Rhetorical Questions in LLM Representations: A Linear Probing Study
Summary: arXiv:2604.14128v1 Announce Type: cross
Rhetorical questions have long been a subject of interest in linguistic studies, primarily due to their unique function of persuasion rather than information-seeking. Despite their prevalence, the internal representation of such questions in large language models (LLMs) remains poorly understood. Recent research aims to elucidate how these models interpret rhetorical questions by employing linear probing techniques on two distinct social-media datasets that offer varying discourse contexts.
Key Findings
- Emergence of Rhetorical Signals: The study finds that rhetorical signals appear early in the representations of LLMs and are most consistently captured by the last-token representations.
- Linear Separability: Rhetorical questions can be linearly separated from traditional information-seeking questions within the analyzed datasets, showcasing a significant distinction in their representation.
- Cross-Dataset Transfer: The ability to detect rhetorical questions remains robust even under cross-dataset transfer conditions, achieving an Area Under the Receiver Operating Characteristic (AUROC) score ranging from 0.7 to 0.8.
- Variability in Transferability: Despite the successful transferability, the findings indicate that this does not equate to a shared representation across datasets. Probes trained on different datasets often yield disparate rankings when applied to a common target corpus.
- Qualitative Divergences: The qualitative analysis reveals that these differences in ranking can be traced back to distinct rhetorical phenomena. While some probes capture discourse-level rhetorical stance, others focus on localized, syntax-driven interrogative acts.
Implications of the Study
These findings suggest that LLM representations of rhetorical questions are not governed by a singular representation; rather, they are encoded through multiple linear directions that prioritize various cues. This nuanced understanding could have significant implications for the development of more effective LLMs and their applications in natural language processing tasks.
Future Directions
Future research may delve deeper into the underlying mechanisms that enable LLMs to discern and represent rhetorical questions. Understanding how different datasets influence the representation of rhetorical phenomena can provide insights into improving model training and enhancing their interpretative capabilities. Moreover, exploring additional discourse contexts beyond social media could yield valuable data, further refining the understanding of rhetorical question representation in LLMs.
In conclusion, the study offers a crucial step towards deciphering the complex interactions between language models and rhetorical constructs, paving the way for advancements in both theoretical linguistics and practical AI applications.