Towards Truthful Multilingual Large Language Models: Benchmarking and Alignment Strategies

• URL: http://arxiv.org/abs/2406.14434v1
• Date: Thu, 20 Jun 2024 15:59:07 GMT
• Title: Towards Truthful Multilingual Large Language Models: Benchmarking and Alignment Strategies
• Authors: Weihao Liu, Ning Wu, Wenbiao Ding, Shining Liang, Ming Gong, Dongmei Zhang,
• Abstract summary: 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.
• Score: 38.3269908062146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of large language models (LLMs), building multilingual large language models (MLLMs) that can serve users worldwide holds great significance. However, existing research seldom focuses on the truthfulness of MLLMs. Meanwhile, contemporary multilingual aligning technologies struggle to balance massive languages and often exhibit serious truthfulness gaps across different languages, especially those that differ greatly from English. In our work, we construct a benchmark for truthfulness evaluation in multilingual scenarios and explore the ways to align facts across languages to enhance the truthfulness of MLLMs. Furthermore, we propose Fact-aware Multilingual Selective Synergy (FaMSS) to optimize the data allocation across a large number of languages and different data types. Experimental results demonstrate that our approach can effectively reduce the multilingual representation disparity and enhance the multilingual capabilities of LLMs.

Related papers

• Multilingual Large Language Models: A Systematic Survey [38.972546467173565]
  This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs) We first discuss the architecture and pre-training objectives of MLLMs, highlighting the key components and methodologies that contribute to their multilingual capabilities. We present a detailed taxonomy and roadmap covering the assessment of MLLMs' cross-lingual knowledge, reasoning, alignment with human values, safety, interpretability and specialized applications.
  arXiv  Detail & Related papers  (2024-11-17T13:21:26Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• LLM for Everyone: Representing the Underrepresented in Large Language Models [21.07409393578553]
  This thesis aims to bridge the gap in NLP research and development by focusing on underrepresented languages. A comprehensive evaluation of large language models (LLMs) is conducted to assess their capabilities in these languages. The proposed solutions cover cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment.
  arXiv  Detail & Related papers  (2024-09-20T20:53:22Z)
• 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)
• 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)
• A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias [5.104497013562654]
  We present an overview of MLLMs, covering their evolution, key techniques, and multilingual capacities. We explore widely utilized multilingual corpora for MLLMs' training and multilingual datasets oriented for downstream tasks. We discuss bias on MLLMs including its category and evaluation metrics, and summarize the existing debiasing techniques.
  arXiv  Detail & Related papers  (2024-04-01T05:13:56Z)
• Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models [79.46179534911019]
  Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to imbalanced training corpora. We extend the evaluation to real-world user queries and non-English-centric LLMs, offering a broader examination of multilingual performance.
  arXiv  Detail & Related papers  (2024-03-15T12:47:39Z)
• Analysis of Multi-Source Language Training in Cross-Lingual Transfer [6.992785466925966]
  Cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem. We show that the use of multiple source languages in XLT-a technique we term Multi-Source Language Training (MSLT)-leads to increased mingling of embedding spaces for different languages. On the other hand, we discover that using an arbitrary combination of source languages does not always guarantee better performance.
  arXiv  Detail & Related papers  (2024-02-21T06:37:07Z)
• 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)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)
• 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)
• Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models [4.731313022026271]
  In this study, we focus on a single low-resource language and perform extensive evaluation and probing experiments using cross-lingual post-training (XPT) Results show that XPT not only outperforms or performs on par with monolingual models trained with orders of magnitudes more data but also is highly efficient in the transfer process.
  arXiv  Detail & Related papers  (2022-09-14T05:20:52Z)
• Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure [54.01613740115601]
  We study three language properties: constituent order, composition and word co-occurrence. Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
  arXiv  Detail & Related papers  (2022-03-16T07:09:35Z)
• When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer [15.578267998149743]
  We show that the absence of sub-word overlap significantly affects zero-shot transfer when languages differ in their word order. There is a strong correlation between transfer performance and word embedding alignment between languages. Our results call for focus in multilingual models on explicitly improving word embedding alignment between languages.
  arXiv  Detail & Related papers  (2021-10-27T21:25:39Z)
• X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering [55.57776147848929]
  We propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for Natural Language Understanding (NLU) Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.
  arXiv  Detail & Related papers  (2021-04-20T00:13:35Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)
• A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT [60.9051207862378]
  multilingual BERT works remarkably well on cross-lingual transfer tasks. Datasize and context window size are crucial factors to the transferability. There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
  arXiv  Detail & Related papers  (2020-04-20T11:13:16Z)

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.