Multilingual Few-Shot Learning via Language Model Retrieval

• URL: http://arxiv.org/abs/2306.10964v1
• Date: Mon, 19 Jun 2023 14:27:21 GMT
• Title: Multilingual Few-Shot Learning via Language Model Retrieval
• Authors: Genta Indra Winata, Liang-Kang Huang, Soumya Vadlamannati, Yash Chandarana
• Abstract summary: Transformer-based language models have achieved remarkable success in few-shot in-context learning. We conduct a study of retrieving semantically similar few-shot samples and using them as the context. We evaluate the proposed method on five natural language understanding datasets related to intent detection, question classification, sentiment analysis, and topic classification.
• Score: 18.465566186549072
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transformer-based language models have achieved remarkable success in few-shot in-context learning and drawn a lot of research interest. However, these models' performance greatly depends on the choice of the example prompts and also has high variability depending on how samples are chosen. In this paper, we conduct a comprehensive study of retrieving semantically similar few-shot samples and using them as the context, as it helps the model decide the correct label without any gradient update in the multilingual and cross-lingual settings. We evaluate the proposed method on five natural language understanding datasets related to intent detection, question classification, sentiment analysis, and topic classification. The proposed method consistently outperforms random sampling in monolingual and cross-lingual tasks in non-English languages.

Related papers

• Distilling Monolingual and Crosslingual Word-in-Context Representations [18.87665111304974]
  We propose a method that distils representations of word meaning in context from a pre-trained language model in both monolingual and crosslingual settings. Our method does not require human-annotated corpora nor updates of the parameters of the pre-trained model. Our method learns to combine the outputs of different hidden layers of the pre-trained model using self-attention.
  arXiv  Detail & Related papers  (2024-09-13T11:10:16Z)
• Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval [5.446052898856584]
  This paper proposes a novel hybrid batch training strategy to improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings. The approach fine-tunes multilingual language models using a mix of monolingual and cross-lingual question-answer pair batches sampled based on dataset size.
  arXiv  Detail & Related papers  (2024-08-20T04:30:26Z)
• Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detection [1.4779899760345434]
  This study compares the performance of (1) fine-tuned language models and (2) large language models on the task of check-worthy claim detection. We composed a multilingual and multi-topical dataset comprising texts of various sources and styles.
  arXiv  Detail & Related papers  (2023-11-10T15:36:35Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Language Models are Few-shot Multilingual Learners [66.11011385895195]
  We evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones.
  arXiv  Detail & Related papers  (2021-09-16T03:08:22Z)
• Specializing Multilingual Language Models: An Empirical Study [50.7526245872855]
  Contextualized word representations from pretrained multilingual language models have become the de facto standard for addressing natural language tasks. For languages rarely or never seen by these models, directly using such models often results in suboptimal representation or use of data.
  arXiv  Detail & Related papers  (2021-06-16T18:13:55Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Towards Zero-Shot Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension [20.570539023748424]
  We propose a simple method to generate multilingual question and answer pairs on a large scale. These synthetic samples can be used to improve the zero-shot performance of multilingual QA models on target languages.
  arXiv  Detail & Related papers  (2020-10-22T19:59:37Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

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.