Muhammad Younas

30/11/2024

11/11/2024

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[Submitted on 1 Nov 2024]
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Bohan Lyu, Yadi Cao, Duncan Watson-Parris, Leon Bergen, Taylor Berg-Kirkpatrick, Rose Yu
Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in over-reliance on tools, diminishing the model's ability to solve simple problems through basic reasoning. In contrast, human experts first assess problem complexity using domain knowledge before choosing an appropriate solution approach. Inspired by this human problem-solving process, we propose a novel two-component fine-tuning method. In the first component World Knowledge Distillation (WKD), LLMs learn directly from solutions generated using tool's information to internalize domain knowledge. In the second component Tool Usage Adaptation (TUA), we partition problems into easy and hard categories based on the model's direct answering accuracy. While maintaining the same alignment target for easy problems as in WKD, we train the model to intelligently switch to tool usage for more challenging problems. We validate our method on six scientific benchmark datasets, spanning mathematics, climate science and epidemiology. On average, our models demonstrate a 28.18% improvement in answer accuracy and a 13.89% increase in tool usage precision across all datasets, surpassing state-of-the-art models including GPT-4o and Claude-3.5.
Comments:26 pages, 15 figuresSubjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)ACM classes:I.2.6; I.2.7Cite as:arXiv:2411.00412 [cs.LG] (or arXiv:2411.00412v1 [cs.LG] for this version) https://lnkd.in/d6YQ44iy

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10/10/2024

AI Pioneers Win Nobel Prize in Physics
John J. Hopfield and Geoffrey E. Hinton, pioneers in artificial intelligence, have won the 2024 Nobel Prize in Physics for their groundbreaking work on neural networks. Their research has revolutionized how machines learn and process information, leading to breakthroughs in fields like image recognition and language translation.

Why the Nobel Prize in Physics for AI?
The Nobel Prize in Physics is typically awarded for discoveries that advance our understanding of the physical world. While AI may seem like a field of computer science, its foundations lie in the principles of physics and mathematics.
Hopfield and Hinton's groundbreaking research on artificial neural networks, inspired by the human brain, has revolutionized how machines learn and process information. Their work has bridged the gap between computer science and physics, making significant contributions to our understanding of intelligence and computation.
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09/10/2024

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04/07/2024

Grateful to have completed the Google AI Essentials program!
Expanding my skillset to stay ahead in the exciting world of AI.

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This certificate verifies my successful completion of Google's "Google AI Essentials" on Coursera

13/06/2024

We're hiring a Motion Graphic Designer & Video Editor.
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26/11/2023

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22/11/2023

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05/07/2023

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