Papers with -shot setting
On Training Instance Selection for Few-Shot Neural Text Generation (2021.acl-short)
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| 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. |
Investigating Multilingual NMT Representations at Scale (D19-1)
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| Challenge: | Multilingual Neural Machine Translation models have shown success in transfer learning settings, but their mode of transfer remains elusive. |
| Approach: | They propose to use a representation similarity framework to compare multilingual representations using a SVCCA representation similar to the previous work. |
| Outcome: | The proposed model can be used to compare representations across languages and layers. |
MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task that aims at classifying mention spans into entity types. |
| Approach: | They propose a variational memory-augmented few-shot named entity recognition model that uses a memory module to store information from source domain and retrieve relevant information from the memory to augment few-shot task in target domain. |
| Outcome: | The proposed model can adapt the learned knowledge from source domain to target domain and achieve superior performance on English and Chinese cross domain few-shot NER datasets. |
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks (2024.findings-acl)
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| Challenge: | Large language models generate fluent text with minimal task-specific supervision, but their ability to generate rationales for knowledge-intensive tasks (KITs) remains under-explored. |
| Approach: | They propose to generate retrieval-augmented rationalization of KIT model predictions via external knowledge guidance within a few-shot setting. |
| Outcome: | The proposed rationales were compared with crowd-sourced rationale models on factuality, sufficiency, and convincingness. |
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models (2021.emnlp-main)
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| Challenge: | Existing pre-trained models need fine-tuning on tens of thousands of examples to achieve good results. |
| Approach: | They propose a framework that leverages pre-trained text-to-text models and aligns them with their pre-training framework. |
| Outcome: | The proposed framework outperforms the XLM-Roberta-large on multiple QA benchmarks and is applicable to multilingual situations. |
Evaluating the Potential of Language-family-specific Generative Models for Low-resource Data Augmentation: A Faroese Case Study (2024.lrec-main)
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| Challenge: | generative language models have shown promising results for translation in zero, one, and fewshot learning settings, among other types of tasks. |
| Approach: | They propose to prompt a generative language model for the Nordic languages for Faroese to English translation in a zero, one, and few-shot setting and challenge its Farose language understanding capabilities on a small dataset. |
| Outcome: | The proposed model can translate Faroese to English in a zero, one, and few-shot setting and then use it to create an annotated Farose semantic textual similarity (STS) dataset. |
COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images (2021.emnlp-main)
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| Challenge: | Existing benchmarks for visual-grounded models have focused on synthetic images . et al., 2018: compositional generalization is crucial for building models that generalize to new settings. |
| Approach: | They propose a test-bed for visually-grounded compositional generalization with real images. |
| Outcome: | The proposed test-bed enables compositional splits where models need to generalize to new concepts and compositions in a zero- or few-shot setting. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |