Eliciting Better Multilingual Structured Reasoning from LLMs through Code

• URL: http://arxiv.org/abs/2403.02567v2
• Date: Wed, 12 Jun 2024 07:13:01 GMT
• Title: Eliciting Better Multilingual Structured Reasoning from LLMs through Code
• Authors: Bryan Li, Tamer Alkhouli, Daniele Bonadiman, Nikolaos Pappas, Saab Mansour,
• Abstract summary: We introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. We propose two methods to remedy this gap, building on the insight that LLMs trained on code are better reasoners.
• Score: 17.870002864331322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks. We then propose two methods to remedy this gap, building on the insight that LLMs trained on code are better reasoners. First, at training time, we augment a code dataset with multilingual comments using machine translation while keeping program code as-is. Second, at inference time, we bridge the gap between training and inference by employing a prompt structure that incorporates step-by-step code primitives to derive new facts and find a solution. Our methods show improved multilingual performance on xSTREET, most notably on the scientific commonsense reasoning subtask. Furthermore, the models show no regression on non-reasoning tasks, thus demonstrating our techniques maintain general-purpose abilities.

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