| Challenge: | Existing methods rely on output-level signals for sample identification, such as predictive entropy or semantic similarities with test-time data, which overlook models’ internal dynamics which could pinpoint specific knowledge gaps. |
| Approach: | They propose a Neuron-Aware Active Few-Shot Learning framework that shifts the selection paradigm from output-level proxies to models’ internal dynamics. |
| Outcome: | Experiments on three datasets show that NeuFS outperforms existing AFSL baselines. |
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Active Few-Shot Learning for Text Classification (2025.naacl-long)
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Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, Seyed Abolghasem Mirroshandel, Owen Rambow, Cornelia Caragea
| Challenge: | Recent advances in Large Language Models (LLMs) have boosted the use of Few-Shot Learning (FSL) methods in natural language processing. |
| Approach: | They propose a method that identifies effective support instances from the unlabeled pool and can work with different LLMs. |
| Outcome: | The proposed method improves on five tasks on which it is tested on five LLMs. |
Active Learning Principles for In-Context Learning with Large Language Models (2023.findings-emnlp)
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| Challenge: | In-context learning has significantly enhanced predictive performance in few-shot learning settings. |
| Approach: | They propose to use pool-based Active Learning to identify the most informative demonstrations for few-shot learning over a single iteration to identify best demonstrations. |
| Outcome: | The proposed model outperforms all other methods, including random sampling, in the analysis of 24 classification and multi-choice tasks. |
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training (2023.findings-eacl)
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| Challenge: | Recent work on few-shot classification has addressed the issue of data prioritisation of unlabelled data. |
| Approach: | They propose a weighted approach that uses a set of pattern-exploiting training models to actively select unlabelled data as candidates for annotation. |
| Outcome: | The proposed approach shows consistent improvement over baseline methods on two technical fact-checking datasets and using six different pretrained language models. |
MEAL: Stable and Active Learning for Few-Shot Prompting (2023.findings-emnlp)
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| Challenge: | Existing methods for few-shot classification have high variance across different sets of few shots and finetuning runs. |
| Approach: | They propose novel ensembling methods that significantly reduce run variability and introduce a new active learning criterion for *data selection*. |
| Outcome: | The proposed method significantly reduces run variability and improves performance on five tasks. |
Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)
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| Challenge: | Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available. |
| Approach: | They propose to introduce cross-task prototypes to model relationships between training tasks in few-shot learning for event detection. |
| Outcome: | The proposed model improves on three few-shot learning datasets. |
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
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| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
| Approach: | They propose an active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. |
| Outcome: | The proposed approach outperforms existing methods ADAPET, PERFECT, and SetFit in few-shot scenarios and can be extended to non-few scenarios. |
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification (2024.findings-acl)
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| Challenge: | Recent studies have focused on few-shot and zero-shot learning, but label occurrences vary widely . authors propose a new classification challenge that can be used to manage labels across the full frequency spectrum . |
| Approach: | They propose a new classification challenge that allows for label co-occurrences without predefined limits. |
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BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types. |
| Approach: | They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. |
| Outcome: | The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures. |
Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026.findings-acl)
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| Challenge: | Existing studies on small language models are characterised by a labelled data scarcity due to data collection/annotation costs or privacy considerations, making the training of typical deep learning models unfeasible. |
| Approach: | They propose a method for Automatic Combination of SamplE Selection Strategies to leverage the strengths and complementarity of various well-established selection objectives. |
| Outcome: | The proposed method outperforms all in-context learning strategies and performs on par or exceeds the in-constinction learning specific baselines. |
Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models (2022.emnlp-main)
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| Challenge: | Pre-trained masked language models perform few-shot learning, but discriminative models like ELECTRA do not fit into the paradigm. |
| Approach: | They propose to use ELECTRA to train pre-trained models to score originality of target options without introducing new parameters. |
| Outcome: | The proposed model outperforms masked language models in a wide range of tasks without adding new parameters. |