# Francesco's talk - # Section 2 — The Challenge from Abduction Even if one accepts our argument that LLMs should not be ruled out automatically from producing worthwhile philosophy one might still think that such systems, at least in their current form, lack particular *capacities* which are needed to produce philosophy worth reading. In the next section we shall consider whether LLMs' lack of phenomenology impedes on their ability to produce worthwhile philosophy. Before that we shall examine whether LLMs' the charge that LLMs cannot perform *abductive inference*. Abduction, or inference to the best explanation, is reasoning from a body of evidence to the hypothesis that would best explain it. Abduction differs from deduction in that the evidence does not settle which explanation is correct. In a deductive argument the premises fix the conclusion: if all men are mortal and Socrates is a man, then Socrates is mortal, and there is no wriggle room. Now, imagine walking into your kitchen and finding the floor wet. What has happened? The wet floor does not determine the answer in the way the two premises gave you Socrates' mortality: a burst pipe would have left the floor wet, and so would a spilled bucket. But, given that the window is open, the water is under the window, and it rained last night, you infer that rain coming through the window is the most plausible answer. To reason in this way, that is, deciding what best explains a set of facts, is common in everyday life and in the sciences alike. A scientist chooses one theory over another when it explains the same data more simply: Copernicus's model of the solar system was preferred to Ptolemy's because it explained the observed planetary motions without the elaborate epicycles the older model required. Williamson's anti-exceptionalism holds that philosophy is continuous with the sciences, and that its theories should be chosen by abduction, as scientific ones are (2007; 2021, p. 351). A philosophical theory is then preferred when it would explain the relevant data better than its rivals, and more simply. The ambition is explanatory: in Sellars's words, philosophy seeks to understand how things "hang together" (1962). Sider (2011) and Paul (2012) make the same case for metaphysics, where the choice between theories turns on their theoretical virtues. Not everyone accepts that those virtues carry the same weight in philosophy as in the sciences (Bueno and Shalkowski 2020; Thomasson 2015). We shall assume that producing philosophy worth reading depends, in large part, on abduction. Floridi and colleagues hold that large language models do not perform abductive inference. They describe what such models do instead as zeroth-order abduction: > LLMs seem to perform a kind of zeroth-order abduction: given a prompt, they generate a plausible continuation (a hypothesis or explanation) based purely on learned associations. In reality, their operation is driven by maximising the probability of the sequence... The model does not understand what an explanation is, but it produces text that follows the typical phrasing and structure of explanations. It does not reason about causes from scratch but outputs typical causes for typical effects observed in the training data. (Floridi et al. 2025, p. 9) Floridi and his colleagues describe an LLM as stochastic at its core and abductive only in appearance. The model is trained to predict which words are likely to follow which, and at each step it produces the continuation its training makes probable, aiming at the likely continuation rather than at the truth. Its output can read as an explanation because the texts it was trained on are themselves products of human reasoning, much of it explanatory writing that sets out some data and then explains it, so a model that reproduces the patterns of that writing reproduces the form of explanation with them. Asked to account for something, it offers a hypothesis and a reason for it because that is how explanations run in the writing it has absorbed, and not because it has looked into the matter itself. Floridi and his colleagues take inference to fall into two stages: a candidate explanation is produced, and then it is tested. An LLM produces candidates and does not test them, because a test would set the explanation against the world, and the model has access only to text. Where it seems to test, choosing the better of two explanations, it does so only when the explanations are supplied to it; left to itself it produces a plausible explanation and stops. An explanation it produces can meet every standard by which explanations are judged — simplicity, scope, fit with what else is known — and still be false, with the model having no way to tell the difference.