14 ms·

Inductive or deductive? Rethinking the fundamental reasoning abilities of LLMs

107 points by belter 2y ago

hydrox24 2y ago
Is there a good reason to exclude abductive reasoning from an analysis like this? It's even considered by at least one of the referenced papers (Fangzhi 2023a). Abductive reasoning is common in day-to-day life. It seeks the best explanation for some (often incomplete) observations, and reaches conclusions without certainty. I would have thought it would be important to assess for LLMs.
- soferio 2y ago
  https://en.wikipedia.org/wiki/Abductive_reasoning https://en.wikipedia.org/wiki/Abductive_reasoning Is abductive inference synonymous with bayesian inference?
  - keiferski 2y ago
    Here’s a good page to read on that: https://plato.stanford.edu/entries/abduction/#AbdVerBayConThe https://plato.stanford.edu/entries/abduction/#AbdVerBayConTh...
  - randcraw 2y ago
    I like to think of abductive reasoning as the basis for science that explains natural processes that happened in the past -- like astronomy and geology and evolution -- where experiments are too big to conduct or processes too slow to observe in real-time. So we propose mechanistic explanations for nonobvious outcomes like the formations of stars, or motion of large land mass via plate tectonics or glaciation, or long-range organism speciation over millennia. That's the role for abduction, to explain how all that happened.
  - User23 2y ago
    No, but agreement with priors is one way one might choose between possibilities. For example suppose you go outside and the streets are wet. Perhaps it rained, or perhaps someone drove a fire truck around spraying water all over the streets. You might select the former because of its higher prior probability.
- refulgentis 2y ago
  My instinct is it is a distinction without a difference in this context. i.e. if deductive is "I watched the cue ball hit the 8 ball, therefore, the 8 ball is moving" and abductive is "the 8 ball is moving towards me, therefore the cue ball must have hit it. I cannot claim to have deduced this because I did not observe it", LLMs cannot observe the situation, so any deduction (in the binary induction/deductive sense) must be done by abduction.
jcims 2y ago
LLMs don’t feel like a whole brain, they feel like the impulsive side that gets filtered by other processes.
- eightysixfour 2y ago
  While there is sometimes an exaggeration of the differences, I have always found LLMs to behave like (and have many of the weaknesses of) the left hemisphere of the brain as described in books like “The Master and His Emissary.” Considering the left is the language center, this shouldn’t be surprising.
- AdieuToLogic 2y ago
  > LLMs don’t feel like a whole brain ... Because they are not. LLM's are a better form of Bayesian inference[0] in that the generated tokens are statistically a better fit within a larger context. 0 - https://en.wikipedia.org/wiki/Bayesian_inference https://en.wikipedia.org/wiki/Bayesian_inference
  - stavros 2y ago
    This strikes me as saying "Brains aren't brains, they're merely a collection of neurons that output some voltage in response to their input voltage".
    - suprjami 2y ago
      You're comparing the physical item with the reasoning capabilities of that item. A popular LLM joke at the moment is "How many Rs in strawberry?" You, a human being, understand this question. The purpose of the activity is to count the letter R in that word and give the one correct answer. LLMs don't understand any of that. They don't know what words are, what letters are, what numbers are, what counting is, that a question can have one definite correct answer or many answers, what a question is, nor what an answer is. They break your input into tokens and then look at the most likely set of output tokens given your input. That's all. If you train a model on enough sensible input tokens and sensible responses then the output will mostly seem human and sensible. But it's never reasoning.
AdieuToLogic 2y ago
Large Language Model algorithms do not reason. They are statistical text generators, whose results are defined by their training data set. This is why the paper cited reads thusly: Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive and deductive reasoning ... There is no differentiation because what was sought is the existence of what does not. The authors then postulate: This raises an essential question: In LLM reasoning, which poses a greater challenge - deductive or inductive reasoning? There is no such thing as "LLM reasoning." Therefore, the greatest challenge is accepting this fact and/or that anthropomorphism is a real thing.
- astrange 2y ago
  They are not statistical text generators. They are black boxes wrapped in an interface part of which is a statistical text generator. You can write programs into transformer models and run them deterministically; they aren't "statistical".
  - godelski 2y ago
