Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties

• URL: http://arxiv.org/abs/2502.07058v3
• Date: Thu, 20 Mar 2025 15:01:11 GMT
• Title: Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties
• Authors: Zixin Tang, Chieh-Yang Huang, Tsung-Che Li, Ho Yin Sam Ng, Hen-Hsen Huang, Ting-Hao 'Kenneth' Huang,
• Abstract summary: This paper introduces a novel and cost-effective approach to benchmark model performance across language varieties.<n>International online review platforms, such as Booking.com, can serve as effective data sources.
• Score: 22.274503709032317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmark model performance across language varieties. We argue that international online review platforms, such as Booking.com, can serve as effective data sources for constructing datasets that capture comments in different language varieties from similar real-world scenarios, like reviews for the same hotel with the same rating using the same language (e.g., Mandarin Chinese) but different language varieties (e.g., Taiwan Mandarin, Mainland Mandarin). To prove this concept, we constructed a contextually aligned dataset comprising reviews in Taiwan Mandarin and Mainland Mandarin and tested six LLMs in a sentiment analysis task. Our results show that LLMs consistently underperform in Taiwan Mandarin.

Related papers

• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Do We Need Language-Specific Fact-Checking Models? The Case of Chinese [15.619421104102516]
  This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese. We first demonstrate the limitations of translation-based methods and multilingual large language models, highlighting the need for language-specific systems. We propose a Chinese fact-checking system that can better retrieve evidence from a document by incorporating context information.
  arXiv  Detail & Related papers  (2024-01-27T20:26:03Z)
• XFEVER: Exploring Fact Verification across Languages [40.1637899493061]
  This paper introduces the Cross-lingual Fact Extraction and VERification dataset designed for benchmarking the fact verification models across different languages. We constructed it by translating the claim and evidence texts of the Fact Extraction and VERification dataset into six languages. The training and development sets were translated using machine translation, whereas the test set includes texts translated by professional translators and machine-translated texts.
  arXiv  Detail & Related papers  (2023-10-25T01:20:17Z)
• Cross-Lingual Knowledge Editing in Large Language Models [73.12622532088564]
  Knowledge editing has been shown to adapt large language models to new knowledge without retraining from scratch. It is still unknown the effect of source language editing on a different target language. We first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese.
  arXiv  Detail & Related papers  (2023-09-16T11:07:52Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
  Existing large language models show disparate capability across different languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
  arXiv  Detail & Related papers  (2023-08-09T13:32:06Z)
• How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning [14.02101305717738]
  Multilingual large language models (MLLMs) are jointly trained on data from many different languages. It remains unclear to what extent, and under which conditions, languages rely on each other's data. We find that MLLMs rely on data from multiple languages from the early stages of fine-tuning and that this reliance gradually increases as fine-tuning progresses.
  arXiv  Detail & Related papers  (2023-05-22T17:47:41Z)
• LERT: A Linguistically-motivated Pre-trained Language Model [67.65651497173998]
  We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original pre-training task. We carried out extensive experiments on ten Chinese NLU tasks, and the experimental results show that LERT could bring significant improvements.
  arXiv  Detail & Related papers  (2022-11-10T05:09:16Z)
• Compositional Evaluation on Japanese Textual Entailment and Similarity [20.864082353441685]
  Natural Language Inference (NLI) and Semantic Textual Similarity (STS) are widely used benchmark tasks for compositional evaluation of pre-trained language models. Despite growing interest in linguistic universals, most NLI/STS studies have focused almost exclusively on English. There are no available multilingual NLI/STS datasets in Japanese, which is typologically different from English.
  arXiv  Detail & Related papers  (2022-08-09T15:10:56Z)
• Does Transliteration Help Multilingual Language Modeling? [0.0]
  We empirically measure the effect of transliteration on Multilingual Language Models. We focus on the Indic languages, which have the highest script diversity in the world. We find that transliteration benefits the low-resource languages without negatively affecting the comparatively high-resource languages.
  arXiv  Detail & Related papers  (2022-01-29T05:48:42Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

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.