Grounding Fallacies Misrepresenting Scientific Publications in Evidence (2025)

Abstract: Health-related misinformation claims often falsely cite a credible biomedical publication as evidence. These publications only superficially seem to support the false claim, when logical fallacies are applied. In this work, we aim to detect and to highlight such fallacies, which requires assessing the exact content of the misrepresented publications. To achieve this, we introduce MissciPlus, an extension of the fallacy detection dataset Missci. MissciPlus extends Missci by grounding the applied fallacies in real-world passages from misrepresented studies. This creates a realistic test-bed for detecting and verbalizing fallacies under real-world input conditions, and enables new and realistic passage-retrieval tasks. MissciPlus is the first logical fallacy dataset which pairs the real-world misrepresented evidence with incorrect claims, identical to the input to evidence-based fact-checking models. With MissciPlus, we i) benchmark retrieval models in identifying passages that support claims only with fallacious reasoning, ii) evaluate how well LLMs verbalize fallacious reasoning based on misrepresented scientific passages, and iii) assess the effectiveness of fact-checking models in refuting claims that misrepresent biomedical research. Our findings show that current fact-checking models struggle to use misrepresented scientific passages to refute misinformation. Moreover, these passages can mislead LLMs into accepting false claims as true.

## introduction health-related misinformation has caused significant harm in our society @xcite . human fact-checking (hfc), which is timeconsuming, must prioritize the most impactful claims and struggles with the rapid spread of misinformation @xcite . two main approaches can automatically passage 1 passage 2 passage ... passage n passage 1 passage n original passages from study (missciplus) output 2. generate 1. select study supports claim provided study link hydroxychloroquine is a cure for covid-19. figure 1 : we link the paraphrased context from missci to real-world passages. the llm must (i) find relevant passages from the original study and (ii) generate a fallacious premise to (falsely) support the claim. the missci dataset @xcite addresses implicit fallacious reasoning by reconstructing the fallacious logical argument, explicitly verbalizing the fallacies that led to the incorrect claim. however, missci provides pre-selected and simplified short phrases (as shown in figure 1 ) instead of the actual misrepresented study as evidence. in a real-world scenario the relevant parts of the misrepresented study are not presented in this form. here, the models must first (i) identify the relevant passages within the entire misrepresented study and (ii) verbalize the fallacious reasoning based on the original scientific text. to address this, we present missciplus ( §4), an extension of missci with 2,257 human-annotated links between the simplified phrases from missci and the real-world passages from the misrepresented publications. figure 1 shows (parts of) a fallacious logical argument from missci for the claim that "hydroxychloroquine is a cure for covid-19". each claim in missci has a kernel of truth which is anchored in the study's content ("chloroquine reduced infection of the coronavirus"), denoted as the accurate premise in green. however, additional information from the same study ("the study used cell cultures for their experiments") undermines the claim's validity and reveals a reasoning gap. the relevant content from the study that is needed to identify this reasoning gap between claim and study is referred to as the publication context. the reasoning gap between the study's content and the claim as a conclusion indicates the fallacious reasoning. to bridge this reasoning gap the model must verbalize a fallacious premise ("the results can be transferred to humans because the human body consists of cell structure."), blue in figure 1 , and classify the fallacy associated with this fallacious premise ("fallacy of composition"). unlike missci, where the accurate premise and publication context are provided directly, missciplus requires llms to retrieve the required real-world passages from the misrepresented study and reason over them. a complete fallacious argument, including the misrepresented study passages, is provided in §b. missci-plus is the first fallacy dataset to pair real-world misinformation claims with publication passages as evidence. this setup is identical to the input used by evidence-based scientific afc models and enables their evaluation over real-world fallacious misinformation. we use missciplus to answer the following research questions: our findings suggest that lexical and semantic sim-ilarity based ranking models perform the best at identifying the required evidence passages. however, all models cannot leverage these passages to refute misinformation. in summary, we contribute missciplus, an extension of missci to ground fallacies in real-world evidence, which bridges the gap to afc. we propose novel task definitions, along with extensive experiments on retrieving the required passages from the misrepresented study needed to detect misrepresented publications in the wild, as well as experiments using afc models and llms in detecting science distortions. fallacy detection much research on fallacy detection has primarily focused on surface-level fallacies @xcite @xcite @xcite . other works extended these inventories to include logical fallacies that may require additional context for detection. however, all of this research relied on educational examples, fake news