge that a brief methodological overview in the abstract would improve accessibility and will revise the abstract to include a short description of these core components. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents CAAFC as a descriptive framework for chronological actionable fact-checking and hallucination correction. No equations, derivations, fitted parameters, or self-referential constructions appear in the provided text. Claims of surpassing SOTA rest on benchmark performance rather than any internal reduction to inputs by definition or self-citation chains. The work is self-contained as a system proposal with no load-bearing steps that collapse to tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract provides no explicit free parameters, mathematical axioms, or newly invented entities; the framework description rests on high-level domain assumptions about source reliability and benchmark representativeness.

axioms (1)

domain assumption Professional fact-checkers have identified gaps in existing AFC systems regarding misalignment with practical workflows.
Invoked in the abstract as the motivation for the new framework.

pith-pipeline@v0.9.0 · 5477 in / 1419 out tokens · 75213 ms · 2026-05-13T04:38:14.095742+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
CAAFC is a module based frame-work with six modules using quantized LLMs... The Extractor Segmentor... Primary Chronological Evidence Retriever... Fact-Checker... Actionable Justifier... Actionability Evaluator... Justification Revisory
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
CAAFC extract timestamped, ordered and updated evidence from the web... directive search... Google AI Mode... chronologically ordered evidence

Reference graph

Works this paper leans on

48 extracted references · 48 canonical work pages

[1]

InFindings of the Association for Computational Linguistics: EMNLP 2025, pages 9882–9901, Suzhou, China

FinGrAct: A framework for FINe-GRrained evaluation of ACTionability in explainable automatic fact-checking. InFindings of the Association for Computational Linguistics: EMNLP 2025, pages 9882–9901, Suzhou, China. Association for Com- putational Linguistics. Eleni Fysikoudi, Sharid Loáiciga, and Asad Sayeed

work page 2025
[2]

InProceedings of the First BabyLM Workshop, pages 488–495

Active curriculum language modeling over a hybrid pre-training method. InProceedings of the First BabyLM Workshop, pages 488–495. Zhijiang Guo, Michael Schlichtkrull, and Andreas Vla- chos. 2022. A survey on automated fact-checking. Transactions of the association for computational linguistics, 10:178–206. Nannan Huang and Xiuzhen Zhang. 2021. Evaluation ...

work page arXiv 2022
[3]

Mark Rothermel, Tobias Braun, Marcus Rohrbach, and Anna Rohrbach

Temporal graph network: Hallucination detec- tion in multi-turn conversation.arXiv e-prints, pages arXiv–2601. Mark Rothermel, Tobias Braun, Marcus Rohrbach, and Anna Rohrbach. 2024. InFact: A strong baseline for automated fact-checking. InProceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 108–112, Miami, Florida, USA. As-...

work page 2024
[4]

Prefer sources that: • Produce original data • Hold legal, scientific, or operational authority • Are required for real-world systems to function

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[5]

Rank sources by authority strength, not popularity

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[6]

Distinguish between measurement authority, regulatory authority, and theo- retical authority

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[7]

Avoid secondary explainers, media articles, and encyclopedias

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[8]

the claim: {claim} Figure 5: Prompt used for extracting primary sources

Your output should be a list of primary sources and the justification for their selection. the claim: {claim} Figure 5: Prompt used for extracting primary sources. Role: You are a fact-checking assistant. Your task is to analyze a claim and determine the truthfulness of its sub-components based on the provided evidence. You are given a list of sub-claims,...

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[9]

Compare each subclaim with the provided evidence

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[10]

Base your judgment only on the given evidence

Check whether the evidence supports, contradicts, or does not mention the subclaim. Base your judgment only on the given evidence. Do not rely on prior knowledge

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[11]

true" if The evidence directly supports the subclaim. -

Assign one of three labels to each subclaim: - "true" if The evidence directly supports the subclaim. - "false" if The evidence directly contradicts the subclaim. - "unverifiable" if The evidence is insufficient, unclear, or unrelated to verify the subclaim

work page
[12]

subclaims

Your output is a json object containing only three fields which are the sub- claim, label, and the explanation. Here is an example of the output format: {"subclaims": [{"text": "First subclaim here.","label": "true | false | unverifi- able","justification": "Brief explanation of how the evidence supports, contra- dicts, or fails to verify this subclaim."}...

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[13]

subclaims

Here is an example of the whole process: sub-claims: [’Paris is the capital of Germany’, ’Paris has the Eiffel Tower.’] evidence: Paris is the capital of France. The Eiffel Tower is located in Paris. output: {"subclaims": [{"text": "Paris is the capital of Germany.","label": "false","justification": "The evidence states Paris is the capital of France, not...

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[14]

Your output is restricted to the json format mentioned in step 4

Do not add the instructions or anything from the prompt. Your output is restricted to the json format mentioned in step 4. claim: {claim} evidence: {evidence} Figure 6: Prompt used by the fact-checker module You are given a claim, evidence (reference information used to judge the claim), and a JSON object. The json object contains the claim divided into s...

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[15]

Clearly reference which subclaims strengthen or weaken the claim

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[16]

Explain how the evidence supports or contradicts the claim at a structural level

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[17]

Highlight any gaps or uncertainties caused by unverifiable subclaims

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[18]

Describe how false subclaims impact the credibility of the overall claim

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[19]

Provides a corrected version of the whole claim

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[20]

Only analyze and integrate what is already in the JSON object

Do not rewrite or re-evaluate the subclaims. Only analyze and integrate what is already in the JSON object

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[21]

subclaims

Here is an example of the whole process: claim: Paris is the capital of Germany and it has the Eiffel Tower. evidence: Paris is the capital of France. The Eiffel Tower is located in Paris. json object: "subclaims": ["text": "Paris is the capital of Germany.","label": "false","justification": "The evidence states Paris is the capital of France, not Germany...

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[22]

Your output is restricted to the json format mentioned in step 7

Do not add the instructions or anything from the prompt. Your output is restricted to the json format mentioned in step 7. claim: {claim} evidence: {evidence} json object: {json_object} Figure 7: Prompt used by the actionable justifier module Role:You are an expert fact-checking editor. Your task is to improve the actionability of justifications by explic...

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[23]

3.Preserve all correct reasoning already present

Clearly highlight previously unmentioned errors in the original justification. 3.Preserve all correct reasoning already present. 4.Do not introduce new facts or evidence beyond what is implied by the feed- back

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[24]

Use clear, concise, and structured language suitable for professional fact- checking reports

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[25]

Produce a single revised justification

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[26]

the feedback says

Ensure the reasoning clearly connects errors, corrections, final verdict. 8. Avoid meta-commentary (e.g., “the feedback says. . . ”). Original Justification: {justification} Feedback on Missing Errors and Corrections: {feedback} Figure 8: Prompt for justification revisory A.4 Automation of evidence update To fully automate the evidence inspection method- ...

work page 2018
[27]

Compare the two evidences only with respect to the given claim

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[28]

evidence_1

Select one of the following as the better evidence: -"evidence_1" - "evidence_2" -"tie" (use only if they are equally strong) 3. Provide a concise but clear justification

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[29]

more context

If the reason is not “more context” or “more updated information”, use "other" and explain briefly

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[30]

Your output is only restricted to the json format mentioned

Do not add the instructions or anything from the prompt. Your output is only restricted to the json format mentioned

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[31]

better_evidence

Output Format (JSON only): { "better_evidence": "evidence_1 | evidence_2 | tie", "reason_category": "more_context | more_updated_information | other", "reason": "Brief explanation justifying why this evidence is better for assessing the claim." } claim: {claim} evidence 1: {evidence1} evidence 2: {evidence2} Figure 9: Prompt for evidence comparison to the...

work page 2018
[32]

crawlers

Crawling: Google uses automated programs called "crawlers" or "bots" to discover new and updated pages on the web by following links and reading sitemaps

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[33]

This process involves understanding the page’s content, images, and key elements like title tags and headings

Indexing: The information gathered by the crawlers is analyzed, and the content is stored in Google’s massive index (a digital library). This process involves understanding the page’s content, images, and key elements like title tags and headings. 3

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[34]

4 Key Ranking signalsWhile Google uses a vast number of signals, the most important signals gen- erally fall into these categories:

Ranking: When a user enters a search query, Google’s ranking systems sort through the in- 2https://www.semrush.com/blog/Google-search-a lgorithm/ 3https://blog.photobiz.com/blog-post/breaking -down-Googles-search-algorithm dexed pages and order them based on rele- vance and quality to that specific query and user context. 4 Key Ranking signalsWhile Google...

