Confidently Wrong: Exploring the Calibration and Expression of (Un)Certainty of Large Language Models in a Multilingual Setting (2023)

Abstract: While the fluency and coherence of Large Language Models (LLMs) in text generation have seen significant improvements, their competency in generating appropriate expressions of uncertainty remains limited.Using a multilingual closed-book QA task and GPT-3.5, we explore how well LLMs are calibrated and express certainty across a diverse set of languages, including low-resource settings. Our results reveal strong performance in high-resource languages but a marked decline in performance in lower-resource languages. Across all, we observe an exaggerated expression of confidence in the model, which does not align with the correctness or likelihood of its responses. Our findings highlight the need for further research into accurate calibration of LLMs especially in a multilingual setting.

## introduction accurate estimation of one's own certainty and the confidence in provided information is pivotal not only for humans but also for machine learning models, particularly those intended for broad use. when someone asks a person a factual question, they search in their memory for the answer and produce it when they find it. if they are not able to find it, they reply i don't know. it is also the case that the more hesitant they feel about knowing a fact, the more they fill their answers with hedges such as i guess or uhm to express they are not certain @xcite . ideally, a capable llm should be able to express its uncertainty about the facts for which it has weaker evidence when generating an answer. a significant body of research has examined the phenomenon of overconfidence in humans, shedding light on their tendency to overestimate their knowledge and capabilities @xcite . in parallel, a substantial amount of work has been dedicated to the calibration of models in machine learning, typically with a focus on english-language data @xcite @xcite . however, if multilingual support is stated when releasing an llm, claims about model performance based solely on english datasets are not enough, and the capacity to show accurate performance across multiple languages is essential. as such, we investigate the calibration, performance, and verbal expression of certainty in an llm (gpt-3.5) in a closed-book qa setting across six languages: amharic, dutch, english, german, hindi, and spanish, for which we release our dataset and annotations foot_0 . our methodology for this investigation is based on a similar exploration conducted by @xcite , albeit their research was solely based on english-language performance. our findings reveal that while the performance of the model on high-resource languages is commendable, a significant drop in performance is observed in lower-resource languages. interestingly, our results further demonstrate an excessively confident model, with confidence levels appearing to be unrelated to the correctness and, in most cases, to the likelihood of its responses. highlighting the need for continued research and improvement of calibration of language models, particularly in a multilingual setting. efforts to improve llm reliability have been introduced by studies such as @xcite , which devised prompting strategies for gpt-3, and @xcite , who explored llm selfevaluation. finally, the interpretation and generation of uncertainty in llms have been examined by @xcite and @xcite , who looked at the impact of naturalistic expressions and linguistic calibration respectively. their findings provide valuable insights for our exploration of multilingual llms. closed-book qa question-answering (qa) traditionally relies on information retrieval and a model that generates answers from the retrieved text. @xcite first proposed a closedbook qa approach, using only the question and the model's internal knowledge. they reported competitive performance against open-domain systems, however, later studies attributed these results to test-train overlap @xcite , not inherent model knowledge. @xcite confirmed these findings, showing bart's @xcite limitations in memorisation and knowledge retrieval. conversely, @xcite demonstrated gpt-3's high performance in factual qa. @xcite built an english-only dataset (with questions on physics, math puzzles, metaphors, etc.) and focus on comparing the performance of a large number of language models. recent studies have started to explore long-form qa (amplayo et al., 2023), requiring more complex information use. multilinguality while the majority of language models are trained almost exclusively on english data, the exploration of their multilingual capabil-ities is an active field of research. though their performance is often on par with existing state-ofthe-art cross-lingual models and translation models @xcite , it increases further when explicitly trained on multilingual data @xcite . @xcite demonstrated the strong multilingual reasoning abilities of the models using chain-of-thought prompting on a mathematical task, even for low-resource languages. however, when @xcite investigated similar behaviour, the model behaved in a translating manner, meaning performance can be affected by tasks where data cannot be explicitly translated. the closest work to ours is @xcite who built a multilingual qa dataset based on wikipedia entities and with different question categories. they cover 9 different languages (4 of them overlap with ours) and presented baseline results including mt5 @xcite language model and t5-cbqa @xcite . we propose here an extension of an english dataset into a multilingual dataset, including 6 languages and present the performance of gpt-3.5, while also being interested in measuring the certainty when responding to such questions and how this relates to the change of accuracy across languages. ## results and discussion in this section, we report our findings on the performance of the model in terms of its accuracy, perceived confidence and their interplay. we also investigate difficulties overall and for specific languages. we look at 1000 samples for all languages. ## conclusion our work highlights performance disparities in llms across languages and shows a prevalent cross-language expressed overconfidence in responses in a closed-book setting. these findings underscore the importance of nuanced, multilingual calibration in llms. accordingly, we hope to spur further progress by releasing our multilingual dataset and annotations.