mlsanityData to 14 Oct 2026

IMHO Fine-Tuning Improves Claim Detection

arXiv 1905.07000 ↗First posted 16 May 2019cs.CLcited by 27

Not on the map. Why?

Abstract (arXiv)

Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 9; 2022: 4; 2023: 9; 2024: 4; 2025: 1.

27 papers.

Sort:

By area:

  1. 2023MEDITRON-70B: Scaling Medical Pretraining for Large Language Modelscited by 274
  2. 2023Continual Pre-training of Language Modelscited by 198
  3. 2023Continual Pre-Training of Large Language Models: How to (re)warm your model?cited by 137
  4. 2021Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Mediacited by 51
  5. 2021Efficient Domain Adaptation of Language Models via Adaptive Tokenizationcited by 34
  6. 2025Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Reportcited by 31
  7. 2024AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotatorscited by 28
  8. 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?cited by 28
  9. 2023Adapting a Language Model While Preserving its General Knowledgecited by 23
  10. 2021LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Contentcited by 22
  11. 2022Downstream Datasets Make Surprisingly Good Pretraining Corporacited by 20
  12. 2022Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twittercited by 19
15 more
  1. 2023ModuleFormer: Modularity Emerges from Mixture-of-Expertscited by 17
  2. 2023From Chaos to Clarity: Claim Normalization to Empower Fact-Checkingcited by 17
  3. 2021Belief-based Generation of Argumentative Claimscited by 14
  4. 2021Generating Informative Conclusions for Argumentative Textscited by 14
  5. 2021ENTRUST: Argument Reframing with Language Models and Entailmentcited by 10
  6. 2023Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Mediacited by 8
  7. 2024Utilizing Large Language Models for Information Extraction from Real Estate Transactionscited by 8
  8. 2021DESYR: Definition and Syntactic Representation Based Claim Detection on the Webcited by 7
  9. 2021On the Role of Images for Analyzing Claims in Social Mediacited by 6
  10. 2022MM-Claims: A Dataset for Multimodal Claim Detection in Social Mediacited by 6
  11. 2023Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spanscited by 4
  12. 2022FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Informationcited by 3
  13. 2023Topic Ontologies for Argumentscited by 3
  14. 2021"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essayscited by 2
  15. 2024KemenkeuGPT: Leveraging a Large Language Model on Indonesia's Government Financial Data and Regulations to Enhance Decision Makingcited by 0