Identifying and Ignoring Irrelevant Conditions in Math Word Problems with Large Language Models

Alapfogalmak

Instructing large language models to identify and ignore irrelevant conditions improves accuracy in solving math word problems.

Kivonat

This article introduces a novel approach, I3C, that instructs large language models (LLMs) to identify and ignore irrelevant conditions in math word problems. By selecting the most confusing problems as demonstrations, I3C significantly enhances the performance of LLMs in solving complex multi-step reasoning tasks. Extensive experiments on eight math word problem datasets demonstrate the effectiveness and efficiency of this method.

Abstract:

• Existing chain-of-thought prompting methods struggle with irrelevant conditions.
• I3C proposes a novel approach to instruct LLMs to identify and ignore irrelevant conditions.
• Demonstrations are used to enhance few-shot learning abilities.

Introduction:

• Math word problem solving requires multi-step reasoning abilities.
• CoT prompting methods guide LLMs but can be confused by irrelevant conditions.
• I3C aims to improve reasoning paths by identifying and ignoring irrelevant conditions.

Proposed Approach:

• I3C identifies irrelevant condition candidates based on semantic relevance.
• LLMs verify if candidates are indeed irrelevant.
• A novel instruction is created to help LLMs avoid confusion and improve reasoning paths.

Experiments:

• I3C combined with CoT methods improves performance on various MWP datasets.
• I3C-Select outperforms existing prompting methods by selecting confusing problems as demonstrations.

Statisztikák

I3C achieves an accuracy of 96.0% on GSM-IC2-1K with GPT-3.5-Turbo.
I3C significantly outperforms Complex-CoT by +11.7% on GSM-IC2-1K.

Idézetek

"Instructing Large Language Models to Identify and Ignore Irrelevant Conditions."
"I3C can be combined with any CoT prompting methods to improve the performance of solving MWPs."

Főbb Kivonatok

Instructing Large Language Models to Identify and Ignore Irrelevant Conditions

by Zhenyu Wu,Ch... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12744.pdf

Mélyebb kérdések

How does the selection of confusing problems impact the effectiveness of I3C?

The selection of confusing problems plays a crucial role in enhancing the effectiveness of I3C. By choosing the most challenging and perplexing problems as demonstrations, I3C can provide LLMs with valuable training data to improve their problem-solving capabilities. These confusing problems help LLMs understand how to identify and ignore irrelevant conditions effectively, leading to better reasoning paths and accurate answers. Selecting such problematic instances ensures that the model is exposed to diverse scenarios, enabling it to learn robust strategies for handling complex math word problems.

What are the implications of using demonstrations for few-shot learning in improving LLM performance?

Using demonstrations for few-shot learning has significant implications for enhancing LLM performance in solving math word problems. Demonstrations serve as valuable examples that guide LLMs on how to approach different types of problems and generate accurate reasoning paths. By providing these demonstrations during training, LLMs can learn from expert solutions without requiring extensive human annotations or task-specific fine-tuning. This approach enables models like I3C-Select to leverage existing knowledge efficiently and adapt quickly to new problem instances, ultimately improving their ability to solve MWPs accurately with minimal supervision.

How might the concept of identifying and ignoring irrelevant information apply beyond math word problems?

The concept of identifying and ignoring irrelevant information is not limited to math word problems but can be applied across various domains where understanding context is essential. In natural language processing tasks like text summarization or sentiment analysis, models need to distinguish between relevant and irrelevant details within a given text. By instructing models on how to identify key information while disregarding extraneous content, they can produce more concise summaries or accurate sentiment classifications.
Moreover, in image recognition tasks, filtering out noise or background elements that are not pertinent to object detection or classification can significantly enhance model performance. Models trained with instructions similar to those used in I3C could focus on relevant features while discarding distractions present in images.
Overall, teaching AI systems how to recognize what is important within a contextually rich environment extends far beyond math word problems and has broad applications across various fields where discerning relevance is critical for successful outcomes.