Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance

• URL: http://arxiv.org/abs/2305.15233v3
• Date: Tue, 16 Jul 2024 08:18:48 GMT
• Title: Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
• Authors: Sunkyoung Kim, Dayeon Ki, Yireun Kim, Jinsik Lee,
• Abstract summary: Cross-lingual QA is a cross-lingual prompting method that translates only the question and answer parts, thus reducing translation costs. Experiments on four typologically diverse multilingual benchmarks show that Cross-lingual QA effectively stimulates models to elicit their cross-lingual knowledge. We show that prompting open-source MLLMs with cross-lingual in-context examples enhances performance as the model scale increases.
• Score: 2.371686365695081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual in-context examples, either in the source or target language. However, translating entire in-context examples into the target language might compromise contextual integrity and be costly in the case of long-context passages. To address this, we introduce Cross-lingual QA, a cross-lingual prompting method that translates only the question and answer parts, thus reducing translation costs. Experiments on four typologically diverse multilingual benchmarks show that Cross-lingual QA prompting effectively stimulates models to elicit their cross-lingual knowledge, outperforming prior monolingual prompting approaches. Furthermore, we show that prompting open-source MLLMs with cross-lingual in-context examples enhances performance as the model scale increases.

Related papers

• Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
  We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information. We evaluate four state-of-the-art large language models on MLNeedle.
  arXiv  Detail & Related papers  (2024-08-19T17:02:06Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment [24.742581572364124]
  In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update. We provide the first in-depth analysis of ICL for cross-lingual text classification. We propose a novel prompt construction strategy -- Cross-lingual In-context Source-Target Alignment (X-InSTA)
  arXiv  Detail & Related papers  (2023-05-10T07:24:36Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Delving Deeper into Cross-lingual Visual Question Answering [115.16614806717341]
  We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers.
  arXiv  Detail & Related papers  (2022-02-15T18:22:18Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

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.