| Challenge: | Existing methods for zero-shot learning are based on in-context training, but performance drops when no demonstrations are available. |
| Approach: | They propose a new method that constructs pseudo-demonstrations for a given test input using a raw text corpus and applies techniques to reduce copying. |
| Outcome: | The proposed method outperforms previous zero-shot methods on nine classification datasets and is on par with in-context learning with labeled training data in the few-shot setting. |
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Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown striking ability to adapt to target tasks with a few input-output demonstrations. |
| Approach: | They propose a framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. |
| Outcome: | The proposed framework outperforms baselines on 23 BIG-Bench Hard tasks on average accuracy and head-to-head comparison. |
DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning (2025.naacl-long)
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| Challenge: | Existing methods to conduct in-context learning without using human-annotated demonstrations are unreliable and lead to error accumulation. |
| Approach: | They propose a method to conduct in-context learning without using human-annotated demonstrations. |
| Outcome: | The proposed method outperforms existing methods using human-annotated demonstrations. |
Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates. |
| Approach: | They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance. |
| Outcome: | The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
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| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
Zero- and Few-Shot NLP with Pretrained Language Models (2022.acl-tutorials)
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| Challenge: | a tutorial aims to introduce NLP researchers to the latest techniques for learning from little-to-no data . aims at bringing interested researchers up to speed about the latest and ongoing techniques . |
| Approach: | They aim to introduce techniques for learning from little-to-no data using pretrained language models. |
| Outcome: | This tutorial aims to bring interested NLP researchers up to speed about recent techniques . it will cover methods from manual engineering, better inference algorithms to better tuning methods . |
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)
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| Challenge: | In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples. |
| Approach: | They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass . |
| Outcome: | The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation. |
Many-Shot Scaling of In-Context Learning with Self-Generated Demonstrations (2026.findings-acl)
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| Challenge: | In-context learning methods that use self-generated annotations do not scale to many-shot scenarios. |
| Approach: | They propose a framework analogous to semi-supervised learning that uses self-generated annotations instead of ground truth labels. |
| Outcome: | The proposed framework outperforms ground truth ICL under zero-shot, few-shot and many-shot settings. |
Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) empowers Large Language Models for rapid task adaptation without fine-tuning. |
| Approach: | They propose a method that aligns fine-tuning gradients between entire training set and selected examples to enable in-context learning and fine-uning. |
| Outcome: | The proposed method outperforms random selection on large LLMs from 4-shot to 128-shot scenarios across 9 datasets. |
C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples. |
| Approach: | They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. |
| Outcome: | The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks. |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |