Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models
- URL: http://arxiv.org/abs/2407.05502v1
- Date: Sun, 7 Jul 2024 21:26:36 GMT
- Title: Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models
- Authors: Nikhil Sharma, Kenton Murray, Ziang Xiao,
- Abstract summary: With Retrieval Augmented Generation (RAG), Large Language Models (LLMs) are playing a pivotal role in information search.
We studied LLM's linguistic preference in a RAG-based information search setting.
We found that LLMs displayed systemic bias towards information in the same language as the query language in both information retrieval and answer generation.
- Score: 7.615938028813914
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With Retrieval Augmented Generation (RAG), Large Language Models (LLMs) are playing a pivotal role in information search and are being adopted globally. Although the multilingual capability of LLMs offers new opportunities to bridge the language barrier, do these capabilities translate into real-life scenarios where linguistic divide and knowledge conflicts between multilingual sources are known occurrences? In this paper, we studied LLM's linguistic preference in a RAG-based information search setting. We found that LLMs displayed systemic bias towards information in the same language as the query language in both information retrieval and answer generation. Furthermore, in scenarios where there is little information in the language of the query, LLMs prefer documents in high-resource languages, reinforcing the dominant views. Such bias exists for both factual and opinion-based queries. Our results highlight the linguistic divide within multilingual LLMs in information search systems. The seemingly beneficial multilingual capability of LLMs may backfire on information parity by reinforcing language-specific information cocoons or filter bubbles further marginalizing low-resource views.
Related papers
- 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) - Towards Truthful Multilingual Large Language Models: Benchmarking and Alignment Strategies [38.3269908062146]
We construct a benchmark for truthfulness evaluation in multilingual scenarios.
We propose Fact-aware Multilingual Selective Synergy (FaMSS) to optimize the data allocation across a large number of languages.
arXiv Detail & Related papers (2024-06-20T15:59:07Z) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - Enhancing Multilingual Capabilities of Large Language Models through
Self-Distillation from Resource-Rich Languages [60.162717568496355]
Large language models (LLMs) have been pre-trained on multilingual corpora.
Their performance still lags behind in most languages compared to a few resource-rich languages.
arXiv Detail & Related papers (2024-02-19T15:07:32Z) - Zero-Shot Cross-Lingual Reranking with Large Language Models for
Low-Resource Languages [51.301942056881146]
We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages.
Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba)
We examine cross-lingual reranking with queries in English and passages in the African languages.
arXiv Detail & Related papers (2023-12-26T18:38:54Z) - Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars.
We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English.
Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
arXiv Detail & Related papers (2023-06-20T08:27:47Z) - Don't Trust ChatGPT when Your Question is not in English: A Study of
Multilingual Abilities and Types of LLMs [16.770697902481107]
Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities.
We propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
arXiv Detail & Related papers (2023-05-24T02:05:03Z) - Locating Language-Specific Information in Contextualized Embeddings [2.836066255205732]
Multilingual pretrained language models (MPLMs) exhibit multilinguality and are well suited for transfer across languages.
The question whether MPLM representations are language-agnostic or they simply interleave well with learned task prediction heads arises.
We locate language-specific information in MPLMs and identify its dimensionality and the layers where this information occurs.
arXiv Detail & Related papers (2021-09-16T15:11:55Z)
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.