Decomposed Prompting: Unveiling Multilingual Linguistic Structure in Large Language Models

Core Concepts

The author introduces the decomposed prompting approach to probe the linguistic structure understanding of English-centric Large Language Models (LLMs) in sequence labeling tasks, demonstrating its efficacy and efficiency over iterative prompting. This method offers insights into the multilingual transferability of English-centric LLMs.

Abstract

The study explores the effectiveness of decomposed prompting in evaluating multilingual structural knowledge in English-centric LLMs. It surpasses iterative prompting methods, showcasing improved accuracy and efficiency. The research delves into the nuances of multilingual performance, highlighting the impact of language proximity to English and script types on model performance. Additionally, a comparison between English-centric and multilingual LLMs reveals varying strengths based on linguistic proximity and base capabilities.
The investigation provides valuable insights into the cross-lingual transfer capabilities of large language models, shedding light on their proficiency in multilingual tasks. By dissecting sequence labeling processes into token-level prompts, the study enhances understanding of LLMs' linguistic structure knowledge beyond their primary training language. The findings emphasize the importance of considering trade-offs between linguistic breadth and depth when selecting foundational models for diverse language tasks.

Stats

Mistral 7B model achieves superior performance and efficiency through advanced attention techniques like Sliding Window Attention.
Mistral 7B model fine-tuned on OpenHermes 2.5 dataset.
BLOOMZ is a multi-task fine-tuned variant trained on 46 languages with approximately 7 billion parameters.
mTk-Instruct is a multilingual encoder-decoder model with around 13 billion parameters.
Decomposed prompting outperforms iterative prompting across zero- and few-shot settings for English-centric LLMs.

Quotes

"Prompting methods have seldom been applied to sequence labeling tasks."
"Our method outperforms existing iterative prompting techniques in both zero- and few-shot settings."
"The study provides valuable insights into the cross-lingual transfer capabilities of large language models."

Key Insights Distilled From

Decomposed Prompting

by Erco... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18397.pdf

Deeper Inquiries

How do evaluation methodologies impact our understanding of large language models?

Evaluation methodologies play a crucial role in assessing the performance and capabilities of large language models (LLMs). The choice between probability-based and generation-based evaluation methods can significantly impact our understanding of LLMs. Probability-based evaluation, which relies on model output logits to determine the most likely prediction, provides a more accurate reflection of multilingual understanding abilities compared to generation-based evaluation, which solely considers the generated text. Access to internal representations through probability measurements allows for deeper insights into how LLMs process information and make predictions. This access enables researchers to analyze behavior, interpretability, and even apply LLMs for Bayesian inference tasks.

What are the implications of choosing between an English-centric LLM or a multilingual LLM for diverse language tasks?

The decision between using an English-centric LLM or a multilingual LLM has significant implications for diverse language tasks. English-centric LLMs excel in understanding English linguistic structures due to their predominant training data but may struggle with languages distant from English. On the other hand, multilingual LLMs offer broader language coverage and enhanced cross-lingual transferability but may lack refined proficiency in specific languages like English.
For tasks requiring robust multilingual skills such as reasoning or commonsense understanding across various languages, choosing a multilingual LLM might be advantageous despite potential limitations in depth of language understanding compared to an English-centric model. Researchers must weigh the trade-offs between linguistic breadth and depth when selecting foundational models for diverse language tasks.

How can decomposed prompting be further enhanced to address limitations related to recurring words with different POS tags?

To address limitations related to recurring words with different POS tags in decomposed prompting, several enhancements can be considered:

Context-Aware Prompting: Develop prompts that consider contextual dependencies within sentences rather than treating each token independently.

Token-Level Context Encoding: Incorporate mechanisms that capture token-level context information during prompt generation to differentiate tokens based on their syntactic roles within sentences.

Dynamic Prompt Generation: Implement dynamic prompt generation strategies that adapt based on previous predictions or contextual cues within sequences containing recurring words with varying POS tags.

Fine-Tuning Strategies: Explore fine-tuning techniques that focus on improving model sensitivity towards subtle differences in word usage contexts leading to distinct POS tagging requirements.

By incorporating these enhancements into decomposed prompting methodology, it is possible to overcome challenges associated with handling recurring words exhibiting multiple POS tag assignments effectively during sequence labeling tasks conducted by large language models like GPT-3 and others mentioned in the context provided above.