Rhetorical Questions in LLM Representations: A Linear Probing Study

2026-04-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A recent study investigates how large language models (LLMs) internally represent rhetorical questions, which are used for persuasion or signaling stance rather than seeking information. Researchers used linear probes on two distinct social media datasets to analyze these representations. The findings indicate that rhetorical signals appear early in LLM processing and are most effectively captured by last-token representations. Rhetorical questions are linearly separable from information-seeking questions within datasets, achieving an AUROC of 0.7-0.8, and remain detectable even with cross-dataset transfer. However, the study reveals that transferability does not imply a single shared representation, as probes trained on different datasets yield distinct rankings on the same target corpus, with top-ranked instance overlap often below 0.2. Qualitative analysis suggests these divergences reflect different rhetorical phenomena, with some probes capturing discourse-level rhetorical stance and others focusing on syntax-driven interrogative acts.

Key takeaway

For research scientists developing or fine-tuning LLMs for nuanced language understanding, recognize that rhetorical questions are not encoded uniformly. You should consider employing multiple linear probes or specialized models to capture the diverse rhetorical phenomena, such as discourse-level stance versus syntax-driven acts, to improve the model's ability to interpret persuasive language accurately.

Key insights

LLMs encode rhetorical questions via multiple linear directions, emphasizing distinct cues rather than a single shared representation.

Principles

• Rhetorical signals emerge early in LLM processing.
• Last-token representations best capture rhetorical signals.
• Linear separability exists between rhetorical and information-seeking questions.

Method

Linear probes were applied to LLM representations of rhetorical questions from social media datasets to analyze their internal encoding and separability from information-seeking questions.

In practice

• Use last-token representations for rhetorical question detection.
• Consider dataset-specific probes for nuanced rhetorical analysis.

Topics

• LLM Representations
• Rhetorical Questions
• Linear Probing
• Social Media Data
• Cross-Dataset Transfer

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.