Optimizing Chain-of-Thought Prompts to Improve Large Language Model Performance on Lateral Thinking Puzzles

Core Concepts

An iterative system for optimizing chain-of-thought prompts can significantly improve large language model performance on lateral thinking puzzles that require creative, "outside the box" problem-solving.

Abstract

The paper proposes a novel iterative system for optimizing chain-of-thought (CoT) prompting on the GPT-4 model to tackle the sentence puzzle subtask of the BRAINTEASER shared task, which tests lateral thinking abilities. The key steps of the system are: Generating naive CoT prompts by randomly sampling the training data. Identifying distinct categories in the model's output reasoning to partition the training data. Performing independent human evaluation to isolate specific challenges in each category. Using the findings to inform the development of new, more targeted CoT prompts. Optionally, identifying gaps in the data to guide future data collection and synthesis. The iterative prompt engineering process significantly improves performance on the adversarial datasets, which are designed to prevent memorization. The system also provides insights into the dataset itself, identifying questions with multiple logical options or that are unanswerable given the provided premises. By combining model reasoning with human evaluation, the authors are able to quickly identify and address problematic questions, leading to more consistent results that suggest the model relies less on memorization when using the optimized CoT prompts.

Stats

"Even though his clothes were completely drenched, not a single hair on his head was moist." "The ground outside the building is wet." "Consistent exercise has made him a very strong man." "The rope stretches proportionally, providing the extra length needed for the horse to reach the hay ten meters away."

Quotes

"Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks." "By combining model reasoning with human evaluation, we can quickly identify and evaluate problematic questions. This process can further explain model performance and provide guidance for future data collection/generation." "Not only does this process optimize CoT prompting for a specific task, our system also provides insights for improving future data collection and synthesis."

Key Insights Distilled From

Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization

by Alvin Po-Chu... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02517.pdf

Deeper Inquiries

How could this iterative prompt engineering system be extended to other types of reasoning tasks beyond lateral thinking puzzles?

The iterative prompt engineering system developed for optimizing chain-of-thought prompts can be extended to various reasoning tasks beyond lateral thinking puzzles by adapting the methodology to suit the specific requirements of different tasks. For tasks that involve logical inference, the system can be modified to focus on generating prompts that encourage sequential reasoning and deduction. For tasks involving common sense knowledge, prompts can be designed to test the model's ability to apply everyday knowledge to solve problems. By tailoring the prompt optimization process to the unique characteristics of each task, the system can effectively enhance the performance of large language models across a wide range of reasoning challenges.

What are the limitations of using human evaluation as a benchmark, and how could the system be improved to rely less on subjective human judgments?

Human evaluation as a benchmark has limitations due to the subjective nature of human judgments, which can introduce bias and inconsistency in the evaluation process. To reduce reliance on subjective human judgments, the system could incorporate multiple evaluators for each question and use statistical methods to aggregate their responses. Additionally, introducing objective metrics for evaluating model performance, such as logical consistency, coherence, and accuracy, can provide more reliable benchmarks. Implementing automated evaluation techniques, such as leveraging pre-defined criteria for assessing reasoning abilities, can further reduce the subjectivity of human judgments and enhance the system's reliability.

What other techniques, beyond chain-of-thought prompting, could be explored to enhance large language models' abilities to solve creative, lateral thinking problems?

Beyond chain-of-thought prompting, several techniques can be explored to enhance large language models' abilities to solve creative, lateral thinking problems. One approach is to incorporate external knowledge sources, such as structured knowledge graphs or domain-specific databases, to provide additional context for reasoning. Another technique is to implement reinforcement learning algorithms that reward the model for generating novel and creative solutions. Additionally, leveraging generative adversarial networks (GANs) to train the model on generating diverse and imaginative responses can enhance its lateral thinking capabilities. By combining these techniques with iterative prompt optimization, large language models can be equipped to tackle a wide range of creative problem-solving tasks effectively.