    Can you cite the deterministic part? And I don't think fixing your seeds count as making a model not statistical, though yes deterministic. It's also still statistical if it gives you the same answer 99999/100000 times. You can definitely tune these things to be much more certain about certain problems but it tends to decrease capabilities in others. I also don't like the term "black box." While they aren't transparent they aren't completely opaque either. A big reason I didn't like the term is that I feel it also encourages people to not research this more. While we don't completely know what's going on I see no reason we can't. Is there some proof they their calculations are an unbreakable encryption or is it just a hard problem. I'm pretty sure it's the latter, and I think it's good to differentiate "we're dumb" from "indeterminate"
    - taylorius 2y ago
      The issue with comprehending LLM's internal workings is just that they're so big. The operation of the network itself is deterministic - but it's output is a set of probabilities over the value of the next token in the stream. At that point you have to choose one, based on that probability distribution, and then start the process again. That choice is where the randomness comes in.
sfink 2y ago
Confession: I haven't read the paper. But any mention of LLM reasoning ability ought to address the obvious confound: the LLM is trained on examples of deductive reasoning, inductive reasoning, abductive reasoning, SAT-solver reasoning, geniuses' musings, etc. If they replicate one of those examples, then should that be called "reasoning" of any sort or not? Regurgitating those examples may even involve some generalization, if the original topics of an example are swapped out (perhaps by a nearby topic in latent space). Given that it appears they're training and testing on synthetic problems, this objection probably does not apply to their actual results. But given the fuzziness it creates for the definition of "reasoning" of any sort, I would have expected some working definition of reasoning in the paper's abstract. Training on Moby Dick and thus being able to regurgitate text from Moby Dick does not mean the LLM is capable of writing a new Moby Dick-like book. (Thankfully; one is more than enough!)
- viraptor 2y ago
  The tasks used are artificially created and don't exist in the training sets. For example there's very little practical math in base 11 on the internet, or English with explicitly mixed up but rule based grammar.
WaitWaitWha 2y ago
Neither. LLMs are just really, really good pattern matchers with enormous set of patterns.
randcraw 2y ago
The conclusions of the authors that LLMs can reason inductively very well runs counter to what I've read elsewhere. A big part of doing induction is the ability to generalize a shared pattern from multiple disparate examples, recognizing the essential elements that are necessary and sufficient to satisfy that pattern's operators' constraints. To date, I've seen consensus that LLMs can match verbs or basic relational operators across examples, thereby associating the mechanisms in similar events that lead to similar outcomes. But extending that facility further, to employing predicate logic operators, or even the simpler propositional ones appears to fall largely outside LLM capabilities. To suggest then that LLMs can then perform higher-order reasoning skills yet, like the modeling of contrapositives, this seems quite a stretch.
- golergka 2y ago
  I've just successfully chatted to ChatGPT about equivalence or at least high similarities between QFT, neural networks and cellular automata (referencing Wofram's work). Does that pattern matching count?
  - Vampiero 2y ago
    No but GPT is really good at fooling laymen who are not experts of a field, and it stands to reason that it just fed you a bunch of bs
  - xwolfi 2y ago
    And you were able to verify, of course, that anything new or surprising to you (as in, not a simple derivation of your own prompts), was true ? I noticed that if I ask it to tell me how good cryptocurrencies are, it'll do it, and then if I say I disagree and they're wrong, it'll simply switch and agree with me as well. The thing has no care for truth, no opinion of its own, no ability to insist, and just feeds you whatever is statistically close to your own questions.
- 082349872349872 2y ago
  Can LLM-based tools even handle the donkey's bridge of syntactic consequence ("A->B & A; B?") yet? Lagniappe: https://www.youtube.com/watch?v=9B2ww3fiX30 https://www.youtube.com/watch?v=9B2ww3fiX30
godelski 2y ago
I'm really tired of these papers and experiments. You cannot test reasoning when you don't know what's in the training set. You have to be able to differentiate reasoning from memorization, and that's not trivial. Moreso, the results look to confirm that at least some memorization is going on. Do we really not think GPT has extensively been trained on arithmetic in base 10, 8, and 16? This seems like a terrible prior. Even if not explicitly, how much code has it read that performs these tasks. How many web pages, tutorials, Reddit posts cover oct and hex? They also haven't defined zero shot correctly. Arithmetic in these bases aren't 0-shot. They're explicitly in distribution... I'm unsure about base 9 and 11. It's pretty interesting to see that GPT 4 is much better at these. Anyone know why? Did they train on these? More bases? Doesn't seem unreasonable but I don't