websites @xcite , or fact-checking articles @xcite and assumed that the explicitly stated text was sufficient to detect the fallacies. missci @xcite developed models to verbalize the implicit fallacious reasoning. missciplus differs from existing fallacy datasets by grounding implicit fallacies in real-world evidence documents. scientific afc a large body of research on scientific afc used scientific documents as evidence to assess the veracity of claims @xcite @xcite @xcite @xcite . these approaches face challenges with fine-grained differences, such as specificity mismatches @xcite . our work bridges the gap between scientific afc and fallacy detection and sheds light on the abilities of afc models to reason over claims and misrepresented evidence passages. science communication a very related research area concerns science communication research @xcite , which compares claims and the cited evidence document across various dimensions such as claim strength @xcite , certainty of the used language @xcite , sentence-level causal exaggerations @xcite , quantification of the information match @xcite , or combinations of multiple dimensions @xcite . our work differs by focusing on harmful misinformation and tasks that involve retrieving relevant passages and articulating the fallacious reasoning. ## grounding missciplus with evidence to create missciplus, we selected all fallacious logical arguments from missci, for which the full misrepresented study is available via pmc 2 . this resulted in 118 fallacious arguments misrepresenting 100 distinct publications, which we automatically split into the constituent passages (cf. §c.1). we used the ims model @xcite to pre-select relevant passages and avoid exhaustively annotating every paraphrased information with every passage. the ims model quantifies the information match between textual statements and scientific text, which aligns well with our needs. for each paraphrased information (p 0 and s i ), we selected the top-ranked passage according to ims (cf. §c.2) and collected a minimum of six passages per argument in total, if possible. we employed two biology master's students with annotation experience in biomedical misinformation on missci. the annotators assigned an entailment label by comparing each paraphrased information (p 0 and s i ) with each selected passage (s j ), determining whether s j entails (and hence is linked to) the paraphrased information. following @xcite , the annotators could express uncertainty if the entailment relation was ambiguous. if the paraphrased information was not linked to any pre-selected passage after consolidation, one annotator manually selected a corresponding passage from the entire study, if possible, which was then double-annotated. we removed four arguments for which no paraphrased information could be linked to any passage, yielding 2,257 double-annotated relations between passages and paraphrased information across 114 arguments. we retained the same instances for validation (30 arguments) and test (84 arguments) splits as in missci. the inter-annotator 2 https://www.ncbi.nlm.nih.gov/pmc/ agreement, measured by cohen's κ, was 0.602. for details about the annotation process please refer to §c.3. overall, 400 pieces of paraphrased information (88.6% of the accurate premises p 0 ; 72.0% of the publication contexts s i ; 76.8% overall) could be linked to at least one passage (analysed in §c.4). descriptive statistics about the passages are in §d. ## retrieving relevant passages a prerequisite to reconstruct the fallacious argument is the identification of the relevant passages in the study. these passages provide evidence why the claim was made, and why this involves fallacies. they are needed for the fallacious argument reconstruction and for effective debunking, which must explain why a claim was thought to be true and why it actually is not @xcite . ## subtask 3: fallacious argument reconstruction given an incorrect claim, c, and relevant selected passages s @xmath0, the model must generate the fallacious premises p i along with their corresponding fallacy classes f i . these generated fallacious premises, @xmath1, p2 , ..., pn ], should bridge all reasoning gaps between s and the incorrect claim c. we assume oracle output of both passage retrieval tasks from section 5 and only provide passages linked to a fallacy (with at least one s 0 j passage as the basis for the claim). we experimented with gpt-4 turbo and gpt-3.5 as proprietary llms , and llama3-8b as a small open-source llm with strong leaderboard performance. as in missci, we prompted the llms in a zero-shot setting (cf. §h.1 for prompt selection). by default, all prompts include definition, logical form and toy examples, as shown in figure 4 , from literature @xcite as supplementary information for each fallacy (cf. §a for all fallacy classes). ## scientific afc evaluation a key novelty of missciplus are the claims paired with real-world evidence passages that can be assessed by fallacy detection models and evidence-based fact-checking models. this allows to test afc models on fallacious claims. ## conclusion and future work we introduced missciplus, an extension of missci to reconstruct fallacious arguments based on real passages of the misrepresented studies. we showed that existing ranking models and llms struggle to reconstruct fallacious arguments using the real-world evidence. moreover, fine-tuned afc models and llms failed to refute claims in missciplus when presented with misrepresented evidence, highlighting the dangers of persuasive llms. future work may improve models using synthetic data generation approaches, or extend the task definition over multiple documents.