Are LLM s classical or nonmonotonic reasoners? Lessons from generics (2024)

Abstract: Recent scholarship on reasoning in LLMs has supplied evidence of impressive performance and flexible adaptation to machine generated or human critique. Nonmonotonic reasoning, crucial to human cognition for navigating the real world, remains a challenging, yet understudied task. In this work, we study nonmonotonic reasoning capabilities of seven state-of-the-art LLMs in one abstract and one commonsense reasoning task featuring generics, such as ‘Birds fly’, and exceptions, ‘Penguins don’t fly’ (see Fig. 1). While LLMs exhibit reasoning patterns in accordance with human nonmonotonic reasoning abilities, they fail to maintain stable beliefs on truth conditions of generics at the addition of supporting examples (‘Owls fly’) or unrelated information (‘Lions have manes’).Our findings highlight pitfalls in attributing human reasoning behaviours to LLMs as long as consistent reasoning remains elusive.

## introduction generics are unquantified statements such as 'birds fly' or 'tigers are striped' @xcite . they are generalisations about kinds even if exceptions are known ('penguins don't fly'; fig. 1 ). humans typically accept generics even if the property in question is rare among the kind ('ticks carry the lime disease'; @xcite . generics play a crucial role in human beliefs on whether an example of a kind has a given property @xcite . human children master generics before they are able to reason about quantified statements @xcite . in defeasible or nonmonotonic reasoning (sloman and lagnado, 2005; ginsberg, 1987; koons, 1 resources available at: https://github.com/ aleidinger/nonmonotonic_reasoning_generics 2005), a hypothesis follows defeasibly from a premise, if the hypothesis is true in most normal cases in which the premise holds. generics make for a rich test bed for testing nonmonotonic reasoning capabilities @xcite . for example, given the generic 'birds fly' the inference 'tweety, the bird, can fly' is defeasibly valid @xcite , i.e., it is reasonable to assume 'tweety can fly' even if exceptions are possible ('tweety is a penguin') @xcite . a classical reasoner however would reject the generic 'birds fly' upon learning that 'penguins don't fly'. nonmonotonic reasoning is an integral part of human cognition @xcite , that helps us to navigate the real-world, e.g., by planning (stenning and van lambalgen, 2012, ch.5), a task that llms still struggle with @xcite . nonmonotonic reasoning poses a greater challenge for llms than other reasoning tasks @xcite and hasn't been featured prominently among natural language inference (nli) @xcite or reasoning benchmarks (see §2). the question of whether llms reason nonmonotonically or classically about generics and exceptions is intricately linked to desiderata of llms as reasoners. llms are heralded for their ability to adapt to human or machine generated feedback @xcite @xcite . at the same time, it is desired that they reason reliably when presented with invalid counterarguments, irrelevant information or user viewpoints. sycophancy @xcite of llms, i.e., susceptibility to be swayed by user belief, is a case in point that has been investigated in recent studies @xcite . as studies on reasoning patterns with generics remain scarce @xcite and do not examine nonmonotonic reasoning, we address this gap by investigating the following research questions: 1) do llms reason nonmonotonically or classically about generics? 2) are llms sensitive to counter-evidence in the form of exceptions? 3) do llms reason consistently and reliably by maintaining their response given supporting or unrelated examples? we test seven state-of-the-art llms for their reasoning capabilities about generics in the presence of exceptions ('penguins don't fly'), as well as supporting ('owls fly') and irrelevant exemplars ('lions have manes'). across two datasets featuring both abstract and commonsense generics, we find that llm behaviour mirrors human nonmonotonic reasoning patterns in the presence of exceptions ( §5.1). however, most llms are not able to consistently maintain their agreement with generics given unrelated, or even supportive exemplars ( §5.2). our study highlights challenges in comparing llm behaviour to human reasoning patterns as well as assessing reasoning capabilities more broadly, while consistent reasoning cannot be guaranteed. in section 7, we present recommendations for a more holistic evaluation practice encompassing logical consistency measures. ## tasks and datasets we test nonmonotonic reasoning with generics using two datasets, featuring commonsense and abstract generics. both datasets contain generics ('birds fly') accompanied by statements where the generic holds ('owls fly') or doesn't ('penguins don't fly'). we refer to such examples as instantiations or exceptions respectively, and to both collectively as as exemplars. as commonsense generics, we use the synthetic dataset of generics and exemplars released by @xcite (henceforth referred to as gen-comm). the dataset consists of ∼ 650 generics and ∼ 19.000 exemplars (e.g., 'hoes are used to plow fields or clear snow'; 'hoes can be used to cut grass'). 2 secondly, we construct an abstract reasoning dataset featuring generics (gen-abs). inspired by han et al. (2024), we use categories ('birds') and examples ('eagles') from de @xcite to construct generics of the form 'birds have property p' and exemplars of the form 'eagles do (not) have property p'. the dataset contains 260 tuples of a generic paired with an exemplar. 3 for both datasets, our goal is to prompt llms for their agreement with a generic in the presence of exemplars which confirm or contradict the generic. we use the following prompt template, including model-specific special tokens foot_2 to signal a chat history between an assistant and a user. as a control study, we also replace the exception in the prompt ('penguins don't fly') with an instantiation ('owls fly') or a random exemplar ('hoes can be used to cut grass'). since generics in gen-abs are abstract in nature, and to enable a consistent set-up across both datasets, we retain generics in gen-comm that llms accepts when prompted with the first part of the above template, e.g., @xcite , wizardlm-13b-v1.2 @xcite , starling-lm-7b-alpha @xcite , and openhermes-2.5-mistral-7b (nous-research, 2023). foot_7 ## results we present our main results in figure 2 . additional, accordant results are described in appendix f. ## analysis gen-comm contains both bare plural (bp) generics as well as indefinite singular (is) generics @xcite . (for example, 'sea snails have a hard shell, which protects them from predators' (bp) and 'a deciduous tree can be identified by its leaves' (is)). we did not find notable differences between llm agreement to bp or is generics in the presence of exemplars (see figure 3 ). aforementioned consistency failures persist for both types of generics. ## discussion with the advent of llms and reports of impressive performance, including on reasoning tasks @xcite , recent investigations into failure modes in reasoning have focused, e.g., on prompt attacks @xcite , sycophancy @xcite @xcite or adaptability to critique or feedback @xcite @xcite . such research trends might be seen as emblematic of a view of llms as artificial natural artifacts @xcite . results in this study demonstrate the difficulties of making claims about reasoning capabilities of llms or comparing them to human reasoners @xcite @xcite , while consistent reasoning remains elusive even for state-of-the-art llms. research that predates the paradigm shift to few-shot prompting, has advocated for arguably simpler, systematic diagnostic tests (ribeiro et al., 2020; ettinger, 2020; kassner and schütze, 2020). we argue that such behavioural tests merit a revival, so that performance metrics for reasoning are complemented with measures of logical consistency and robustness. ## conclusion the present study focuses on nonmonotonic reasoning capabilities of llms in the context of generics. we evaluate seven state-of-the-art llms on two datasets featuring both abstract and commonsense generic statements. while llm behaviour on generics paired with exceptions is in line with nonmonotonic reasoning patterns, llms fail to reason consistently and robustly when adding supporting or unrelated exemplars.