Written By Introduction
The elaboration of Language Models, particularly Large Language Models( LLMs) like ChatGPT, has sparked significant interest in their capabilities. This paper aims to give a comprehensive evaluation frame for ChatGPT, specifically assessing its multitask, multilingual, and multimodal eventuality.
Methodology
We outline the methodologies used for data collection and evaluation across 23 different NLP tasks covering eight operation areas. The evaluation is extended to the new multimodal dataset, exploring ChatGPT’s capability to induce content from textual prompts via an intermediate law generation step.
Multitask Evaluation
In this section, we present the results of the multitask evaluation, showcasing ChatGPT’s performance compared to other LLMs in zero- shot literacy and forfeiture- tuned models in colorful NLP tasks.
Multilingual Evaluation
The multilingual aspect of ChatGPT is examined, assaying its proficiency in understanding and generatingnon-Latin script languages.
Multimodal Evaluation
ChatGPT’s multimodal capabilities are assessed, studying its eventuality in generating multimodal content from textual prompts.
logic Evaluation
This section delves into ChatGPT’s logic capacities, assaying its delicacy across ten logic orders, including logical,non-textual, and firm logic.
Hallucination Issues
The paper uncovers daydream problems in ChatGPT and explores its generation of foreign visions from its parametric memory due to the lack of access to an external knowledge base.
Interactivity and mortal Collaboration
The interactive point of ChatGPT is delved , revealing its eventuality for mortal collaboration and its impact on performance enhancement.
Conclusion
The paper concludes by recapitulating the findings of the comprehensive evaluation of ChatGPT’s multitask, multilingual, and multimodal capabilities. It highlights its strengths in zero- shot literacy and deducible logic while relating areas for enhancement, similar asnon-Latin script languages and inductive logic. The eventuality for daydream and the benefits of interactivity are also bandied, emphasizing the significance of mortal collaboration in enhancing performance. The released codebase for evaluation set birth contributes to the exploration community and opens avenues for farther advancements in LLMs.
