"Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using Large Language Models"

Making LLMs understand domain expertise by breaking down concepts systematically

Two novel methods help LLMs solve complex conceptual problems by augmenting them with conceptual information, achieving significant improvements over existing techniques like Chain-of-Thoughts.

https://arxiv.org/abs/2412.15309

Original Problem 🤔:

→ LLMs struggle with complex conceptual problems that require specific domain knowledge and reasoning capabilities

→ Current shallow customization methods like In-Context Learning and Chain-of-Thoughts often lead to hallucinations and poor performance

→ LLMs lack crucial conceptual information needed for solving engineering and science problems

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Solution in this Paper 💡:

→ The paper introduces two new shallow customization methods: Conceptual In-Context Learning (C-ICL) and Chain of Concepts (CoC)

→ C-ICL augments LLMs with conceptual information through single-shot prompting

→ CoC incrementally introduces concepts through multiple inter-related prompts using a directed acyclic graph structure

→ The methods were tested on proprietary data model generation tasks

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Key Insights 🔍:

→ Complex conceptual problems require specific domain expertise beyond general language understanding

→ Incremental concept introduction leads to better reasoning capabilities

→ Structured concept hierarchies improve LLM performance on domain-specific tasks

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Results 📊:

→ 30.6% improvement in response correctness with C-ICL compared to Chain-of-Thoughts

→ 29.88% improvement with CoC

→ Significant reduction in hallucinations and parroting issues

→ Better semantic quality and syntactic correctness in generated outputs