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MasonTigers Submission at SemEval-2024 Task 9: Solving Puzzles with Large Language Models

Core Concepts
Large language models excel in complex reasoning tasks when guided by explanatory prompts like chain-of-thought, showcasing their latent potential for advanced reasoning.
Abstract: MasonTigers' submission to SemEval-2024 Task 9 focused on solving puzzles using large language models. Utilized large language models with prompting techniques to achieve competitive results. Demonstrated the effectiveness of chain-of-thought prompting in breaking down complex reasoning processes. Introduction: Large Language Models have shown impressive performance but struggle with complex reasoning tasks. SemEval-2024 Task 9 introduced BrainTeaser dataset to evaluate reasoning capabilities. Various prompting strategies were explored to guide models through puzzles. Experiments: Zero-shot, few-shot, and chain-of-thought prompting strategies were employed. Results showed improved performance with more specific prompts and steps. BrainTeaser Dataset: Designed to evaluate lateral thinking abilities of models through multiple-choice puzzles. Results: GPT4 Turbo outperformed other models, especially with CoT prompting and ensemble method. Conclusion: MasonTigers' approach achieved competitive rankings by leveraging structured thought processes and explanatory prompts.
During the test phase, a total of 240 prompts (120 for both sentence and word puzzles) are provided. During the test phase, a total of 216 prompts (120 for sentence and 96 for word puzzles) are provided.
"Our work sheds light on how step-wise explanatory prompts can unlock more of the knowledge encoded in the parameters of large models." "Ultimately, our system achieved highly competitive rankings on the leaderboard, placing 2nd on word puzzles and 13th on sentence puzzles."

Key Insights Distilled From

by Md Nishat Ra... at 03-25-2024
MasonTigers at SemEval-2024 Task 9

Deeper Inquiries

How can automated prompting processes improve scalability in guiding large language models?

Automated prompting processes can significantly enhance scalability in guiding large language models by reducing the manual effort required to construct effective prompts. By automating the prompt generation process, researchers and developers can streamline the task of providing structured input to these models, making it easier to scale up experiments and applications. Automated prompts can be generated based on predefined templates or rules, leveraging natural language processing techniques to create diverse and contextually relevant inputs for the models. Furthermore, automation allows for rapid iteration and testing of different prompt formats, enabling researchers to explore a wide range of strategies efficiently. This iterative approach helps in identifying the most effective prompts that lead to improved model performance. Additionally, automated prompting processes can facilitate consistency in prompt design across different experiments and datasets, ensuring reproducibility and comparability of results. Overall, automated prompting processes offer a more efficient way to guide large language models by saving time and resources while promoting standardization and systematic exploration of prompt variations.

What are the limitations that need to be addressed to overcome model performance lagging behind human levels?

Several limitations need addressing to bridge the gap between model performance and human-level reasoning abilities: Long-term Memory: Large language models often struggle with retaining information over extended contexts due to architectural constraints like limited memory capacity. Enhancements in memory mechanisms or novel architectures may be necessary to enable better retention of crucial details during complex reasoning tasks. Reasoning Depth: While current models excel at surface-level understanding, they often lack deep reasoning capabilities required for multi-step inference tasks like those presented in puzzles or brainteasers. Developing mechanisms that allow for more intricate logical deductions could help improve overall performance. Domain Knowledge: Models may falter when faced with tasks requiring specific domain expertise not explicitly present in training data. Incorporating external knowledge bases or designing specialized pre-training strategies could mitigate this limitation. Generalization: Ensuring that reasoning skills learned through prompts are transferable across various domains is essential for building more generalizable NLP systems capable of handling diverse tasks effectively. Human-like Intuition: Human cognition involves intuitive leaps based on experience; replicating this nuanced decision-making process remains a challenge for AI systems relying heavily on statistical patterns.

How can developing more generalizable reasoning skills through prompts enhance natural language processing systems?

Developing more generalizable reasoning skills through prompts is crucial for enhancing natural language processing (NLP) systems' adaptability across diverse tasks and domains: Transfer Learning: By training models with prompts designed around generalized reasoning principles rather than task-specific instructions, they become adept at applying learned logic flexibly across various scenarios without extensive retraining. 2 .Robustness: Generalizable reasoning skills acquired through diverse prompts equip NLP systems with robust problem-solving capabilities resilient against noise or outliers commonly encountered in real-world data. 3 .Interpretability: Prompts emphasizing generalizable concepts foster interpretable model behavior as decisions are grounded on transparent logical steps rather than opaque black-box predictions. 4 .Few-shot Learning: Enhanced generalizability enables quicker adaptation during few-shot learning scenarios where minimal examples are available by leveraging broader conceptual understanding acquired through varied prompts. 5 .Adaptation: NLP systems equipped with generalized reasoning skills exhibit greater adaptability when encountering new challenges or unseen data distributions beyond their initial training scope. 6 .Ethical Considerations: Promoting generalizability via reasoned-based prompts aids in mitigating biases inherent within narrow-task-focused training paradigms by encouraging holistic thinking patterns rooted in broader cognitive frameworks. By focusing on developing versatile reasoning abilities through well-crafted prompts encompassing fundamental principles applicable across multiple contexts, NLP systems stand poised towards achieving higher levels of sophistication and proficiency akin to human-level comprehension capacities."