Verbosity preference is associated with systematic variation in LLM response length¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Response word count varied systematically across verbosity preference categories, consistent with the dimension's intended effect. → Saptarshi Basu 2026

Evidence¶

Saptarshi Basu 2026¶

Saptarshi Basu, Sandeep Kakar, Ashok Goel. (2026). A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant. https://arxiv.org/abs/2609.03402 doi:10.48550/arxiv.2609.03402

q2 · i? · design · r2

Numerical result shown in Figure 2 from the same 2,910-response generation experiment: response word counts (lexicon_count) plotted across verbosity preference categories. The article states the variation was "systematic" and treats it as evidence for RQ3.

"Figure 2 shows systematic variation in response length across verbosity preferences, providing evidence that the"

Discussion¶

Related Claims¶

• Prompt-based personalization of an LLM/RAG teaching assistant produces measurable variation in response expression, length, and complexity across personalization conditions — a broader claim this one bears on
• The three fine-tuned LLMs differ significantly in response length, readability, and similarity to posts — related
• Implicit demographic cues produce larger and less predictable effects in open-ended tasks, including longer responses for laptop users and education-related length effects — related
• Higher self-assessment, verbosity, reflective processing, and technical abstraction are associated with greater response complexity, and Evaluation and Analysis questions tend to yield more complex responses — related