# Text Production and Comprehension by Human and Artificial Intelligence: Interdisciplinary Workshop Report

arXiv: https://arxiv.org/abs/2506.22698 · HTML: https://mlsanity.com/paper/2506.22698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs](https://mlsanity.com/paper/2404.08555/) (2024)
- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations](https://mlsanity.com/paper/2305.14556/) (2023)
- [A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge](https://mlsanity.com/paper/2402.17081/) (2024)

## Abstract (arXiv)

This report synthesizes the outcomes of a recent interdisciplinary workshop that brought together leading experts in cognitive psychology, language learning, and artificial intelligence (AI)-based natural language processing (NLP). The workshop, funded by the National Science Foundation, aimed to address a critical knowledge gap in our understanding of the relationship between AI language models and human cognitive processes in text comprehension and composition. Through collaborative dialogue across cognitive, linguistic, and technological perspectives, workshop participants examined the underlying processes involved when humans produce and comprehend text, and how AI can both inform our understanding of these processes and augment human capabilities. The workshop revealed emerging patterns in the relationship between large language models (LLMs) and human cognition, with highlights on both the capabilities of LLMs and their limitations in fully replicating human-like language understanding and generation. Key findings include the potential of LLMs to offer insights into human language processing, the increasing alignment between LLM behavior and human language processing when models are fine-tuned with human feedback, and the opportunities and challenges presented by human-AI collaboration in language tasks. By synthesizing these findings, this report aims to guide future research, development, and implementation of LLMs in cognitive psychology, linguistics, and education. It emphasizes the importance of ethical considerations and responsible use of AI technologies while striving to enhance human capabilities in text comprehension and production through effective human-AI collaboration.