FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

• URL: http://arxiv.org/abs/2109.01951v1
• Date: Sat, 4 Sep 2021 23:08:57 GMT
• Title: FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models
• Authors: Rakesh Chada, Pradeep Natarajan
• Abstract summary: Current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. We propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. Our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their performance degrades significantly in a few-shot setting (< 100 examples). To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. Specifically, we construct the input as a concatenation of the question, a mask token representing the answer span and a context. Given this input, the model is fine-tuned using the same objective as that of its pre-training objective. Through experimental studies on various few-shot configurations, we show that this formulation leads to significant gains on multiple QA benchmarks (an absolute gain of 34.2 F1 points on average when there are only 16 training examples). The gains extend further when used with larger models (Eg:- 72.3 F1 on SQuAD using BART-large with only 32 examples) and translate well to a multilingual setting . On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples). We conduct detailed ablation studies to analyze factors contributing to these gains.

Related papers

• Designing Informative Metrics for Few-Shot Example Selection [14.961505860372492]
  We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of examples. We use both sentence- and word-level metrics to match the complexity of examples to the (test) sentence being considered.
  arXiv  Detail & Related papers  (2024-03-06T17:11:38Z)
• Revisiting Few-Shot Object Detection with Vision-Language Models [49.79495118650838]
  We revisit the task of few-shot object detection (FSOD) in the context of recent foundational vision-language models (VLMs) We propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external data. We discuss our recent CVPR 2024 Foundational FSOD competition and share insights from the community.
  arXiv  Detail & Related papers  (2023-12-22T07:42:00Z)
• MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering [64.6741991162092]
  We present MinPrompt, a minimal data augmentation framework for open-domain question answering. We transform the raw text into a graph structure to build connections between different factual sentences. We then apply graph algorithms to identify the minimal set of sentences needed to cover the most information in the raw text. We generate QA pairs based on the identified sentence subset and train the model on the selected sentences to obtain the final model.
  arXiv  Detail & Related papers  (2023-10-08T04:44:36Z)
• Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games [3.615981646205045]
  We present an approach to train language models that can emulate the desirable behaviours, but not the undesirable ones. Using text-based games as a testbed, our approach, PASA, uses discrete latent variables to capture the range of different behaviours. Results show up to 49% empirical improvement over the previous state-of-the-art model.
  arXiv  Detail & Related papers  (2023-04-14T17:11:26Z)
• Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective [26.41585967095811]
  Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification.
  arXiv  Detail & Related papers  (2022-10-16T17:24:06Z)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• A Good Prompt Is Worth Millions of Parameters? Low-resource Prompt-based Learning for Vision-Language Models [50.27305012063483]
  FewVLM is a few-shot prompt-based learner on vision-language tasks. We pretrain a sequence-to-sequence Transformer model with both prefix language modeling (PrefixLM) and masked language modeling (MaskedLM) We observe that prompts significantly affect zero-shot performance but marginally affect few-shot performance.
  arXiv  Detail & Related papers  (2021-10-16T06:07:59Z)
• True Few-Shot Learning with Language Models [78.42578316883271]
  We evaluate the few-shot ability of LMs when held-out examples are unavailable. Our findings suggest that prior work significantly overestimated the true few-shot ability of LMs.
  arXiv  Detail & Related papers  (2021-05-24T17:55:51Z)
• Few-Shot Question Answering by Pretraining Span Selection [58.31911597824848]
  We explore the more realistic few-shot setting, where only a few hundred training examples are available. We show that standard span selection models perform poorly, highlighting the fact that current pretraining objective are far removed from question answering. Our findings indicate that careful design of pretraining schemes and model architecture can have a dramatic effect on performance in the few-shot settings.
  arXiv  Detail & Related papers  (2021-01-02T11:58:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.