Maximizing Use-Case Specificity through Precision Model Tuning

• URL: http://arxiv.org/abs/2212.14206v1
• Date: Thu, 29 Dec 2022 07:50:14 GMT
• Title: Maximizing Use-Case Specificity through Precision Model Tuning
• Authors: Pranjali Awasthi, David Recio-Mitter, Yosuke Kyle Sugi
• Abstract summary: We present an in-depth analysis of the performance of four transformer-based language models on the task of biomedical information retrieval. Our findings suggest that smaller models, with 10B parameters and fine-tuned on domain-specific datasets, tend to outperform larger language models on highly specific questions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models have become increasingly popular in recent years for tasks like information retrieval. As use-cases become oriented toward specific domains, fine-tuning becomes default for standard performance. To fine-tune these models for specific tasks and datasets, it is necessary to carefully tune the model's hyperparameters and training techniques. In this paper, we present an in-depth analysis of the performance of four transformer-based language models on the task of biomedical information retrieval. The models we consider are DeepMind's RETRO (7B parameters), GPT-J (6B parameters), GPT-3 (175B parameters), and BLOOM (176B parameters). We compare their performance on the basis of relevance, accuracy, and interpretability, using a large corpus of 480000 research papers on protein structure/function prediction as our dataset. Our findings suggest that smaller models, with <10B parameters and fine-tuned on domain-specific datasets, tend to outperform larger language models on highly specific questions in terms of accuracy, relevancy, and interpretability by a significant margin (+50% on average). However, larger models do provide generally better results on broader prompts.

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