CogniDual Framework for LLMs: Advancing Language Models from Deliberate Reasoning to Intuitive Responses Through Self-Training
Practical Solutions and Value
Cognitive psychology studies how humans process information, and language models (LMs) like GPT-4 aim to mimic human thinking. The challenge is to make LMs generate accurate responses without explicit instructions, similar to human intuition.
Researchers have proposed the CogniDual Framework for LLMs (CFLLMs) to help LMs evolve from deliberate reasoning to intuitive responses through self-training. This approach reduces computational demands and improves response accuracy, especially for natural language inference tasks.
The CFLLMs aim to answer key questions about LMs’ reasoning capabilities and whether they can develop intuitive responses without explicit guidance. The experiments show that LMs can improve their reasoning abilities through self-training, similar to human cognition.
The CFLLMs demonstrated substantial performance improvements, especially on natural language inference tasks, without explicit prompting. This indicates the potential for LMs to develop intuitive responses through practice, similar to human System 1 thinking.
Overall, the CFLLMs offer an innovative approach to advancing LMs’ reasoning capabilities and transforming them into more intuitive problem solvers. Future efforts should focus on addressing current limitations and exploring the impact of CFLLMs on cognitive processing preferences of LMs.
AI Solutions for Business
If you want to evolve your company with AI, consider using the CogniDual Framework for LLMs to redefine your way of work. Identify automation opportunities, define KPIs, select AI solutions, and implement gradually to ensure measurable impacts on business outcomes.
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