Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information (2408.10615v1)

Abstract: In recent years, 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.

• The paper introduces the ATF method, a two-step approach that analyzes and filters out irrelevant information to enhance LLM performance.
• It presents the GSMIR dataset, which incorporates thematically aligned distractions to better mimic real-world problem scenarios.
• Experimental results demonstrate significant improvements, with accuracy rising from 50.2% to 74.9% using the ATF method.

Enhancing Robustness in LLMs: Prompting for Mitigating the Impact of Irrelevant Information

The paper "Enhancing Robustness in LLMs: Prompting for Mitigating the Impact of Irrelevant Information" by Ming Jiang et al. addresses a crucial challenge in the domain of LLMs: their susceptibility to irrelevant contextual information during reasoning tasks. The research highlights an advanced methodology aimed at bolstering the resilience of LLMs when confronted with non-essential information in problem descriptions, a scenario frequently encountered in real-world applications.

Key Contributions

1. GSMIR Dataset Creation:
   • The authors introduce the GSMIR dataset, designed to include irrelevant information in mathematical problems derived from the GSM8K dataset. Unlike previous datasets, GSMIR incorporates irrelevant content that is more thematically aligned and logically connected to the problem, thus mimicking real-world distractions more effectively.
2. Investigation of LLMs' Limitations:
   • The research exhaustively investigates why LLMs underperform in the presence of irrelevant information, identifying two critical capabilities: the identification of irrelevant information and the self-exclusion of such information during the reasoning process.
3. Analysis to Filtration Prompting (ATF):
   • To mitigate the identified limitations, the authors propose the ATF method, a two-step process comprising analysis and filtration. This technique guides LLMs in breaking down problem descriptions to identify irrelevant information and subsequently filter it out before engaging in the reasoning process. This approach is shown to significantly enhance the model's reasoning accuracy even when irrelevant information is present.

Detailed Examination of Methodologies

Identification and Exclusion of Irrelevant Information

The paper initially assesses the capability of LLMs in identifying irrelevant content by introducing prompts that guide the model to highlight and explain such information. Results from this assessment reveal that while LLMs can recognize irrelevant information, they fail to effectively exclude it during subsequent reasoning tasks. The authors use various prompting strategies and demonstrate this challenge through empirical evaluation on the newly constructed GSMIR dataset.

Analysis to Filtration Prompting (ATF) Method

The ATF method is proposed to enhance the robustness of LLMs. It involves:

1. Analysis:
   • LLMs are prompted to decompose the problem description into individual clauses and analyze each for irrelevance. This step leverages the model's ability to reason about context and provides justifications for excluding identified irrelevant information.
2. Filtration:
   • Based on the analysis, LLMs are then guided to remove identified irrelevant information from the question before making a decision. This is done using prompt-based mechanisms that ensure only the relevant content remains, thereby improving the model's accuracy in reasoning tasks.

Experimental Validation and Results

The efficacy of the ATF method is tested across various prompting techniques, including Standard Prompting (SP), Chain of Thought (COT), and its zero-shot variations (0-COT). The experimental results demonstrate a significant improvement in the reasoning accuracy of LLMs:

• For instance, introducing ATF with COT improved accuracy from 50.2% to 74.9% on the GSMIR dataset.
• Comparatively, integrating ATF with the LTM technique resulted in an accuracy improvement to 69.9%, closely mirroring performance on the original GSM8K dataset without irrelevant information.

These enhancements underscore the resilience imparted by the ATF method against non-essential information, demonstrating its overarching efficacy across different LLMs and prompting frameworks.

Implications and Future Directions

The implications of this research are manifold. Practically, enhancing the robustness of LLMs against irrelevant information can significantly improve their performance in real-world applications where such distractions are commonplace. Theoretically, this paper paves the way for further exploration into advanced prompting techniques and their role in refining the contextual intelligence of LLMs.

Future research could extend this work by exploring scenarios with multiple pieces of irrelevant information, a limitation acknowledged by the authors. Moreover, applying the ATF method across different LLM architectures and evaluating its generalizability could provide deeper insights into its broader applicability.

Conclusion

Ming Jiang et al.'s work presents a substantial contribution to the field of Natural Language Processing, offering a nuanced understanding of why LLMs falter in the presence of irrelevant information and providing a robust methodology to address this issue. The proposed ATF method not only enhances the reasoning capabilities of LLMs but also sets the stage for future advancements in making these models more resilient and contextually aware.