M-IFEval: Multilingual Instruction-Following Evaluation
- URL: http://arxiv.org/abs/2502.04688v1
- Date: Fri, 07 Feb 2025 06:27:04 GMT
- Title: M-IFEval: Multilingual Instruction-Following Evaluation
- Authors: Antoine Dussolle, Andrea Cardeña Díaz, Shota Sato, Peter Devine,
- Abstract summary: The Instruction Following Evaluation (IFEval) benchmark from the literature does this using objective criteria.
It only includes English instructions, limiting its ability to assess LLMs in other languages.
We propose the Multilingual Instruction Following Evaluation (M-IFEval) benchmark, expanding the evaluation to French, Japanese, and Spanish, with both general and language-specific instructions.
- Score: 2.624902795082451
- License:
- Abstract: Instruction following is a core capability of modern Large language models (LLMs), making evaluating this capability essential to understanding these models. The Instruction Following Evaluation (IFEval) benchmark from the literature does this using objective criteria, offering a measure of LLM performance without subjective AI or human judgement. However, it only includes English instructions, limiting its ability to assess LLMs in other languages. We propose the Multilingual Instruction Following Evaluation (M-IFEval) benchmark, expanding the evaluation to French, Japanese, and Spanish, with both general and language-specific instructions. Applying this benchmark to 8 state-of-the-art LLMs, we find that benchmark performance across languages and instruction types can vary widely, underscoring the importance of a multilingual benchmark for evaluating LLMs in a diverse cultural context.
Related papers
- Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models [8.020688053947547]
One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions.
This ability, known as instruction-following capability, has established a foundation for the use of LLMs across various fields.
We have noted that LLMs can become easily distracted by instruction-formatted statements, which may lead to an oversight of their instruction comprehension skills.
arXiv Detail & Related papers (2024-12-27T04:37:39Z) - MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models [3.961168847961322]
Large language models (LLMs) are commonly used as evaluators in tasks, where they act as proxies for human preferences or judgments.
Existing benchmarks primarily focus on English, offering limited insight into LLMs' effectiveness as evaluators in non-English contexts.
We introduce MM-Eval, a multilingual meta-evaluation benchmark that covers 18 languages across six categories.
arXiv Detail & Related papers (2024-10-23T06:04:55Z) - Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark [12.729687989535359]
evaluating Large Language Models (LLMs) in languages other than English is crucial for ensuring their linguistic versatility, cultural relevance, and applicability in diverse global contexts.
We tackle this challenge by introducing a structured benchmark using the INVALSI tests, a set of well-established assessments designed to measure educational competencies across Italy.
arXiv Detail & Related papers (2024-06-25T13:20:08Z) - DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
Large Language Models (LLMs) are scalable and economical evaluators.
The question of how reliable these evaluators are has emerged as a crucial research question.
We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
arXiv Detail & Related papers (2024-05-24T08:12:30Z) - Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource 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) - OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large
Language Models [59.54423478596468]
We introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in different languages.
For each language, OMGEval provides 804 open-ended questions, covering a wide range of important capabilities of LLMs.
Specifically, the current version of OMGEval includes 5 languages (i.e., Zh, Ru, Fr, Es, Ar)
arXiv Detail & Related papers (2024-02-21T04:42:41Z) - FollowEval: A Multi-Dimensional Benchmark for Assessing the
Instruction-Following Capability of Large Language Models [42.72420855478716]
FollowEval benchmark is composed of instances in both English and Chinese.
Each test example is designed to evaluate more than one dimension.
We have evaluated various LLMs using the FollowEval benchmark and found that their performance significantly lags behind that of humans.
arXiv Detail & Related papers (2023-11-16T11:53:31Z) - Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization [132.25202059478065]
We benchmark large language models (LLMs) on instruction controllable text summarization.
Our study reveals that instruction controllable text summarization remains a challenging task for LLMs.
arXiv Detail & Related papers (2023-11-15T18:25:26Z) - Instruction-Following Evaluation for Large Language Models [52.90926820437014]
We introduce Instruction-Following Eval (IFEval) for large language models.
IFEval is a straightforward and easy-to-reproduce evaluation benchmark.
We show evaluation results of two widely available LLMs on the market.
arXiv Detail & Related papers (2023-11-14T05:13:55Z) - Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs.
We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
arXiv Detail & Related papers (2023-10-11T16:38:11Z)
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.