Explain Like I'm 5 or Whatever I Choose: Evaluating the Interactive Potential of Language Model Responses

2025-11-13 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Research Methodology & Innovation · Depth: Expert, extended

Summary

The article proposes a new evaluation framework for large language models (LLMs), focusing on their ability to generate multiple responses to a single query that vary along an interpretable axis of language complexity. A formative study with 16 participants validated the utility of interactive complexity, identifying jargon, information, and length as key factors. The evaluation tested GPT-5.1, GPT-5 mini, Claude Sonnet 4.5 + Thinking, and DeepSeek-V3.1 on 98 scientific queries, generating 5 responses at different complexity levels. Findings indicate that while models vary complexity across responses, most changes remain inconsistent; Claude Sonnet 4.5, the best performer, reliably shifted complexity measures in the correct direction only 46% of the time for jargon and 33% for information. Models struggled to differentiate responses at higher complexity levels, and increasing sample size or widening audience labels did not significantly alter these inconsistencies.

Key takeaway

For AI Scientists and ML Engineers developing LLM-powered interfaces, you should prioritize robust control over response complexity beyond simple length adjustments. Current models inconsistently manage jargon and information density across complexity levels, even with varied audience prompts. Focus on fine-tuning models to reliably scale these attributes to enable effective direct manipulation interfaces, ensuring user expectations for nuanced complexity changes are met.

Key insights

LLMs inconsistently adjust response complexity, failing to reliably differentiate jargon and information across user-controlled levels.

Principles

• LLM evaluations must incorporate interface-specific criteria beyond static chat.
• Direct manipulation of response complexity is valuable for users.
• Jargon, information, and length are key dimensions of perceived complexity.

Method

A framework evaluating LLMs' ability to generate 5 responses to a query, differing along an interpretable language complexity axis, using measures for jargon, information, and length.

In practice

• Implement direct manipulation sliders for LLM response complexity.
• Focus on consistent jargon and information scaling, not just length.
• Test LLMs with diverse audience anchors for complexity.

Topics

• LLM Evaluation
• Language Complexity
• Human-Computer Interaction
• Direct Manipulation Interfaces
• Scientific Information Seeking
• GPT-5.1, Claude Sonnet 4.5

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• Explain Like I'm 5 or Whatever I Choose: Evaluating the Interactive Potential of Language Model Responses — LLM evaluations must evolve beyond static chat interfaces to incorporate dynamic, interface-specific criteria like controllable language complexity.
• BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design — BED-LLM enhances LLMs' interactive information gathering via sequential Bayesian experimental design to maximize expected information gain.
• How Large Language Models Actually Work (Explained Simply) — LLMs are statistical prediction engines, not reasoning entities, whose capabilities emerge from massive scale and sophisticated attention mechanisms.
• The Context Dilemma: Prompting vs. RAG vs. Fine-Tuning — Prompting, RAG, and fine-tuning are distinct LLM techniques for managing context and grounding, not interchangeable solutions.
• Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost? — Fine-tuning LLMs improves personality questionnaire response stability but fails to induce accurate, multi-dimensional Big Five profiles from unguided text.
• Comparing performance of LLMs is not very interesting — Quantitative LLM performance comparisons quickly become obsolete; qualitative insights offer lasting value.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.