Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

• URL: http://arxiv.org/abs/2304.09138v1
• Date: Tue, 18 Apr 2023 17:21:48 GMT
• Title: Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
• Authors: Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
• Abstract summary: We evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty.
• Score: 49.50140712943701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) GPT-4 outperforms ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model that is capable of solving various tasks across different domains is feasible.

Related papers

• Evaluating the Performance of Large Language Models for SDG Mapping (Technical Report) [6.789534723913505]
  Large language models (LLMs) enable users to protect data privacy by eliminating the need to provide data to third parties. We compare the performance of various language models on the Sustainable Development Goal mapping task. According to the results of this study, LLaMA 2 and Gemma still have significant room for improvement.
  arXiv  Detail & Related papers  (2024-08-05T03:05:02Z)
• Large Language Models as Zero-shot Dialogue State Tracker through Function Calling [42.00097476584174]
  We propose a novel approach for solving dialogue state tracking with large language models (LLMs) through function calling. This method improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. We show that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs.
  arXiv  Detail & Related papers  (2024-02-16T06:13:18Z)
• MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models [70.92847554971065]
  We introduce MT-Eval, a comprehensive benchmark designed to evaluate multi-turn conversational abilities. By analyzing human-LLM conversations, we categorize interaction patterns into four types: recollection, expansion, refinement, and follow-up. Our evaluation of 11 well-known LLMs shows that while closed-source models generally surpass open-source ones, certain open-source models exceed GPT-3.5-Turbo in specific tasks.
  arXiv  Detail & Related papers  (2024-01-30T04:50:28Z)
• Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases [98.35348038111508]
  This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision) The core of our analysis delves into the distinct visual comprehension abilities of each model. Our findings illuminate the unique strengths and niches of both models.
  arXiv  Detail & Related papers  (2023-12-22T18:59:58Z)
• Exploring the Boundaries of GPT-4 in Radiology [46.30976153809968]
  GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.
  arXiv  Detail & Related papers  (2023-10-23T05:13:03Z)
• Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task [17.25356594832692]
  We present an analysis of GPT-3.5 (ChatGPT) and GPT-4 performances on COLIEE Task 4 dataset. Our preliminary experimental results unveil intriguing insights into the models' strengths and weaknesses in handling legal textual entailment tasks.
  arXiv  Detail & Related papers  (2023-09-11T14:43:54Z)
• GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts [0.0]
  Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. In this paper, we examine the performance of GPT-3.5, GPT-4, and BARD models, by performing a thorough technical evaluation on different reasoning tasks.
  arXiv  Detail & Related papers  (2023-05-21T14:45:17Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)
• A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models [71.42197262495056]
  GPT series models have gained considerable attention due to their exceptional natural language processing capabilities. We select six representative models, comprising two GPT-3 series models and four GPT-3.5 series models. We evaluate their performance on nine natural language understanding (NLU) tasks using 21 datasets. Our experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve.
  arXiv  Detail & Related papers  (2023-03-18T14:02:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.