AI Governance and Safety

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Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
Jan 14, 2025Shengsheng Zhou, Shuai Wang, Liang Ding +5Code Generation Factual Recall
From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go
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Growing a Tail: Increasing Output Diversity in Large Language Models
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From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
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The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
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The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures
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The Biggest Risk of Embodied AI is Governance Lag
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Builder, Defender, Breaker: Measurable Independence and Bounded Autonomy When Generative Models Build, Defend and Test Software
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Ceci n'est pas une pipe: AI systems as semantic abstractions
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ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems
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False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK
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Jan 14, 2025 · cs.SEJ/K move · Enter open · S save

Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models

Shengsheng Zhou, Shuai Wang, Liang Ding, Yibing Zhan+4

College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China · The University of Sydney, Sydney, Australia · Yunnan United Vision Technology Company Ltd., China · School of Computer Science, Wuhan University, Wuhan, China

Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness. However, few-shot methods depend on curated or manually crafted reference examples, limiting their applicability in data-free coding scenarios such as real-world data-free coding scenarios and benchmarks without training sets. Existing methods that generate reference examples via recitation or analogy cannot guarantee their authenticity or accuracy. Inspired by human metamemory, we propose a novel metamemory agent to enhance one-time code generation in data-free coding scenarios. The agent guides LLMs to recall relevant prior knowledge, evaluate confidence in recalled information, and selectively exploit reliable content for problem solving. This agent removes the need for external reference examples, improves the authenticity and accuracy of recalled knowledge, and adaptively tailors the recall&evaluation process to each task. Extensive experiments demonstrate that the proposed metamemory agent significantly improves one-time code generation quality across data-free coding scenarios. The AI contribution is the metamemory agent, which makes self-recalled examples reliable through confidence evaluation and selection; the engineering application is data-free automated code generation, validated on eight public benchmarks.

Code Generation Factual Recall Artificial Intelligence Systems

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