Summary of Eliciting Better Multilingual Structured Reasoning From Llms Through Code, by Bryan Li and Tamer Alkhouli and Daniele Bonadiman and Nikolaos Pappas and Saab Mansour
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
by Bryan Li, Tamer Alkhouli, Daniele Bonadiman, Nikolaos Pappas, Saab Mansour
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces xSTREET, a multilingual dataset designed to evaluate large language models (LLMs) on structured reasoning and explanation tasks. The dataset covers four tasks across six languages, including English, and highlights a significant gap in base LLM performance between English and non-English tasks. To improve this, the authors propose a novel method that leverages linguistic and cognitive biases to enhance multilingual LLMs’ capabilities. Empirical evaluations demonstrate the effectiveness of this approach on various benchmarks, indicating promising advancements in multilingual reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops xSTREET, a new dataset for testing language models on reasoning tasks. It looks at four types of questions across six languages. The authors found that current models are much better at answering questions in English than in other languages. To fix this, they came up with a way to make language models more helpful for non-English languages too. |
