Know Your Audience: Do LLMs Adapt to Different Age and Education Levels? (2312.02065v1)

Abstract: LLMs offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs (commercial and open-source) to science questions when prompted to target different age groups and education levels. To assess the adaptability of LLMs to diverse audiences, we compare the readability scores of the generated responses against the recommended comprehension level of each age and education group. We find large variations in the readability of the answers by different LLMs. Our results suggest LLM answers need to be better adapted to the intended audience demographics to be more comprehensible. They underline the importance of enhancing the adaptability of LLMs in education settings to cater to diverse age and education levels. Overall, current LLMs have set readability ranges and do not adapt well to different audiences, even when prompted. That limits their potential for educational purposes.

• The paper examines four LLMs’ adaptability using 33,600 prompt-response pairs and established readability metrics.
• It employs Flesch-Kincaid and BERT classifier evaluations to assess how well texts match specific age and education levels.
• Findings reveal commercial models favor older, more educated audiences, highlighting a 15% success rate in tailored outputs.

Essay on "Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?"

The paper "Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?" by Rooein, Curry, and Hovy critically examines the adaptability of LLMs to different readership demographics based on age and educational attainment. As LLMs grow increasingly integral in educational contexts, understanding their capacity to tailor responses to diverse audience needs becomes imperative.

Research Overview

The authors investigate four prominent LLMs—two commercial and two open-source—to assess their ability to generate text that aligns with specified readability requirements. By utilizing established readability metrics, the paper evaluates how well these models customize responses to science-related questions, adjusted for distinct age groups and educational levels.

Methodology

The research employs a methodological framework involving the collection of 33,600 prompt-response pairs. The researchers prompt the models using various age, education, and difficulty settings, then analyze these responses with the Flesch-Kincaid Reading Ease Index among other readability metrics. They also include a classifier evaluation using BERT to discern any latent linguistic signals related to target demographics, thus appraising the robustness of conventional readability assessments.

Key Findings

The findings underscore a substantial limitation in the models' adaptability, with an overall success rate of merely 15% in producing appropriately readapted material for targeted audience groups. This indicates a predefined "style" in model outputs that persists irrespective of tailored prompts. Notably, commercial models like ChatGPT and GPT3 tend to target older, more educated demographics in their outputs regardless of prompt specifications, suggesting a mismatch with readability aims for younger or less educated audiences.

Implications

The results carry significant educational implications—LLMs are not yet reliably capable of adjusting text complexity to suit the desired readership. The limited adaptability could restrict the utility of LLMs in educational domains where audience-specific communication is critical.

Future Directions

For future inquiries in AI, exploring advanced fine-tuning techniques and developing novel readability metrics tailored for AI outputs are essential steps. Enhancing LLM adaptability requires addressing nuances in reader demographics beyond mere readability scores, perhaps by integrating more sophisticated models or introducing domain-specific datasets that reflect varied linguistic needs across educational tiers.

Conclusion

The paper raises pivotal concerns about the current state of LLMs in educational environments, marking a need for ongoing refinement. As LLMs continue to gain prominence, developing comprehensive strategies to enhance audience adaptability is vital, ensuring that educational resources powered by AI are both accessible and effective for diverse learner profiles.