Draft:Generative AI and LLM prompt evaluations
| Submission declined on 31 May 2024 by Hey man im josh (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
Evaluation[edit]
Evaluation is a systematic determination of a subject's merit, worth, and significance, using criteria governed by a set of standards. It is a critical component in the development and refinement of various programs, projects, products, and policies.
Importance of Evaluation[edit]
Evaluation plays a vital role, particularly in the development of generative AI (GenAI) applications. While casual users of AI chatbots, such as ChatGPT, may not immediately recognize the need for prompt evaluation, it becomes indispensable for developers in the AI field. The outputs of Large Language Models (LLMs) can be unpredictable and varied, which can negatively impact user experience and product competitiveness[1]
Effective evaluation ensures that the models and prompts used in AI applications are thoroughly tested and refined before reaching the end users. This proactive approach helps in avoiding reliance on user-driven trial and error, which can be time-consuming and detrimental to user satisfaction[2]
Challenges in AI Evaluation[edit]
Despite the impressive reasoning capabilities of LLMs, their inconsistent outputs necessitate rigorous evaluation. Many existing prompt evaluation tools, such as OpenAI Evals, are tailored more for engineers and often lack a graphical user interface (GUI), posing accessibility challenges for non-technical team members Additionally, some tools only address parts of the evaluation process and do not offer a comprehensive solution
Comprehensive Evaluation Tools[edit]
Several evaluation tools have been developed to address these challenges. The most effective tools aim to simplify the evaluation process by providing easy-to-use interfaces that do not require terminal commands, making them accessible for all team members. They cover the entire evaluation process, from sample preparation and model configuration to establishing evaluation metrics and analyzing results
Among these, tools such as EvalsOne stand out for their systematic approach, allowing development teams to focus on the logical and creative aspects of their work rather than being bogged down by mundane tasks. These tools have proven effective in improving the efficiency and enjoyment of the development process for their users, setting a high standard in the field of AI evaluation.
References[edit]
- ^ Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-01). "On the Dangers of Stochastic Parrots: Can Language Models be Too Big? 🦜". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7.
- ^ Bommasani, Rishi; Hudson, Drew A.; Adeli, Ehsan; Altman, Russ; Arora, Simran; von Arx, Sydney; Bernstein, Michael S.; Bohg, Jeannette; Bosselut, Antoine (2022-07-12), On the Opportunities and Risks of Foundation Models, arXiv:2108.07258