Papers with few-shot learning scenarios
Analyzing Text Representations by Measuring Task Alignment (2023.acl-short)
Copied to clipboard
| Challenge: | Recent advances in text classification have shown that pre-trained representations are key for text classification. |
| Approach: | They propose a task alignment score that measures alignment at different levels of granularity. |
| Outcome: | The proposed score shows that task alignment can explain the performance of a given representation. |
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations (2020.acl-main)
Copied to clipboard
| Challenge: | Span-ConveRT is a light-weight model for dialog slot-filling . we show consistent gains over a span extractor and a BERT-based model . |
| Approach: | They propose a model for dialog slot-filling which frames the task as a turn-based span extraction task. |
| Outcome: | The proposed model is especially useful for few-shot learning scenarios. |
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection (2022.findings-naacl)
Copied to clipboard
| Challenge: | a lack of general-purpose tools to characterize and predict ideology across genres of text remains a challenge . a recent study compared ideology-driven pretraining tasks with long or formal written texts . |
| Approach: | They propose to use a large-scale dataset to train pretraining models that compare political news articles on the same story written by different ideologies. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on ideology prediction and stance detection tasks. |
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples. |
| Approach: | They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance. |
| Outcome: | The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks. |
Logical DA: Enhancing Data Augmentation for Logical Reasoning via a Multi-Agent System (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing data augmentation paradigms isolate data synthesis from label validation, thereby reducing their utility for complex reasoning tasks. |
| Approach: | They propose a framework for enhancing reasoning-focused data augmentation in few-shot learning scenarios that integrates four agents through two synergistic phases: diverse data generation and label verification. |
| Outcome: | The proposed framework achieves the highest average improvement in task accuracy in both fine-tuning and in-context learning paradigms. |
A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-district tests, especially for few-shot learning scenarios. |
| Approach: | They propose a rational-centric framework with human-in-the-loop to exploit spurious associations and bias models towards generally applicable underlying distributions. |
| Outcome: | The proposed framework leads to significant prediction benefits on in-distribution and out-of-district tests, compared to state-of the-art benchmarks. |
Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)
Copied to clipboard
Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
| Challenge: | Pretrained language models are increasingly being used for many tasks. |
| Approach: | They propose to use verbalizers to help interpret masked word distributions into output predictions. |
| Outcome: | The proposed approach outperforms models trained with individual templates while using significantly less resources. |
Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) generate fluent text when the target output follows natural language patterns. |
| Approach: | They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts. |
| Outcome: | The proposed method produces diverse and natural text while preserving label semantics. |