mlsanityData to 14 Oct 2026

From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT

arXiv 2405.11040 ↗First posted 17 May 2024cs.CL physics.med-phcited by 1

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requiring fine-tuning or extensive retraining. ARCoT integrates a retrieval mechanism to access relevant domain-specific information and employs step-back and chain-of-thought prompting techniques to guide the LLM's reasoning process, ensuring more accurate and context-aware responses. Benchmarking on a medical physics multiple-choice exam, our model outperformed standard LLMs and reported average human performance, demonstrating improvements of up to 68% and achieving a high score of 90%. This method reduces hallucinations and increases domain-specific performance. The versatility and model-agnostic nature of ARCoT make it easily adaptable to various domains, showcasing its significant potential for enhancing the accuracy and reliability of LLMs in specialized fields.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

22 of 33 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  6. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  7. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 752
  8. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 722
  9. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 639
  10. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
  11. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  12. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
10 more
  1. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  2. 2023LongLoRA: Efficient Fine-tuning of Long-Context Large Language Modelscited by 244
  3. 2023A Study of Generative Large Language Model for Medical Research and Healthcarecited by 163
  4. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 159
  5. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrieverscited by 84
  6. 2023Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMscited by 62
  7. 2023Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physicscited by 49
  8. 2024Fine-tuning Large Language Models for Domain-specific Machine Translationcited by 43
  9. 2020How fine can fine-tuning be? Learning efficient language modelscited by 41
  10. 2023Semantic Compression With Large Language Modelscited by 17

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICscited by 10

Similar, not cited

Close in wording, no citation either way

  1. 2024A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options
  2. 2024Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
  3. 2025Performance of Large Language Models in Supporting Medical Diagnosis and Treatment
  4. 2024Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications
  5. 2025ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiography
  6. 2026When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models
  7. 2024A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
  8. 2025Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning