Researchers at Austria’s University of Innsbruck have published a paper probing the application of temporal validity in generative artificial intelligence (AI) systems, stating that the benchmark could bring changes to the ecosystem.
Temporal validity is the relevancy of a statement to another about the progression of time. For AI systems, this metric allows models to identify the time-based value of statements, a key functionality that sets models apart.
In the 18-page research paper, AI models have demonstrated sufficient capability in spotting the duration of temporal validity in simple statements. However, in the presence of additional contextual information, generative AI models vary in their abilities to identify temporal validity in the statements.
To effectively measure the abilities of large language models (LLMs) to follow temporal validity in complex statements, the researchers unveiled a benchmarking system using data gleaned from X (formerly Twitter).
“We propose Temporal Validity Change Prediction, a natural language processing task benchmarking the capability of machine learning models to detect contextual statements that induce such change,” read the report.
After creating a data set from X, the researchers tested temporal validity duration prediction on several mainstream generative AI models. In their submission, OpenAI‘s ChatGPT failed to impress with its temporal common sense (TCS) capabilities, with the researchers pointing to the systems adopted in training the chatbot.
“ChatGPT ranks among the lower-performing models, which is consistent with other studies on TCS understanding,” read the paper. “Its short-comings may be due to the few-shot learning approach and a lack of knowledge about dataset specifics traits.”
The paper pointed to several use cases for AI models with advanced TCS, including utilities in financial market predictions and generating news stories from social media posts. Other use cases for AI chatbots include improving their abilities to track knowledge that is still necessary while new inputs are evaluated to determine relevance.
AI research reaches new heights
Over the last few months, groundbreaking research into AI and LLMs has been published, poking holes into the abilities of frontier models. One study pointed out that mainstream AI models favor sycophancy over factual responses, given their reliance on reinforcement learning from human feedback (RLHF) in model training.
Another 2023 study uncovered a chatbot glitch that allows terrible actors to access employees’ details by repeating a single word, forcing the model to derail from its alignment training.
Other studies probed the application of blockchain with AI models to improve users’ trust, privacy, and security.
In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.
In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.
Watch: What does blockchain and AI have in common? It’s data
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