Efficient Text Classification with Conformal In-Context Learning

• URL: http://arxiv.org/abs/2512.05732v1
• Date: Fri, 05 Dec 2025 14:11:44 GMT
• Title: Efficient Text Classification with Conformal In-Context Learning
• Authors: Ippokratis Pantelidis, Korbinian Randl, Aron Henriksson,
• Abstract summary: Large Language Models (LLMs) demonstrate strong in-context learning abilities, yet their effectiveness in text classification depends heavily on prompt design.<n>We present a comprehensive evaluation of Conformal In-Context Learning (CICLe) across diverse NLP classification benchmarks.<n>In terms of efficiency, CICLe reduces the number of shots and prompt length by up to 34.45% and 25.16%, respectively.
• Score: 2.566571621858397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) demonstrate strong in-context learning abilities, yet their effectiveness in text classification depends heavily on prompt design and incurs substantial computational cost. Conformal In-Context Learning (CICLe) has been proposed as a resource-efficient framework that integrates a lightweight base classifier with Conformal Prediction to guide LLM prompting by adaptively reducing the set of candidate classes. However, its broader applicability and efficiency benefits beyond a single domain have not yet been systematically explored. In this paper, we present a comprehensive evaluation of CICLe across diverse NLP classification benchmarks. The results show that CICLe consistently improves over its base classifier and outperforms few-shot prompting baselines when the sample size is sufficient for training the base classifier, and performs comparably in low-data regimes. In terms of efficiency, CICLe reduces the number of shots and prompt length by up to 34.45% and 25.16%, respectively, and enables the use of smaller models with competitive performance. CICLe is furthermore particularly advantageous for text classification tasks with high class imbalance. These findings highlight CICLe as a practical and scalable approach for efficient text classification, combining the robustness of traditional classifiers with the adaptability of LLMs, and achieving substantial gains in data and computational efficiency.

Related papers

• CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs [53.749193998004166]
  Curriculum learning plays a crucial role in enhancing the training efficiency of large language models.<n>We propose CurES, an efficient training method that accelerates convergence and employs Bayesian posterior estimation to minimize computational overhead.
  arXiv  Detail & Related papers  (2025-10-01T15:41:27Z)
• GLiClass: Generalist Lightweight Model for Sequence Classification Tasks [49.2639069781367]
  We propose GLiClass, a novel method that adapts the GLiNER architecture for sequence classification tasks.<n>Our approach achieves strong accuracy and efficiency comparable to embedding-based methods, while maintaining the flexibility needed for zero-shot and few-shot learning scenarios.
  arXiv  Detail & Related papers  (2025-08-11T06:22:25Z)
• Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context [24.905102026459428]
  Transformers have demonstrated remarkable in-context learning capabilities, adapting to new tasks by simply conditioning on demonstrations without parameter updates.<n>It remains unclear to what extent the transformers optimally learn in-context compared to principled learning algorithms.
  arXiv  Detail & Related papers  (2025-02-07T00:26:45Z)
• ICLERB: In-Context Learning Embedding and Reranker Benchmark [45.40331863265474]
  In-Context Learning (ICL) enables Large Language Models to perform new tasks by conditioning on prompts with relevant information.<n>Traditional retrieval methods focus on semantic relevance, treating retrieval as a search problem.<n>We propose reframing retrieval for ICL as a recommendation problem, aiming to select documents that maximize utility in ICL tasks.
  arXiv  Detail & Related papers  (2024-11-28T06:28:45Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science [0.1499944454332829]
  We evaluate the classification performance of large language models (LLMs) using in-context learning (ICL) and instruction tuning (IT) ICL offers a rapid alternative for task adaptation by learning from examples without explicit gradient updates. Our research highlights the significant advantages of ICL in handling CSS tasks in few-shot settings.
  arXiv  Detail & Related papers  (2024-09-23T02:43:08Z)
• Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions [49.36683223327633]
  Large Language Models (LLMs) encapsulate extensive world knowledge and exhibit powerful context modeling capabilities.<n>We propose a novel framework that synergizes the strengths of LLMs with robust knowledge representation to enable effective and efficient KGC.<n>We achieve a 47% relative improvement over previous methods based on non-fine-tuned LLMs and, to our knowledge, are the first to achieve classification performance comparable to fine-tuned LLMs.
  arXiv  Detail & Related papers  (2024-08-13T10:15:55Z)
• LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification [13.319594321038926]
  We propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead.
  arXiv  Detail & Related papers  (2024-06-06T03:46:59Z)
• Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data [36.09359953556684]
  Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better accuracy than the 0-shot setting, but it pays in terms of efficiency, due to the longer input prompt.
  arXiv  Detail & Related papers  (2024-04-03T03:24:19Z)
• A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation [121.0693322732454]
  Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods to enhance CLIP's performance in downstream tasks. We revisit a classical algorithm, Gaussian Discriminant Analysis (GDA), and apply it to the downstream classification of CLIP.
  arXiv  Detail & Related papers  (2024-02-06T15:45:27Z)
• Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods [61.49061000562676]
  We introduce Cluster Learnability (CL) to assess learnability. CL is measured in terms of the performance of a KNN trained to predict labels obtained by clustering the representations with K-means. We find that CL better correlates with in-distribution model performance than other competing recent evaluation schemes.
  arXiv  Detail & Related papers  (2022-06-02T19:05:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.