摘要： [目的/意義]探討ChatGPT等大規(guī)模預(yù)訓(xùn)練語(yǔ)言模型在網(wǎng)絡(luò)健康信息識(shí)別中的應(yīng)用效果,為人工智能在健康信息領(lǐng)域的應(yīng)用提供參考。[方法/過(guò)程]以國(guó)內(nèi)某權(quán)威辟謠平臺(tái)與健康相關(guān)的信息為研究對(duì)象,使用“ChatGPT”和“訊飛星火”對(duì)其真實(shí)性進(jìn)行鑒定,對(duì)其性能進(jìn)行評(píng)估,并將鑒定結(jié)果與醫(yī)學(xué)專(zhuān)家或權(quán)威機(jī)構(gòu)的鑒定結(jié)果進(jìn)行比較。[結(jié)果/結(jié)論]ChatGPT和訊飛星火的鑒別準(zhǔn)確率分別為93.9%和92.9%,F1值分別為0.951和0.946,應(yīng)用效果良好。兩者生成的解釋文本內(nèi)容比較詳細(xì),語(yǔ)言比較流暢,文本長(zhǎng)度和語(yǔ)義相似度與專(zhuān)家文本高度接近,但對(duì)個(gè)別信息的解釋仍存在科學(xué)依據(jù)不夠詳細(xì)、邏輯錯(cuò)誤等問(wèn)題。實(shí)驗(yàn)結(jié)果表明,大規(guī)模預(yù)訓(xùn)練語(yǔ)言模型在輔助網(wǎng)絡(luò)健康信息識(shí)別任務(wù)方面具有一定的優(yōu)勢(shì),但仍需要人工干預(yù)以保證結(jié)果的準(zhǔn)確性和可靠性。

關(guān)鍵詞: 人工智能, 健康信息, 鑒別, ChatGPT

Abstract: [Purpose/Significance] Taking the popular "chat robot" ChatGPT and the recently launched similar product "iFLYTEK Spark" as the research object, this paper explores their applications in the identification of online health information, and discusses their advantages and disadvantages, in order to provide reference for the large-scale pre-training language model in the field of health information identification. Based on the review of relevant literature on online health information authentication, deep learning models have been widely applied in the task of online health information authentication in recent years. With the rapid development of large pre-training language models such as ChatGPT, it is a novel idea to explore their discriminating ability in online health information. [Method/Process] Researchers selected health-related information from the most authoritative rumor-refuting websites in China, used "ChatGPT" and "iFLYTEK Spark" to verify the authenticity of the online health information, evaluated their performance, and compared their identification results with the expert identification results. The identification accuracy of ChatGPT and iFLYTEK Spark language model was 93.9% and 92.9%, respectively, and the F1 value was 0.951 and 0.946, respectively, which had a good application effect. The generated explanatory texts were more detailed and the language was relatively smooth. In terms of the length and dispersion of the explanatory text, ChatGPT is closer to that of medical experts, while iFLYTEK Spark's explanatory text is relatively long and less discrete. In terms of semantic similarity, ChatGPT and iFLYTEK Spark were almost equal in performance, and their understanding of health information was close to that of human experts to some extent. Through the analysis of typical samples, it can be seen that an AI large model cannot accurately identify news or emergency information for the time being, and the understanding of individual health propositions with complex semantics will occasionally be biased. [Results/Conclusions] The experimental results show that ChatGPT and iFLYTEK Spark have good discriminative effect in the field of online health information identification, but there are shortcomings, and manual intervention is needed to ensure the accuracy and reliability of the results. In the future, in the field of AI large model research, researchers are suggested to attach importance to the construction and application of high-quality corpora in vertical fields. In the field of online health information identification, practitioners can use models such as ChatGPT as tools to help identify and refine health information. There are also limitations in this article. For example, the amount of data involved in the test is not large enough, ChatGPT uses GPT3.5 model, and the online application time of iFLYTEK Spark language model is relatively short. In future studies, the amount of online health information can be further increased, and the updated version of an AI large model can be tested and evaluated.

Key words: artificial intelligence, health information, identification, ChatGPT

中圖分類(lèi)號(hào): 

王超, 孔祥輝. 大型預(yù)訓(xùn)練語(yǔ)言模型在網(wǎng)絡(luò)健康信息鑒別中的應(yīng)用探討[J]. 農(nóng)業(yè)圖書(shū)情報(bào)學(xué)報(bào), 2023, 35(6): 51-59.

WANG Chao, KONG Xianghui. Application of Large-scale Pre-Training Language Model in Network Health Information Identification[J]. Journal of Library and Information Science in Agriculture, 2023, 35(6): 51-59.

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