mlsanityData to 7 Oct 2026

MisSynth: Improving MISSCI Logical Fallacies Classification with Synthetic Data

arXiv 2510.26345 ↗First posted 30 Oct 2025cs.CL cs.AI cs.LGcited by 0

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSCI dataset and framework. In this work, we propose MisSynth, a pipeline that applies retrieval-augmented generation (RAG) to produce synthetic fallacy samples, which are then used to fine-tune an LLM model. Our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines. For instance, the LLaMA 3.1 8B fine-tuned model achieved an over 35% F1-score absolute improvement on the MISSCI test split over its vanilla baseline. We demonstrate that introducing synthetic fallacy data to augment limited annotated resources can significantly enhance zero-shot LLM classification performance on real-world scientific misinformation tasks, even with limited computational resources. The code and synthetic dataset are available on https://github.com/mxpoliakov/MisSynth.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 17 of its 28 references that are in the map. First posted on arXiv 30 Oct 2025.

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17 of 28 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2025Gemma 3 Technical Reportcited by 1,686
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  7. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
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  11. 2023A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoningcited by 41
  12. 2023The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Taskscited by 35
5 more
  1. 2022Logical Fallacy Detectioncited by 25
  2. 2024Missci: Reconstructing Fallacies in Misrepresented Sciencecited by 14
  3. 2024Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understandingcited by 12
  4. 2024Grounding Fallacies Misrepresenting Scientific Publications in Evidencecited by 7
  5. 2025RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premisescited by 6

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  1. 2025Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
  2. 2025Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMs
  3. 2024Are LLMs Good Zero-Shot Fallacy Classifiers?
  4. 2023Minimizing Factual Inconsistency and Hallucination in Large Language Models
  5. 2024A Logical Fallacy-Informed Framework for Argument Generation
  6. 2025Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
  7. 2024LLM Robustness Against Misinformation in Biomedical Question Answering
  8. 2025Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles