The Turing Test is a measure of a machine's ability to exhibit intelligent behavior that is indistinguishable from that of a human. It was first proposed by the British mathematician and computer scientist, Alan Turing, in 1950 as a way to test a machine's ability to think and communicate like a human.
In the Turing Test, a human evaluator engages in a natural language conversation with a machine and a human. The evaluator does not know which entity is the machine and which is the human, and must decide which is which based solely on the responses to their questions. If the machine can successfully convince the evaluator that it is the human, then it is said to have passed the Turing Test.
The Turing Test can also be used to evaluate the quality of the machine-generated text. If the machine-generated text is of high enough quality to fool the evaluator into thinking it was written by a human, it can be said to have passed the Turing Test for natural language generation.
Today, researchers have proposed additional measures for evaluating the quality of machine-generated text. They include:
Accuracy: The text should be accurate and not misrepresent any facts.
Coherence: This refers to how well the generated text flows together and makes sense as a whole. A coherent text should have a clear structure and logical flow.
Relevance: The question here is how well the generated text meets the user's needs and objectives. It only becomes relevant if it provides information that is useful and meaningful to the reader.
Fluency: This refers to how well the generated text reads and sounds like natural language. A fluent text should be grammatically correct and use appropriate language and style for the audience.
These measures can be used in combination to evaluate the quality of natural language generation and help identify areas for improvement. Let’s review where we stand.
Fluency: GPT-3 is known for producing very fluent and natural-sounding texts. In this respect, GPT-4 (essentially ChatGPT) sets new standards. The ability of these models to produce grammatically correct texts that use appropriate language and style has been repeatedly praised. Given the current state of the art and the advances expected in the near future, this criterion appears to be practically met.
There are aspects that are more difficult to get by:
In terms of accuracy, even the most capable models can produce text that is factually inaccurate or contains errors. Because of the way these models have been designed and trained, this should not be surprising. However, it can be problematic for applications that require high levels of accuracy such as in banking & finance.
Coherence is another challenge. Sometimes the generated text lacks a clear structure or has inconsistencies in its content. This can make it difficult to use for applications that require a high degree of stringency, such as investment writing.
Digipal has placed great emphasis on accuracy, ensured by an analytical layer. Coherence is achieved by means of a predetermined (and adaptable) text structure. Furthermore, we address the criterion of relevance through algorithms that dynamically decide which statements are essential for understanding the overall result.
Ensuring accuracy, coherence and relevance serves as a solid foundation to leverage language models in the current form and as they become even more powerful.
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