MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models

• URL: http://arxiv.org/abs/2410.12478v2
• Date: Fri, 18 Oct 2024 03:19:51 GMT
• Title: MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
• Authors: Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Kam-Fai Wong,
• Abstract summary: This paper introduces a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on Large Language Models (LLMs) The benchmark comprises four meticulously checked and human-evaluated high-quality multilingual datasets for LA tasks and one for the LS task tailored to specific social, cultural, and geographical contexts of a language. Experiments reveal that on LA tasks English exhibits notable linguistic dominance in confidence estimations than other languages, while on LS tasks, using question-related language to prompt LLMs demonstrates better linguistic dominance in multilingual confidence estimations.
• Score: 23.384966485398184
• License:
• Abstract: The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trustworthiness of the generations become essential. However, current LLM confidence estimations in languages other than English remain underexplored. This paper addresses this gap by introducing a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on LLMs, focusing on both language-agnostic (LA) and language-specific (LS) tasks to explore the performance and language dominance effects of multilingual confidence estimations on different tasks. The benchmark comprises four meticulously checked and human-evaluate high-quality multilingual datasets for LA tasks and one for the LS task tailored to specific social, cultural, and geographical contexts of a language. Our experiments reveal that on LA tasks English exhibits notable linguistic dominance in confidence estimations than other languages, while on LS tasks, using question-related language to prompt LLMs demonstrates better linguistic dominance in multilingual confidence estimations. The phenomena inspire a simple yet effective native-tone prompting strategy by employing language-specific prompts for LS tasks, effectively improving LLMs' reliability and accuracy on LS tasks.

Related papers

• 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)
• XTRUST: On the Multilingual Trustworthiness of Large Language Models [14.128810448194699]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of natural language processing (NLP) tasks. A key question that now preoccupies the AI community concerns the capabilities and limitations of these models. X is the first comprehensive multilingual trustworthiness benchmark.
  arXiv  Detail & Related papers  (2024-09-24T05:38:33Z)
• 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)
• Evaluating Knowledge-based Cross-lingual Inconsistency in Large Language Models [16.942897938964638]
  Large Language Models (LLMs) have shown exceptional performance in various Natural Language Processing (NLP) tasks. Despite their successes, these models often exhibit significant inconsistencies when processing the same concepts across different languages. This study focuses on three primary questions: the existence of cross-lingual inconsistencies in LLMs, the specific aspects in which these inconsistencies manifest, and the correlation between cross-lingual consistency and multilingual capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-07-01T15:11:37Z)
• Quantifying Multilingual Performance of Large Language Models Across Languages [48.40607157158246]
  Large Language Models (LLMs) perform better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. We propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using internal representations. Our analysis reveals that high-resource languages exhibit higher similarity scores with English, demonstrating superior performance, while low-resource languages show lower similarity scores.
  arXiv  Detail & Related papers  (2024-04-17T16:53:16Z)
• 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. This work extends the evaluation from NLP tasks to real user queries. For culture-related tasks that need deep language understanding, prompting in the native language tends to be more promising.
  arXiv  Detail & Related papers  (2024-03-15T12:47:39Z)
• Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet? [82.02076369811402]
  Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning. We present an extensive and systematic comparison of the three approaches, testing them on 6 high- and low-resource languages, three different NLU tasks, and a myriad of language and domain setups. Our observations show that supervised instruction tuning has the best trade-off between performance and resource requirements.
  arXiv  Detail & Related papers  (2024-03-04T10:48:13Z)
• 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)
• A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models [23.384966485398184]
  This paper introduces a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on Large Language Models (LLMs) The benchmark comprises four meticulously checked and human-evaluated high-quality multilingual datasets for LA tasks and one for the LS task tailored to specific social, cultural, and geographical contexts of a language. Experiments reveal that on LA tasks English exhibits notable linguistic dominance in confidence estimations than other languages, while on LS tasks, using question-related language to prompt LLMs demonstrates better linguistic dominance in multilingual confidence estimations.
  arXiv  Detail & Related papers  (2024-02-21T08:20:06Z)
• CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
  This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
  arXiv  Detail & Related papers  (2023-06-15T15:49:51Z)
• 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)

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.