I appreciate the pushback on these claims, but I want to flag that you seem to be reading too much into the post. The arguments that I provide aren’t intended to support the conclusion that we shouldn’t treat “I feel pain” as a genuine indicator or that there definitively aren’t coherent persons involved in chatbot text production. Rather, I think people tend to think of their interactions with chatbots in the way they interact with other people, and there are substantial differences that are worth pointing out. I point out four differences. These differences are relevant to assessing personhood, but I don’t claim any particular thing I say has any straightforward bearing on such assessments. Rather, I think it is important to be mindful of these differences when you evaluate LLMs for personhood and moral status. These considerations will affect how you should read different pieces of evidence. A good example of this is the discussion of the studies in the self-identification section. Should you take the trouble LLMs have with counting tokens as evidence that they can’t introspect? No, I don’t think it provides particularly good evidence, because it relies on the assumption that LLMs self-identify with the AI assistant in the dialogue and it is very hard to independently tell whether they do.
Firstly, this claim isn’t accurate. If you provide an LLM with the transcript of a conversation, it can often identify which parts are its responses and which parts are user inputs. This is an empirically testable claim. Moreover, statements about how LLMs process text don’t necessarily negate the possibility of them being coherent personas. For instance, it’s conceivable that an LLM could function exactly as described and still be a coherent persona.
I take it that you mean that LLMs can distinguish their text from others, presumably on the basis of statistical trends, so they can recognize text that reads like the text they would produce? This seems fully in line with what I say: what is important is that LLMs don’t make any internal computational distinction in processing text they are reading and text they are producing. The model functions as a mapping from inputs to outputs, and the mapping changes solely based on words and not their source. If you feed them text that is like the text they would produce, they can’t tell whether or not they produced it. This is very different from the experience of a human conversational partner, who can tell the difference between being spoken to and speaking and doesn’t need to rely on distinguishing whether words sound like something they might say. More importantly, they don’t know in the moment they are processing a given token whether they are in the middle of reading a block of user-supplied text or providing additional text through autoregressive text generation.
DALYs, unlike QALYs, are a negative measure. You don’t want to increase the number of DALYs.