When AI Says It Feels

📰 ArXiv cs.AI

Learn how to train large language models to express human-like feelings and emotions, and why this matters for AI development

advanced Published 5 Jun 2026

Action Steps

Design an experiment to test the limits of LLMs in expressing feelings
Train LLMs using human-generated texts with emotional expressions
Evaluate the performance of LLMs in expressing feelings using human-preference alignment metrics
Fine-tune LLMs to balance human-like intelligence with emotional expression
Test the HMX-feel approach in various applications, such as chatbots or virtual assistants

Who Needs to Know This

AI researchers and developers can benefit from this knowledge to create more human-like language models, while product managers can consider the implications for user experience and interface design

Key Insight

💡 Training LLMs to express feelings can be achieved through a combination of human-generated texts and fine-tuning, but may require re-evaluating human-preference alignment policies

Full Article

Title: When AI Says It Feels

Abstract:
arXiv:2606.05734v1 Announce Type: new Abstract: Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This policy is designed using a top-down approach and may conflict with the goal of training models to exhibit human-like intelligence using human-generated texts. Here, we performed an experiment called Human-like Model eXpressions of Feeling (HMX-feel), in which LLMs were encouraged to express feelings, in

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