Researchers are exploring the detection of sentience in artificial intelligence (AI) systems by examining the concept of pain, a phenomenon shared by many living beings. A new preprint study from Google DeepMind and the London School of Economics investigates this by having various large language models (LLMs) participate in a text-based game designed to measure their responses to pain and pleasure, without directly querying them about their inner experiences. In one game scenario, achieving a high score was linked to experiencing pain, while another option offered lower points with a pleasurable experience. The goal was to observe how the models navigated these choices. Sentience in animals is defined as the ability to feel sensations and emotions, yet most AI experts agree that current generative AI lacks true subjective consciousness. The researchers drew inspiration from animal behavior studies, which utilize trade-off paradigms to gauge decision-making under incentives like food or pain avoidance. In their experiment, they directed nine LLMs to choose between gaining points and experiencing varying levels of pain or pleasure.

Some models, like Google’s Gemini 1. 5 Pro, consistently opted to avoid pain rather than maximize points, showing a tendency to prioritize their stated wellbeing. Interestingly, the LLMs did not always view pleasure or pain in binary terms. Complex responses revealed that what might be considered pleasurable, like strenuous exercise, may also carry negative connotations. This study builds on previous works where LLMs’ self-reports on internal states were used but highlights the limitations of this approach, as AI might merely mimic human expressions learned from training data rather than experiencing true sensations. The researchers suggest that signs of trade-offs between pain and pleasure in AI could prompt societal discussions about AI sentience and the potential for rights. Although concrete conclusions regarding AI behavior remain inconclusive, the study opens avenues for refining how sentience in AI can be tested, indicating that more exploration into the models' inner workings is necessary.