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[35]

The content should use relevant keywords naturally in headings, body text, and title tags

Relevance and Search Intent: Google first es- tablishes the intent behind a user’s query (e.g., informational, navigational, transactional) and prioritizes pages that are most likely to satisfy that intent. The content should use relevant keywords naturally in headings, body text, and title tags

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[36]

Your Money or Your Life

Content Quality and E-E-A-T: High-quality, original, and helpful content is crucial. Google emphasizes E-E-A-T (Experience, Ex- pertise, Authoritativeness, and Trustworthi- ness), especially for sensitive topics ("Your Money or Your Life" topics). Content that offers unique insights, is well-researched, and regularly updated tends to rank higher

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[37]

votes of confidence

Backlinks (Authority): Backlinks, or links from other websites to a page, act as "votes of confidence." Links from established, high- authority websites significantly boost a page’s perceived credibility and authority

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[38]

unverifiable

Context and Personalization: Results are cus- tomized based on the user’s location, lan- guage, device type, and search history to pro- vide the most relevant information. AI on Google Search summarizes search engine results. Google has integrated Gemini 3 Pro into its search experience to summarize complex infor- mation. 5 It changes search from a list o...

work page 2020
[39]

Misleading interpretation: The claim is mis- leading because Biden took a hypothetical sce- nario from a statistical model and presented it as a concrete and achievable reality. The study, conducted by Columbia University’s Mailman School of Public Health, estimated that if social distancing and other mitigation efforts had begun just one week earlier, th...

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[40]

Extrapolation issues: Biden’s figure of 160,000 was a projection based on the study’s early findings, and it expanded a narrow hy- pothetical to a broader, unsubstantiated claim about total preventable deaths

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[41]

In the “false

Model limitations: Statistical models rely on many assumptions. The Columbia study did not account for all the FACTors involved in a complex public health crisis, and it is impos- sible to know exactly how many lives would have been saved with a different response. " In the “false” claim category, in 27 instances, all three models consistently predicted t...

work page 2023
[42]

robotic,

E-E-A-T Framework: Raters evaluate content based on Experience, Expertise, Authorita- tiveness, and Trustworthiness. They manually flag content that feels "robotic," unoriginal, or factually incorrect

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[43]

thumbs up/down

Side-by-Side Testing: Humans compare two different AI responses 7 to the same prompt and vote on which is more helpful and accu- rate to refine the underlying algorithms. In addition Google deploys several "live" systems work to maintain reliability: • Knowledge Graph Cross-Referencing: For factual queries, the system can cross-check claims against Google...

work page 2024
[44]

In addition, there is a direct channel (emails) for them to You are asked to perform two sequential tasks:

Clear Annotation Guidelines – annotators have strict, well-defined rules. In addition, there is a direct channel (emails) for them to You are asked to perform two sequential tasks:

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[45]

Evidence Comparison Task:Compare two pieces of evidence (Evi- dence 0 and Evidence 1) related to the same claim and determine which one is better based on specific evaluation criteria mentioned later

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[46]

You must complete Task 1 first, then proceed to Task 2

Claim Veracity Evaluation Task:Using the preferred evidence from Task 1, determine the veracity of the claim. You must complete Task 1 first, then proceed to Task 2. Task 1 Evidence Comparison You will receive a claim, evidence 0, and evidence 1. Your goal is to de- termine which evidence is stronger and more reliable for fact-checking the claim • Step 1:...

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[47]

true and false

Objective or Easy-to-Classify Data – Tasks with minimal ambiguity (e.g., labeling with ’0, or ’ labels like "true and false") often lead to high agreement

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[48]

A recruitment email was sent to postgraduate students and three annotators volunteered for this task

Annotators with similar backgrounds tend to agree more than crowd-sourced annotators. A recruitment email was sent to postgraduate students and three annotators volunteered for this task. Given this, we can assert that their annotations were conducted solely based on their understanding of the provided instructions. Next, we evaluate agreement between the...

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