Chatbots: a critical look into the future of the academia
Bibliographic record
Abstract
Like every other societal domain, science faces yet another reckoning caused by a bot called ChatGPT (Chat Generative Pre-Trained Transformer). ChatGPT was introduced in November 2022 to produce messages that seem like they were written by humans and are conversational. With the release of the latest version of ChatGPT called GPT-4, and other similar models such as Google Bard, Chatsonic, Collosal Chat, these chatbots combine several (about 175 billion) neural networks pre-trained on large Language Models (LLMs), allowing them to respond to user promptings just like humans. GPT-4 for example can admit its mistakes and confront false assumptions thanks to the dialogue style, which also enables it to write essays and to keep track of the context of a discussion while it is happening. However, users may be deceived by the human-like text structure of the AI models to believe that it came from a human origin[1]. These chatbot models could be better, even though they generate text with a high level of accuracy. Occasionally, they produce inappropriate or wrong responses, resulting in faulty inferences or ethical issues. This article will discuss some fundamental strengths and weaknesses of this Artificial intelligence (AI) system concerning scientific research. Keywords: ChatGPT, AI generative models, academia, ethical and moral restraints DOI: 10.25165/j.ijabe.20241702.9075 Citation: Okaiyeto S A, Mujumdar A S, Sutar P P, Liu W, Bai J W, Xiao H W. Chatbots: a critical look into the future of the academia. Int J Agric & Biol Eng, 2024; 17(2): 287–288.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".