know. The experimentation is also extremely lacking. The arithmetic questions only have 1000 tests where they add two digits. This is certainly in the training data. I'm also unconvinced by the syntax reasoning tasks since the transformer (attention) architecture seems to be designed for this. I'm also unconvinced these tasks aren't in training. Caesar ciphers are also certainly in the training data. The prompts are also odd and I guess that's why they're in the appendix. For example, getting GPT to be better at math or many tasks by having it write python code is not novel. There's some stuff here but this really doesn't seem like a lot of work for 12 people from a top university and a trillion dollar company. It's odd to see that many people when the experiments can be run in a fairly short time.
- Infinity315 2y ago
  I think the results still tell us something. Discrepancies in mathematical ability between the various bases would seem to suggest memorization as opposed to generalization.
- om8 2y ago
  How to you define memorization and reasoning? There is a large grey area in between them. Some say that if you can memorize facts and algorithms and apply them to new data, it is a memorization. Some say that it is reasoning. More than that -- It's not clear that what humans do is not "just" a memorization. We can always look at human experience mechanisticly and say that we don't think -- we just memorized thinking patterns and apply them when speaking and "thinking"
xiphias2 2y ago
Transformers are amazing pattern matchers and terrible use of GPUs for reasoning, which is mostly search + execution of highly non-linear programs (lambda calculus). I love seeing Victor Taelin experimenting with parallizing these programs (with HVM and other experiments with proof languages), but it's sometimes a bit sad how much time researchers take in making papers about existing things instead of trying to improve the state-of-the art in something that's most probably missing from the current models.
Datagenerator 2y ago
The human mind wonders and takes time to dream autonomously. Perhaps the llm.c we need for the next breakthrough addresses rounds of meditation in it's training in order to provoke more reason alike features to the NextGen LLM.
lngnmn2 2y ago
[dead]
calf 2y ago
Why does it have to be an either-or? Maybe LLMs are doing a bit of both, in a weird hybrid way; it is both doing a statistical calculation and yet the network is parameterized to do some very rudimentary (and nonhuman) reasoning computations. That's plausible to me, and explains why the reasoning is so hard to isolate... Just like how looking at a human brain it is hard to isolate the reasoning capacities.
bob1029 2y ago
LLMs are incredible about mapping to a space of already seen things. When this space is unimaginably large, you can be fooled for a long time. But, they clearly struggle with generalization and rule following. This failure to generalize (extrapolate, deduce, compute) is why we still can't fire all of our DBAs. Has anyone encountered an LLM-based text-to-SQL engine that actually gets the job done? I think that's your best canary. I stopped caring somewhere around "transpose these 2 letters of the alphabet" not working consistently.
moktonar 2y ago
Asking them to make ASCII art is the final test, to me.
YeGoblynQueenne 2y ago
>> Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. I don't know about "typical" but every source that classifies reasoning (or, more appropriately, logical inference) as deductive and inductive, also includes the abductive category. This categorisation scheme goes all the way back to Charles Sanders Peirce: '[Abduction] is logical inference( ... ) having a perfectly definite logical form. ( ... ) Namely, the hypothesis cannot be admitted, even as a hypothesis, unless it be supposed that it would account for the facts or some of them. The form of inference, therefore, is this: The surprising fact, C, is observed; But if A were true, C would be a matter of course, Hence, there is reason to suspect that A is true.' (Collected Papers of Charles Sanders Peirce. Peirce, 1958) (Quote copied from Abduction and Induction, Essays in their Relation and Integration, Peter Flach and Antonis Kakas eds. 200) Consider a logical theory, formed of rules in the form of implications like A -> B (premise A implies conclusion B). Abduction is the inference of the premises after observation of the conclusions, i.e. if A -> B AND B is observed, then A may be inferred. That's a different inference mode than both deduction: inferring a conclusion from a premise, e.g. if A -> B AND A, then B may be inferred; and induction: inferring a rule from an observation, e.g. inferring A -> B after observing A and B. Note that this is a simplification: induction assumes a background theory of more rules A1 -> A2, .... An -> A that can be applied to the observation A and B to infer A -> B. Anyway, abduction is generally associated with probabilistic reasoning, albeit informally so. That probably means that we should categorise LLM inference as abductive, since it guesses the next token according to a model of probabilities of token sequences. But that's just a, er, guess.

