Description
We tested ChatGPT on 25 tasks focusing on solving common NLP problems and requiring analytical reasoning. These tasks include (1) a relatively simple binary classification of texts like spam, humor, sarcasm, aggression detection, or grammatical correctness of the text; (2) a more complex multiclass and multi-label classification of texts such as sentiment analysis, emotion recognition; (3) reasoning with the personal context, i.e., personalized versions of the problems that make use of additional information about text perception of a given user (user’s examples provided to ChatGPT); (4) semantic annotation and acceptance of the text going towards natural language understanding (NLU) like word sense disambiguation (WSD), and (5) answering questions based on the input text. More information in the paper: https://www.sciencedirect.com/science/article/pii/S156625352300177X
Citations (1)
- https://doi.org/10.1016/j.inffus.2023.101861DataCite MDC
Cited on 01 November 2023
Weight: 1.00
Mentions (0)
No mentions found
Metrics Over Time
Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
52%
Source
Scholar Data Model