mlsanityData to 14 Oct 2026

Text Classification via Large Language Models

arXiv 2305.08377 ↗First posted 15 May 2023cs.CLcited by 106

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Abstract (arXiv)

Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classification. This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e.g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning. In this paper, we introduce Clue And Reasoning Prompting (CARP). CARP adopts a progressive reasoning strategy tailored to addressing the complex linguistic phenomena involved in text classification: CARP first prompts LLMs to find superficial clues (e.g., keywords, tones, semantic relations, references, etc), based on which a diagnostic reasoning process is induced for final decisions. To further address the limited-token issue, CARP uses a fine-tuned model on the supervised dataset for kNN demonstration search in the in-context learning, allowing the model to take the advantage of both LLM's generalization ability and the task-specific evidence provided by the full labeled dataset. Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance comparable to SOTA on MR (92.39 v.s. 93.3). More importantly, we find that CARP delivers impressive abilities on low-resource and domain-adaptation setups. Specifically, using 16 examples per class, CARP achieves comparable performances to supervised models with 1,024 examples per class.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). Builds on 62 of its 81 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

62 of 81 references.

References · 62

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Citing papers per year: 2023: 14; 2024: 40; 2025: 38; 2026: 14.

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  11. 2024SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understandingcited by 20
  12. 2024Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Modelscited by 19
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  4. 2024Designing Efficient LLM Accelerators for Edge Devicescited by 13
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  6. 2024The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMscited by 12
  7. 2023Are Human-generated Demonstrations Necessary for In-context Learning?cited by 11
  8. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  9. 2024Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisionscited by 11
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  11. 2025Time Series Language Model for Descriptive Caption Generationcited by 11
  12. 2025Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistancecited by 11
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  14. 2023Hallucination-minimized Data-to-answer Framework for Financial Decision-makerscited by 9
  15. 2024Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Modelscited by 9
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  17. 2025Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLMcited by 9
  18. 2024PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakerscited by 8
  19. 2024Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledgecited by 8
  20. 2023Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Modelscited by 7
  21. 2024DALLMi: Domain Adaption for LLM-based Multi-label Classifiercited by 7
  22. 2024Incubating Text Classifiers Following User Instruction with Nothing but LLMcited by 6
  23. 2025GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasetscited by 6
  24. 2023BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptionscited by 5
  25. 2024Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lendingcited by 5
  26. 2024Large Language Models for the Automated Analysis of Optimization Algorithmscited by 5
  27. 2024Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Datacited by 5
  28. 2024Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Studycited by 5
  29. 2025CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrievalcited by 5
  30. 2026The Embedder's Dilemma: LLMs Are Better, but at What Cost?cited by 5
  31. 2024Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Taskscited by 4
  32. 2024Lower Bounds on the Expressivity of Recurrent Neural Language Modelscited by 4
  33. 2024Improving In-Context Learning with Small Language Model Ensemblescited by 4
  34. 2024Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classificationcited by 4
  35. 2024Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performancecited by 4
  36. 2025Evaluating multiple models using labeled and unlabeled datacited by 4
  37. 2025SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotatorscited by 4
  38. 2025No Query, No Accesscited by 4

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  4. 2022Performance-Efficiency Trade-Offs in Adapting Language Models to Text Classification Tasks
  5. 2023Large Language Models Are Zero-Shot Text Classifiers
  6. 2026Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
  7. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  8. 2023OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models