Large language models are sophisticated text generators not conversational partners
Talking with Language Models
Computers and SocietyArtificial IntelligenceComputation and LanguageMachine Learning
Summary
People often think they are having real conversations when they interact with large language models, but the authors explain that these models don’t actually understand or remember like humans do. Instead, the models generate suggested texts without meaning or intention behind them. The real dialogue happens in the user's mind as they interpret and respond. This new way of looking at human-AI chats helps clear up confusing philosophical questions about AI identity or responsibility.
What this means in practice
- •For interface designers: Design chat interfaces that clarify AI as text generators rather than conversational agents to manage user expectations and improve interaction transparency.
- •For product managers: Develop policies and guidelines for ethical deployment of AI text tools based on recognizing them as artifacts without agency or responsibility.
A position paper. It proposes an approach and reports no results.
Authors
James Ravi Kirkpatrick, Alexandru Radulescu, Rachel Katharine Sterken
Abstract
When we interact with large language models (LLMs), are we having a conversation? They are designed to invite us to treat them as intelligent interlocutors who remember, act, and make commitments. But appearances deceive. We introduce the artifactual stance, a framework that reconceives human-AI interaction as artifact-mediated exchanges of candidate texts. LLM outputs are candidate texts optimized for utility, not utterances bearing meaning or force. LLMs are sophisticated text generators, not speakers. Between sessions, nothing runs; between turns, no one remembers. What persists is a configuration and a transcript. The "conversation" is a user's solo performance, interpretive labour disguised by interface and artifact design. This shift dissolves recent philosophical puzzles. Questions about what 'I' and 'you' refer to in AI exchanges, about whether systems can lie or be held to promises, about the identity of our supposed interlocutors all rest on a false presupposition. There is no speaker behind the screen, hence no one to refer to, no one to hold responsible. What feels like dialogue with someone is interaction with an artifact that generates text at unprecedented scale and fit. By abandoning the conversational framing, we see these systems for what they are: immensely sophisticated artifacts that afford varied uses. The philosophical questions that matter are about the normative underpinnings of design, adoption, authorization, and human practices of use.