The Inner Sentiments of a Thought

Understand

Transformer-based large-scale language models (LLMs) are able to generate highly realistic text.

• They are duly able to express, and at least implicitly represent, a wide range of sentiments and color, from the obvious, such as valence and arousal to the subtle, such as determination and admiration.
• We provide a first exploration of these representations and how they can be used for understanding the inner sentimental workings of single sentences.
• We train predictors of the quantiles of the distributions of final sentiments of sentences from the hidden representations of an LLM applied to prefixes of increasing lengths.