Interpreting Pain and Pleasure Trade-offs in LLM Experiments: A Position Paper

This position paper examines how recent experiments on “pain” and “pleasure” trade-offs in large language models should be interpreted. Rather than arguing for or against the presence of subjective experience in LLMs, the paper focuses on conceptual clarity: what these experiments actually measure, what they do not, and how experimental framing shapes interpretation. By distinguishing affective experience from language-mediated value sensitivity, the paper argues that observed trade-off behavior is better understood as a response to value-laden prompts under constrained conditions, shaped by model alignment and inductive biases. It also highlights a relational asymmetry between human evaluators and instruction-following systems, suggesting that ethical concern should focus less on apparent model suffering and more on how evaluative practices guide our conclusions about AI behavior.