Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information

Original Paper: https://arxiv.org/abs/2408.10615

By: Ming Jiang, Tingting Huang, Biao Guo, Yao Lu, Feng Zhang

Abstract:

In recent years, Large language models (LLMs) have garnered significant attention due to their superior performance in complex reasoning tasks. However, recent studies may diminish their reasoning capabilities markedly when problem descriptions contain irrelevant information, even with the use of advanced prompting techniques. To further investigate this issue, a dataset of primary school mathematics problems containing irrelevant information, named GSMIR, was constructed. Testing prominent LLMs and prompting techniques on this dataset revealed that while LLMs can identify irrelevant information, they do not effectively mitigate the interference it causes once identified. A novel automatic construction method, ATF, which enhances the ability of LLMs to identify and self-mitigate the influence of irrelevant information, is proposed to address this shortcoming. This method operates in two steps: first, analysis of irrelevant information, followed by its filtering. The ATF method, as demonstrated by experimental results, significantly improves the reasoning performance of LLMs and prompting techniques, even in the presence of irrelevant information on the GSMIR dataset.

Summary Notes

Figure: The various prompt formats that were employed were presented, with the use of differently coloured rectangular blocks to represent each component. The rectangular blocks on the right correspond to those of the same colour on the left (using colour coding for easier iden-tification is recommended). The "Or" symbol indicates the option to choose any one of the building blocks. [Questions with irrelevant information] are generated by adding an unrelat-ed sentence (in red font) to the [original question description].

Introduction

Large Language Models (LLMs) like- GPT3 have revolutionized how we approach complex reasoning tasks in natural language processing.

However, these powerful models can stumble when faced with irrelevant information embedded in problem descriptions.

This blog post delves into a recent study that tackles this issue head-on by introducing a novel method called Analysis to Filtration Prompting (ATF).

We'll explore how ATF significantly enhances the robustness of LLMs, ensuring they maintain high accuracy even when bombarded with irrelevant data.

The Challenge: Irrelevant Information

Identifying the Problem

LLMs have shown remarkable prowess in scenarios where problem descriptions are clean and directly related to the solution.

However, in real-world applications, it's common to encounter problem descriptions cluttered with irrelevant information. Previous studies have highlighted that such irrelevant data can severely degrade the reasoning accuracy of LLMs.

To investigate this, a specialized dataset named GSMIR was developed. This dataset includes primary school mathematics problems embedded with irrelevant information, designed to simulate real-world scenarios more accurately.

Key Methodologies

1. Dataset Creation:
• GSMIR: 500 data points were selected from the GSM8K dataset, each augmented with a sentence containing irrelevant information.
• Templates: Irrelevant information was crafted in two forms—numerical (ratios, percentages) and opinion-based (subjective judgments).
2. Prompting Techniques:
• Various prompting techniques were tested, including Standard Prompting (SP), Chain-of-Thought (COT) prompting, Zero-shot COT (0-COT), Least-to-Most (LTM) prompting, and Instructed Prompting (IP).

Main Findings

Identifying vs. Excluding Irrelevant Information

The study revealed that while LLMs can identify irrelevant information with a decent success rate, they struggle to exclude this information during the process reasoning.

This results in a significant drop in reasoning accuracy.

ATF Methodology: Two-Phase Approach

To address this shortfall, the researchers proposed the ATF method, which operates in two phases:

1. Analysis Phase:
• LLMs are guided to break down the problem description into multiple clauses.
• Each clause is analyzed to determine if it contains irrelevant information.
• The model provides reasons for its conclusions, creating a demonstration that guides future analyses.
2. Filtration Phase:
• Prompts guide the LLMs to filter out identified irrelevant information from the problem description.
• The revised problem description is then used for reasoning, significantly improving accuracy.

Experimental Results

The experimental results were compelling. When evaluated on the GSMIR dataset, the ATF method substantially improved the reasoning accuracy of LLMs across all prompting techniques. For instance, the accuracy of COT prompting increased from 55.2% to 74.9% with ATF.

Implications and Real-World Applications

Enhancing Robustness

The ATF method enhances the robustness of LLMs, making them more reliable in real-world scenarios where irrelevant information is prevalent. This has significant implications for various applications, including:

• Educational Tools: Automated tutoring systems can better handle noisy student inputs.
• Customer Support: Chatbots can more effectively parse and respond to user queries that contain extraneous details.
• Data Analysis: Enhanced LLMs can provide more accurate insights from data-rich environments.

Future Research Directions

While ATF has shown significant improvements, the current study focused on scenarios with a single piece of irrelevant information.

Real-world data often contain multiple pieces of irrelevant information, presenting a greater challenge.

Future research should explore methods to handle such complexities and evaluate different LLMs to further validate the effectiveness of ATF.

Conclusion

The ATF method represents a significant advancement in enhancing the robustness of LLMs against irrelevant information.

By effectively identifying and filtering out noise, ATF ensures that LLMs maintain high reasoning accuracy, making them more practical and reliable for real-world applications.

As we continue to refine these techniques, the potential for LLMs to revolutionize various fields becomes increasingly tangible.

Quote from the Researchers:

"Our findings indicate that LLMs, although proficient at identifying irrelevant information, require targeted methods like ATF to effectively exclude such information and enhance reasoning performance."

Let's look forward to more innovations that will continue to push the boundaries of what LLMs can achieve!