107 points by belter 2y ago

Is there a good reason to exclude abductive reasoning from an analysis like this? It's even considered by at least one of the referenced papers (Fangzhi 2023a). Abductive reasoning is common in day-to-day life. It seeks the best explanation for some (often incomplete) observations, and reaches conclusions without certainty. I would have thought it would be important to assess for LLMs.

soferio 2y ago
https://en.wikipedia.org/wiki/Abductive_reasoning https://en.wikipedia.org/wiki/Abductive_reasoning Is abductive inference synonymous with bayesian inference?
- keiferski 2y ago
  Here’s a good page to read on that: https://plato.stanford.edu/entries/abduction/#AbdVerBayConThe https://plato.stanford.edu/entries/abduction/#AbdVerBayConTh...
- randcraw 2y ago
  I like to think of abductive reasoning as the basis for science that explains natural processes that happened in the past -- like astronomy and geology and evolution -- where experiments are too big to conduct or processes too slow to observe in real-time. So we propose mechanistic explanations for nonobvious outcomes like the formations of stars, or motion of large land mass via plate tectonics or glaciation, or long-range organism speciation over millennia. That's the role for abduction, to explain how all that happened.
- User23 2y ago
  No, but agreement with priors is one way one might choose between possibilities. For example suppose you go outside and the streets are wet. Perhaps it rained, or perhaps someone drove a fire truck around spraying water all over the streets. You might select the former because of its higher prior probability.
refulgentis 2y ago
My instinct is it is a distinction without a difference in this context. i.e. if deductive is "I watched the cue ball hit the 8 ball, therefore, the 8 ball is moving" and abductive is "the 8 ball is moving towards me, therefore the cue ball must have hit it. I cannot claim to have deduced this because I did not observe it", LLMs cannot observe the situation, so any deduction (in the binary induction/deductive sense) must be done by abduction.

jcims 2y ago

LLMs don’t feel like a whole brain, they feel like the impulsive side that gets filtered by other processes.

eightysixfour 2y ago
While there is sometimes an exaggeration of the differences, I have always found LLMs to behave like (and have many of the weaknesses of) the left hemisphere of the brain as described in books like “The Master and His Emissary.” Considering the left is the language center, this shouldn’t be surprising.
AdieuToLogic 2y ago
> LLMs don’t feel like a whole brain ... Because they are not. LLM's are a better form of Bayesian inference[0] in that the generated tokens are statistically a better fit within a larger context. 0 - https://en.wikipedia.org/wiki/Bayesian_inference https://en.wikipedia.org/wiki/Bayesian_inference
- stavros 2y ago
  This strikes me as saying "Brains aren't brains, they're merely a collection of neurons that output some voltage in response to their input voltage".
  - suprjami 2y ago
    You're comparing the physical item with the reasoning capabilities of that item. A popular LLM joke at the moment is "How many Rs in strawberry?" You, a human being, understand this question. The purpose of the activity is to count the letter R in that word and give the one correct answer. LLMs don't understand any of that. They don't know what words are, what letters are, what numbers are, what counting is, that a question can have one definite correct answer or many answers, what a question is, nor what an answer is. They break your input into tokens and then look at the most likely set of output tokens given your input. That's all. If you train a model on enough sensible input tokens and sensible responses then the output will mostly seem human and sensible. But it's never reasoning.

AdieuToLogic 2y ago

Large Language Model algorithms do not reason. They are statistical text generators, whose results are defined by their training data set. This is why the paper cited reads thusly: Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive and deductive reasoning ... There is no differentiation because what was sought is the existence of what does not. The authors then postulate: This raises an essential question: In LLM reasoning, which poses a greater challenge - deductive or inductive reasoning? There is no such thing as "LLM reasoning." Therefore, the greatest challenge is accepting this fact and/or that anthropomorphism is a real thing.

