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. |
Similar Papers
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
Copied to clipboard
| 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. |
Neuron-Aware Active Few-Shot Learning for LLMs (2026.acl-long)
Copied to clipboard
| 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. |
Active Learning Principles for In-Context Learning with Large Language Models (2023.findings-emnlp)
Copied to clipboard
| 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. |
On Training Instance Selection for Few-Shot Neural Text Generation (2021.acl-short)
Copied to clipboard
| Challenge: | Pretraining large neural networks with a language modeling objective has led to dramatic improvements in text generation. |
| Approach: | They propose a selection strategy to select few-shot training instances based on unlabeled data to identify the most worthwhile data points that should be annotated under some budget of labeling cost. |
| Outcome: | The proposed strategy outperforms random sampling on three text generation tasks. |
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent few-shot learning methods focus on improving downstream task performance, but there is limited understanding of the adversarial robustness of such methods. |
| Approach: | They evaluate prompt-based FSL methods against fully fine-tuned models to better understand the impact of various factors towards robustness. |
| Outcome: | The proposed methods show that they are less robust in the face of adversarial perturbations than fully fine-tuned models. |
Few-shot Adaptation Works with UnpredicTable Data (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. |
| Approach: | They finetuned 413,299 tasks from internet tables to find narrow subsets outperform more diverse datasets. |
| Outcome: | The proposed model outperforms training on 40 human-curated NLP datasets on 52 downstream tasks, but not proportionally to dataset scale. |
Zero- and Few-Shot NLP with Pretrained Language Models (2022.acl-tutorials)
Copied to clipboard
| 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 . |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
Copied to clipboard
| 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. |
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (2021.tacl-1)
Copied to clipboard
| Challenge: | a recent study has focused on few-shot learning (FSL) for relation classification, but it requires large amounts of training data. |
| Approach: | They propose a method for deriving more realistic few-shot test data from available datasets for supervised RC. |
| Outcome: | The proposed method yields a challenging benchmark for FSL-RC on which state of the art models show poor performance. |
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent work shows that large-scale pretrained language models (PLMs) are effective few-shot learners. |
| Approach: | They propose a method that treats few-shotlearners as crowdsourcing workers . they propose to use these workers to train models that solve a task well . |
| Outcome: | The proposed approach treats few-shotlearners as crowdsourcing workers . the resulting annotations can be utilized to train models that solve the task well . |