Adapting While Learning: Grounding LLMs for Scientific Problems with
  Intelligent Tool Usage Adaptation
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.
67282af8fab7cbfa93389808
Bohan Lyu
claimed_verified
2024-11-04T09:36:08.498Z
650267e7e751d03da933a24a
/avatars/f047a047d1de304cd97027463541bdf3.svg
Bohan22
user
Bohan22
67282af8fab7cbfa93389809
Yadi Cao
67282af8fab7cbfa9338980a
Duncan Watson-Parris
67282af8fab7cbfa9338980b
Leon Bergen
admin_assigned
2024-11-04T14:24:29.881Z
6303f60ca362e7e8b51d60cd
/avatars/235be39550fb4fc760aa644df1d5e2d8.svg
Leon Bergen
user
lbergen
67282af8fab7cbfa9338980c
Taylor Berg-Kirkpatrick
admin_assigned
2024-11-04T14:24:35.794Z
66845caa4b898a41b7af775c
/avatars/e205290cd372b6b464509de279f6642d.svg
Taylor Berg-Kirkpatrick
user
tberg12
67282af8fab7cbfa9338980d
Rose Yu
extracted_pending
2024-11-04T02:01:29.621Z
65566b1294d4cb86b0f1d625
/avatars/995525ad3b282ea32171615030f7bdcf.svg
Rose Yu
user
yuqirose
Adapting While Learning: Grounding LLMs for Scientific Problems with
  Intelligent Tool Usage Adaptation
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.
A novel fine-tuning method for Large Language Models improves answer accuracy by combining world knowledge distillation with intelligent tool usage adaptation, outperforming existing models across various scientific domains.
Large Language Models World Knowledge Distillation Tool Usage Adaptation scientific benchmark datasets mathematics climate science epidemiology answer accuracy tool usage precision