astrange 2y ago
They are not statistical text generators. They are black boxes wrapped in an interface part of which is a statistical text generator. You can write programs into transformer models and run them deterministically; they aren't "statistical".
- godelski 2y ago
  Can you cite the deterministic part? And I don't think fixing your seeds count as making a model not statistical, though yes deterministic. It's also still statistical if it gives you the same answer 99999/100000 times. You can definitely tune these things to be much more certain about certain problems but it tends to decrease capabilities in others. I also don't like the term "black box." While they aren't transparent they aren't completely opaque either. A big reason I didn't like the term is that I feel it also encourages people to not research this more. While we don't completely know what's going on I see no reason we can't. Is there some proof they their calculations are an unbreakable encryption or is it just a hard problem. I'm pretty sure it's the latter, and I think it's good to differentiate "we're dumb" from "indeterminate"
  - taylorius 2y ago
    The issue with comprehending LLM's internal workings is just that they're so big. The operation of the network itself is deterministic - but it's output is a set of probabilities over the value of the next token in the stream. At that point you have to choose one, based on that probability distribution, and then start the process again. That choice is where the randomness comes in.

sfink 2y ago

Confession: I haven't read the paper. But any mention of LLM reasoning ability ought to address the obvious confound: the LLM is trained on examples of deductive reasoning, inductive reasoning, abductive reasoning, SAT-solver reasoning, geniuses' musings, etc. If they replicate one of those examples, then should that be called "reasoning" of any sort or not? Regurgitating those examples may even involve some generalization, if the original topics of an example are swapped out (perhaps by a nearby topic in latent space). Given that it appears they're training and testing on synthetic problems, this objection probably does not apply to their actual results. But given the fuzziness it creates for the definition of "reasoning" of any sort, I would have expected some working definition of reasoning in the paper's abstract. Training on Moby Dick and thus being able to regurgitate text from Moby Dick does not mean the LLM is capable of writing a new Moby Dick-like book. (Thankfully; one is more than enough!)

viraptor 2y ago
The tasks used are artificially created and don't exist in the training sets. For example there's very little practical math in base 11 on the internet, or English with explicitly mixed up but rule based grammar.

WaitWaitWha 2y ago

Neither. LLMs are just really, really good pattern matchers with enormous set of patterns.

randcraw 2y ago

The conclusions of the authors that LLMs can reason inductively very well runs counter to what I've read elsewhere. A big part of doing induction is the ability to generalize a shared pattern from multiple disparate examples, recognizing the essential elements that are necessary and sufficient to satisfy that pattern's operators' constraints. To date, I've seen consensus that LLMs can match verbs or basic relational operators across examples, thereby associating the mechanisms in similar events that lead to similar outcomes. But extending that facility further, to employing predicate logic operators, or even the simpler propositional ones appears to fall largely outside LLM capabilities. To suggest then that LLMs can then perform higher-order reasoning skills yet, like the modeling of contrapositives, this seems quite a stretch.

golergka 2y ago
I've just successfully chatted to ChatGPT about equivalence or at least high similarities between QFT, neural networks and cellular automata (referencing Wofram's work). Does that pattern matching count?
- Vampiero 2y ago
  No but GPT is really good at fooling laymen who are not experts of a field, and it stands to reason that it just fed you a bunch of bs
- xwolfi 2y ago
  And you were able to verify, of course, that anything new or surprising to you (as in, not a simple derivation of your own prompts), was true ? I noticed that if I ask it to tell me how good cryptocurrencies are, it'll do it, and then if I say I disagree and they're wrong, it'll simply switch and agree with me as well. The thing has no care for truth, no opinion of its own, no ability to insist, and just feeds you whatever is statistically close to your own questions.
082349872349872 2y ago
Can LLM-based tools even handle the donkey's bridge of syntactic consequence ("A->B & A; B?") yet? Lagniappe: https://www.youtube.com/watch?v=9B2ww3fiX30 https://www.youtube.com/watch?v=9B2ww3fiX30

godelski 2y ago

I'm really tired of these papers and experiments. You cannot test reasoning when you don't know what's in the training set. You have to be able to differentiate reasoning from memorization, and that's not trivial. Moreso, the results look to confirm that at least some memorization is going on. Do we really not think GPT has extensively been trained on arithmetic in base 10, 8, and 16? This seems like a terrible prior. Even if not explicitly, how much code has it read that performs these tasks. How many web pages, tutorials, Reddit posts cover oct and hex? They also haven't defined zero shot correctly. Arithmetic in these bases aren't 0-shot. They're explicitly in distribution... I'm unsure about base 9 and 11. It's pretty interesting to see that GPT 4 is much better at these. Anyone know why? Did they train on these? More bases? Doesn't seem unreasonable but I don't know. The experimentation is also extremely lacking. The arithmetic questions only have 1000 tests where they add two digits. This is certainly in the training data. I'm also unconvinced by the syntax reasoning tasks since the transformer (attention) architecture seems to be designed for this. I'm also unconvinced these tasks aren't in training. Caesar ciphers are also certainly in the training data. The prompts are also odd and I guess that's why they're in the appendix. For example, getting GPT to be better at math or many tasks by having it write python code is not novel. There's some stuff here but this really doesn't seem like a lot of work for 12 people from a top university and a trillion dollar company. It's odd to see that many people when the experiments can be run in a fairly short time.

