Summary of Conceptual In-context Learning and Chain Of Concepts: Solving Complex Conceptual Problems Using Large Language Models, by Nishtha N. Vaidya et al.
Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using Large Language Models
by Nishtha N. Vaidya, Thomas Runkler, Thomas Hubauer, Veronika Haderlein-Hoegberg, Maja Mlicic Brandt
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes two novel shallow customization methods for Large Language Models (LLMs) to solve complex conceptual problems, specifically generating proprietary data models in the engineering/industry domain. The methods, Conceptual In-Context Learning (C-ICL) and Chain of Concepts (CoC), aim to augment LLMs with concept-related information, enabling them to tackle tasks like assisted problem-solving. The authors evaluate their algorithms on various sizes of OpenAI LLMs using four evaluation metrics related to correctness, time, and cost. The results show that the proposed methods outperform existing SCMs, including In-context Learning (ICL) and Chain of Thoughts (CoT), with a 30.6% and 29.88% increase in response correctness for C-ICL and CoC, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to help Large Language Models solve complex problems by adding specific information about concepts. It proposes two new methods to make language models better at understanding and generating text related to engineering and industry. The authors test these methods on different sized language models and show that they work better than existing methods, which is important for tasks like problem-solving. |
