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ARXIV:2604.14128 · LLM REPRESENTATIONS · SUBMITTED 16 APR · 18:18 UTC · FRESHNESS STALE
ARXIV:2604.14128LLM REPRESENTATIONSSUBMITTED 16 APR · 18:18 UTCFRESHNESS STALELouie Hong Yao · Vishesh Anand · Yuan Zhuang · Tianyu Jiang · arXiv
Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions.
Opportunity summary
Pain Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions. How large language models internally represent them remains unclear.
Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, we demonstrate that transferability does not simply imply a shared representation. Code availability is flagged in the production record; the public repository link…
LLM Representations moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions.
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Paper Pack
10.48550/arXiv.2604.14128Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions.
Abstract
Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts, and find that rhetorical signals emerge early and are most stably captured by last-token representations. Rhetorical questions are linearly separable from information-seeking questions within datasets, and remain detectable under cross-dataset transfer, reaching AUROC around 0.7-0.8. However, we demonstrate that transferability does not simply imply a shared representation. Probes trained on different datasets produce different rankings when applied to the same target corpus, with overlap among the top-ranked instances often below 0.2. Qualitative analysis shows that these divergences correspond to distinct rhetorical phenomena: some probes capture discourse-level rhetorical stance embedded in extended argumentation, while others emphasize localized, syntax-driven interrogative acts. Together, these findings suggest that rhetorical questions in LLM representations are encoded by multiple linear directions emphasizing different cues, rather than a single shared direction.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions. How large language models internally represent them remains unclear.
METHOD
Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, we demonstrate that transferability does not simply imply a shared representation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
LLM Representations moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions. How large language models internally represent them remains unclear.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, we demonstrate that transferability does not simply imply a shared representation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Representations moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Analyzing how LLMs internally represent rhetorical questions using linear probing, revealing that these signals emerge early and are encoded by multiple, context-dependent directions.
Segment
LLM Representations
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
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passport absent
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stale
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Technical feasibility
partial
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Run minimal reproduction from the Build Passport prototype path.
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Write integration checklist from prototype path and target workflow.
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