Growing Concern: Unreliability of Large Language Models.

Published Date::05/10/2024

A recent study reveals that large language models, including ChatGPT, LLaMA, and BLOOM, are becoming increasingly unreliable from a human perspective. Despite advances in artificial intelligence, these models are prone to errors and inconsistencies, raising concerns about their use in high-risk applications.

According to researchers at the Valencian Institute for Research in Artificial Intelligence (VRAIN) and the University of Cambridge, one of the main concerns about the reliability of language models is that their performance does not match human perception of task difficulty. In other words, there is a mismatch between expectations that the models will fail based on human perception of task difficulty and the tasks on which the models fail.

The study found that recent language models are much more likely to provide incorrect answers rather than avoid giving answers to tasks they are unsure of. This can lead users who initially rely too much on the models to be disappointed. Moreover, unlike people, the tendency to avoid providing answers does not increase with difficulty.

The researchers also discovered that human supervision is unable to compensate for these problems. For example, people can recognise tasks of high difficulty but still frequently consider incorrect results correct in this area, even when they are allowed to say 'I'm not sure', indicating overconfidence.\n\nThe results were similar for multiple families of language models, including OpenAI's GPT family, Meta's open-weighted LLaMA, and BLOOM, a fully open initiative from the scientific community.

Ultimately, large language models are becoming increasingly unreliable from a human point of view, and user supervision to correct errors is not the solution, as we tend to rely too much on models and cannot recognise incorrect results at different difficulty levels. Therefore, a fundamental change is needed in the design and development of general-purpose AI, especially for high-risk applications, where predicting the performance of language models and detecting their errors is paramount.

FAQS:

Q: What is the main concern about the reliability of language models?

A: The main concern is that their performance does not match human perception of task difficulty.

Q: Why are language models prone to errors and inconsistencies?

A: Language models are prone to errors and inconsistencies because they are designed to provide answers even when they are unsure, and human supervision is unable to compensate for these problems.

Q: What are the implications of the unreliability of language models?

A: The unreliability of language models has significant implications for high-risk applications, where predicting the performance of language models and detecting their errors is paramount.

Q: What is the solution to the unreliability of language models?

A: A fundamental change is needed in the design and development of general-purpose AI, especially for high-risk applications.

Q: Which language models were studied in the research?

A: The study examined multiple families of language models, including OpenAI's GPT family, Meta's open-weighted LLaMA, and BLOOM, a fully open initiative from the scientific community.

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