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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

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Fostering effective hybrid human-LLM reasoning and decision making.

Andrea Passerini¹, Aryo Gema², Pasquale Minervini²

¹Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.

Frontiers in Artificial Intelligence

|January 23, 2025

Summary

This perspective highlights the need for more research into human-Large Language Model (LLM) collaboration. Focusing on human-LLM interaction can improve AI

Keywords:

LLMs biases complementary team performance human-AI collaboration hybrid intelligence mutual understanding

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Area of Science:

Artificial Intelligence
Human-Computer Interaction

Background:

Modern Large Language Models (LLMs) demonstrate impressive capabilities but also exhibit non-trivial errors.
Despite significant research into LLM limitations, the dynamics and implications of human-LLM collaboration are underexplored.

Purpose of the Study:

To argue for prioritizing research on human-LLM interaction.
To examine biases hindering effective human-machine collaboration.
To discuss goals for enhancing human-LLM reasoning and decision-making.

Main Methods:

Perspective piece examining existing literature and proposing future research directions.
Analysis of biases impacting human-LLM collaboration.
Discussion of potential solutions and future research goals.

Main Results:

Current research inadequately addresses the potentials and risks of human-LLM collaboration.
Biases can impede effective collaboration between humans and LLMs.

Conclusions:

Future LLM research should prioritize enhancing human-LLM interaction.
Achieving mutual understanding and complementary team performance are key goals for effective human-LLM reasoning.