REVIEW 5 cited by
PMC-LLaMA: Towards Building Open-source Language Models for Medicine
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recently, Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this paper, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA. Our contributions are threefold: (i) we systematically investigate the process of adapting a general-purpose foundation language model towards medical domain, this involves data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive fine-tuning for alignment with domain-specific instructions; (ii) we contribute a large-scale, comprehensive dataset for instruction tuning. This dataset encompasses medical question-answering (QA), rationale for reasoning, and conversational dialogues, comprising a total of 202M tokens; (iii) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component. While evaluating on various public medical question-answering benchmarks, our lightweight PMCLLaMA, which consists of only 13 billion parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, datasets can be found in https://github.com/chaoyi-wu/PMC-LLaMA.
Forward citations
Cited by 5 Pith papers
-
Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models
LLMs more often reproduce older medical conclusions than updated ones, as shown by a new 512-question dataset of Cochrane reviews whose verdicts changed over time.
-
WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management
A new wheat-specific dataset with pretraining, quantitative, and instruction-tuning layers improves VLM performance on wheat stress diagnosis and growth-stage management tasks.
-
MedBookVQA: A Systematic and Comprehensive Medical Benchmark Derived from Open-Access Book
MedBookVQA is a new 5,000-question, textbook-derived multimodal benchmark for testing medical AI systems, with labels for imaging modality, body anatomy, and clinical specialty.
-
CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics
A collaborative data-selection method that scores each private sample's influence on a public anchor set and filters by a global threshold before federated learning or model merging.
-
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Fine-tuning a 1B medical chatbot on LLM-rewritten emotional dialogues improves its emotion scores with only small changes in n-gram overlap with the original medical responses.
Discussion (0). Sign in to comment.