On the Calibration of Massively Multilingual Language Models
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Abstract (arXiv)
Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer. While there has been much work in evaluating these models for their performance on a variety of tasks and languages, little attention has been paid on how well calibrated these models are with respect to the confidence in their predictions. We first investigate the calibration of MMLMs in the zero-shot setting and observe a clear case of miscalibration in low-resource languages or those which are typologically diverse from English. Next, we empirically show that calibration methods like temperature scaling and label smoothing do reasonably well towards improving calibration in the zero-shot scenario. We also find that few-shot examples in the language can further help reduce the calibration errors, often substantially. Overall, our work contributes towards building more reliable multilingual models by highlighting the issue of their miscalibration, understanding what language and model specific factors influence it, and pointing out the strategies to improve the same.
Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 21 Oct 2022.
Builds on
31 of 44 references.
References · 31
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19 more
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Citing papers per year: 2023: 4; 2024: 8; 2025: 2; 2026: 5.
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- 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontierscited by 51
- 2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languagescited by 32
- 2024Goldfish: Monolingual Language Models for 350 Languagescited by 29
- 2023Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Modelscited by 16
- 2024Teaching LLMs to Abstain across Languages via Multilingual Feedbackcited by 14
- 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
- 2025Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Modelscited by 10
- 2023On the Calibration of Multilingual Question Answering LLMscited by 6
- 2024Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Modelscited by 6
- 2024When Quantization Affects Confidence of Large Language Models?cited by 5
- 2024Predict the Next Word: Humans exhibit uncertainty in this task and language models _____cited by 4
- 2026Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variationscited by 3
7 more
- 2023Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibrationcited by 2
- 2024SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Modelscited by 1
- 2026Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Modelscited by 1
- 2025How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigmcited by 0
- 2026Choosing the Right Language Mode at Inference Time for Multilingual Reliabilitycited by 0
- 2026Language Distances are Practical for Equitable Cross-Lingual Transfercited by 0
- 2026Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languagescited by 0
- 2026Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages9 Oct 2026
- 2026Language Distances are Practical for Equitable Cross-Lingual Transfer26 Sep 2026
- 2026Choosing the Right Language Mode at Inference Time for Multilingual Reliability4 Sep 2026
- 2026Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models29 May 2026
- 2026Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations12 Jan 2026
- 2025How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigm17 Nov 2025
- 2025Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models3 Oct 2025
- 2024The Zeno's Paradox of `Low-Resource' Languages28 Oct 2024
- 2024Goldfish: Monolingual Language Models for 350 Languages19 Aug 2024
- 2024SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models27 Jun 2024
- 2024Teaching LLMs to Abstain across Languages via Multilingual Feedback22 Jun 2024
- 2024When Quantization Affects Confidence of Large Language Models?1 May 2024
7 more
- 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers7 Apr 2024
- 2024Predict the Next Word: Humans exhibit uncertainty in this task and language models _____27 Feb 2024
- 2024Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models5 Feb 2024
- 2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages15 Nov 2023
- 2023Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models15 Nov 2023
- 2023On the Calibration of Multilingual Question Answering LLMs15 Nov 2023
- 2023Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration8 Oct 2023
- 2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languagescited by 32
- 2024Goldfish: Monolingual Language Models for 350 Languagescited by 29
- 2023Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Modelscited by 16
- 2024Teaching LLMs to Abstain across Languages via Multilingual Feedbackcited by 14
- 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
- 2025Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Modelscited by 10
- 2023On the Calibration of Multilingual Question Answering LLMscited by 6
- 2024Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Modelscited by 6
- 2024When Quantization Affects Confidence of Large Language Models?cited by 5
- 2024Predict the Next Word: Humans exhibit uncertainty in this task and language models _____cited by 4
- 2026Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variationscited by 3
- 2023Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibrationcited by 2
12 most cited of 18 All 18 (JSON)
- 2026Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages9 Oct 2026
- 2026Language Distances are Practical for Equitable Cross-Lingual Transfer26 Sep 2026
- 2026Choosing the Right Language Mode at Inference Time for Multilingual Reliability4 Sep 2026
- 2026Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models29 May 2026
- 2026Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations12 Jan 2026
- 2025How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigm17 Nov 2025
- 2025Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models3 Oct 2025
- 2024The Zeno's Paradox of `Low-Resource' Languages28 Oct 2024
- 2024Goldfish: Monolingual Language Models for 350 Languages19 Aug 2024
- 2024SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models27 Jun 2024
- 2024Teaching LLMs to Abstain across Languages via Multilingual Feedback22 Jun 2024
- 2024When Quantization Affects Confidence of Large Language Models?1 May 2024
12 newest of 18 All 18 (JSON)