思想的内在情感
The Inner Sentiments of a Thought
AI总结：
本文首次利用LLM隐藏表征训练情感分布预测器，分析句子内在情感轨迹并生成极端情感句子，揭示思想内在运作机制。
摘要：
基于Transformer的大规模语言模型（LLMs）能够生成高度逼真的文本。它们相应地能够表达并至少隐式地表征广泛的情感与色彩，从明显的，如效价和唤醒度，到微妙的，如决心和钦佩。我们对这些表征及其如何用于理解单个句子的内在情感运作进行了首次探索。我们从一个LLM应用于逐渐增长长度的前缀所得到的隐藏表征中，训练句子最终情感分布的分位数预测器。在展示了效价、决心、钦佩、焦虑和烦恼的分布预测器均得到良好校准之后，我们提供了使用这些预测器分析句子的示例，例如说明即使是普通的连词（如“but”）也能极大地改变一句话的情感轨迹。随后，我们展示了如何利用这些分布预测来生成情感处于分布尾部的句子。我们讨论了我们的结果对思想内在运作的启示，例如对精神功能障碍的启示。
英文摘要：
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. After showing that predictors of distributions of valence, determination, admiration, anxiety and annoyance are well calibrated, we provide examples of using these predictors for analyzing sentences, illustrating, for instance, how even ordinary conjunctions (e.g., "but") can dramatically alter the emotional trajectory of an utterance. We then show how to exploit the distributional predictions to generate sentences with sentiments in the tails of distributions. We discuss the implications of our results for the inner workings of thoughts, for instance for psychiatric dysfunction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
