MLissard: Multilingual Long and Simple Sequential Reasoning Benchmarks

TL;DR

MLissard is a multilingual benchmark that assesses language models' ability to handle long, simple sequences and reveals performance drops with increasing complexity, highlighting the benefit of multilingual in-context learning.

Contribution

Introduces MLissard, a new benchmark for evaluating multilingual models on long sequence reasoning and complexity control, with insights into cross-lingual in-context learning benefits.

Findings

Performance declines as sequence complexity increases across models and languages.

In-context examples in non-English languages improve extrapolation performance.

Benchmark and code are publicly available for further research.

Abstract

Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens. However, they often fail on tasks that require repetitive use of simple rules, even on sequences that are much shorter than those seen during training. For example, state-of-the-art LLMs can find common items in two lists with up to 20 items but fail when lists have 80 items. In this paper, we introduce MLissard, a multilingual benchmark designed to evaluate models' abilities to process and generate texts of varied lengths and offers a mechanism for controlling sequence complexity. Our evaluation of open-source and proprietary models show a consistent decline in performance across all models and languages as the complexity of the sequence increases. Surprisingly, the use of in-context examples in languages other than English helps increase extrapolation performance significantly. The datasets and code are available at https://github.com/unicamp-dl/Lissard