Infinity315 2y ago
I think the results still tell us something. Discrepancies in mathematical ability between the various bases would seem to suggest memorization as opposed to generalization.
om8 2y ago
How to you define memorization and reasoning? There is a large grey area in between them. Some say that if you can memorize facts and algorithms and apply them to new data, it is a memorization. Some say that it is reasoning. More than that -- It's not clear that what humans do is not "just" a memorization. We can always look at human experience mechanisticly and say that we don't think -- we just memorized thinking patterns and apply them when speaking and "thinking"

xiphias2 2y ago

Transformers are amazing pattern matchers and terrible use of GPUs for reasoning, which is mostly search + execution of highly non-linear programs (lambda calculus). I love seeing Victor Taelin experimenting with parallizing these programs (with HVM and other experiments with proof languages), but it's sometimes a bit sad how much time researchers take in making papers about existing things instead of trying to improve the state-of-the art in something that's most probably missing from the current models.

Datagenerator 2y ago

The human mind wonders and takes time to dream autonomously. Perhaps the llm.c we need for the next breakthrough addresses rounds of meditation in it's training in order to provoke more reason alike features to the NextGen LLM.

lngnmn2 2y ago

[dead]

calf 2y ago

Why does it have to be an either-or? Maybe LLMs are doing a bit of both, in a weird hybrid way; it is both doing a statistical calculation and yet the network is parameterized to do some very rudimentary (and nonhuman) reasoning computations. That's plausible to me, and explains why the reasoning is so hard to isolate... Just like how looking at a human brain it is hard to isolate the reasoning capacities.

bob1029 2y ago

LLMs are incredible about mapping to a space of already seen things. When this space is unimaginably large, you can be fooled for a long time. But, they clearly struggle with generalization and rule following. This failure to generalize (extrapolate, deduce, compute) is why we still can't fire all of our DBAs. Has anyone encountered an LLM-based text-to-SQL engine that actually gets the job done? I think that's your best canary. I stopped caring somewhere around "transpose these 2 letters of the alphabet" not working consistently.

moktonar 2y ago

Asking them to make ASCII art is the final test, to me.

YeGoblynQueenne 2y ago

>> Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. I don't know about "typical" but every source that classifies reasoning (or, more appropriately, logical inference) as deductive and inductive, also includes the abductive category. This categorisation scheme goes all the way back to Charles Sanders Peirce: '[Abduction] is logical inference( ... ) having a perfectly definite logical form. ( ... ) Namely, the hypothesis cannot be admitted, even as a hypothesis, unless it be supposed that it would account for the facts or some of them. The form of inference, therefore, is this: The surprising fact, C, is observed; But if A were true, C would be a matter of course, Hence, there is reason to suspect that A is true.' (Collected Papers of Charles Sanders Peirce. Peirce, 1958) (Quote copied from Abduction and Induction, Essays in their Relation and Integration, Peter Flach and Antonis Kakas eds. 200) Consider a logical theory, formed of rules in the form of implications like A -> B (premise A implies conclusion B). Abduction is the inference of the premises after observation of the conclusions, i.e. if A -> B AND B is observed, then A may be inferred. That's a different inference mode than both deduction: inferring a conclusion from a premise, e.g. if A -> B AND A, then B may be inferred; and induction: inferring a rule from an observation, e.g. inferring A -> B after observing A and B. Note that this is a simplification: induction assumes a background theory of more rules A1 -> A2, .... An -> A that can be applied to the observation A and B to infer A -> B. Anyway, abduction is generally associated with probabilistic reasoning, albeit informally so. That probably means that we should categorise LLM inference as abductive, since it guesses the next token according to a model of probabilities of token sequences. But that's just a, er, guess.