Large Language Models (LLMs) can self-compose reasoning structures** tailored to specific tasks, solving problems more efficiently and effectively. This self-discovery process allows LLMs to select, adapt, and implement relevant reasoning modules, creating a coherent structure that guides the model in solving the task. The discovered reasoning structures are task-specific and convey the LLM's insights about the task more interpretably than optimized prompts. This self-discovery process is inspired by how humans devise a reasoning program for problem-solving.
Large Language Models Self-Compose Reasoning Structures," here is a bullet-point summary organized for readability:
Self-Composing Reasoning Structures: Large language models (LLMs) have developed the capability to autonomously generate and refine reasoning pathways to address complex problems or narratives.
Autonomy and Adaptability: This signifies a significant advancement towards AI systems that are more autonomous, adaptable, and capable of independent problem-solving.
Mimicking Human Cognitive Processes: The models' ability to self-compose reasoning structures mirrors aspects of human thought and reasoning, suggesting a closer alignment with human cognitive capabilities.
Implications for Computational Creativity: This development is particularly impactful for computational creativity, where AI can independently develop innovative solutions or creative outputs.
Enhanced Decision-Making Systems: Autonomy in reasoning could revolutionize decision-making systems by enabling AI to generate novel solutions to complex issues without direct human structuring of the reasoning process.
Future of AI Development: The article underscores a potential future where AI not only aids in solving predetermined problems but also independently identifies and addresses new challenges through self-composed reasoning structures.
SELF-DISCOVER Framework
Stage 1: Self-Discover Task-Specific Structures
SELECT: Choose relevant reasoning modules from a set of descriptions.
ADAPT: Rephrase module descriptions to fit the task.
IMPLEMENT: Convert adapted descriptions into a structured plan.
Stage 2: Solve Problems Using Discovered Structure
Use the discovered structure to solve each task instance.
Instruct the model to follow the structure and fill in key-value pairs.
Benefits:
Improved Performance: Self-discovered structures significantly enhance the problem-solving capabilities of GPT-4 and PaLM 2.
Universality: Structures discovered by one model can be applied to other models, such as GPT-4 to Llama2.
Commonalities with Human Reasoning: The structures share similarities with human reasoning patterns, demonstrating the LLM's ability to develop task-specific problem-solving strategies.
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