Natural Language Reasoning in Large Language Models: Analysis and Evaluation (2025)

Abstract: While Large Language Models (LLMs) have demonstrated promising results on a range of reasoning benchmarks—particularly in formal logic, mathematical tasks, and Chain-of-Thought prompting—less is known about their capabilities in unconstrained natural language reasoning. Argumentative reasoning, a form of reasoning naturally expressed in language and central to everyday discourse, presents unique challenges for LLMs due to its reliance on context, implicit assumptions, and value judgments. This paper addresses a gap in the study of reasoning in LLMs by presenting the first large-scale evaluation of their unconstrained natural language reasoning capabilities based on natural language argumentation. The paper offers three contributions: (i) the formalisation of a new strategy designed to evaluate argumentative reasoning in LLMs: argument-component selection; (ii) the creation of the Argument Reasoning Tasks (ART) dataset, a new benchmark for argument-component selection based on argument structures for natural language reasoning; and (iii) an extensive experimental analysis involving four different models, demonstrating the limitations of LLMs on natural language reasoning tasks.

## introduction the question of whether large language models (llms) can perform reasoning is a thorny one. not only have there been a wide range of studies exploring the issue (and coming to wildly different conclusions), but techniques such as chain of thought prompting (cot) @xcite and multi-hop question-answering @xcite , that purport to place reasoning at the forefront of llm interaction, have generated remarkable performance enhancements and demanding challenge tasks @xcite . coupled with high profile marketing touting llm rea-soning capabilities foot_0 and anecdotal evidence of both spectacular success and spectacular failure, it is no wonder there has been such an explosion of work in trying to fairly assess reasoning competence in llms @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . to date, however, all of this work has largely centred on structured problem-solving tasks such as arithmetic, logic puzzles, and theorem proving, or has focused on single-domain and basic inferential reasoning adapted into natural language. these approaches do not fully capture the complexities of human natural language reasoning as it naturally occurs in argumentation. though a focus on such problems offers an opportunity to carefully control variability under laboratory conditions, it also risks seriously misrepresenting llm performance with respect to realistic human reasoning. even @xcite , who demonstrate deep weaknesses in current llm capacity, rest their argument on classical planning, a very narrow and tightly constrained type of reasoning. what is required is a vocabulary, a model, a dataset and a set of tasks that also cover natural, in-situ human reasoning, as it is expressed in language. this is the domain of argumentation theory @xcite , and our goal is to leverage recent results in the area to equip us with the tools to assess llm performance in realistic settings. while argumentative reasoning does not serve as the sole or definitive test of reasoning ability, it provides a valuable lens for examining llms' capacity to handle complex, naturalistic reasoning as expressed in everyday language-complementing existing approaches by ad-dressing a dimension that remains comparatively underexplored. we address this challenge by presenting the first large-scale evaluation of the natural language reasoning capabilities of llms through argumentation, a setting where reasoning inherently unfolds in natural language, extending beyond simple inferential processes. this paper has, therefore, the three following main contributions: (i) we formalise a new task that we define as argumentcomponent selection which is designed to evaluate argumentative reasoning in llms; (ii) we create and release publicly the argument reasoning tasks (art) dataset, a new benchmark for argumentation reasoning consisting of 112,212 multiple-choice questions covering a total of sixteen different tasks addressing structural aspects of argumentation; and (iii) we present a complete set of experiments involving four open-and closed-weight models as well as a thorough analysis of the observed results. ## related work as pointed out in recent work, cot reasoning can also be achieved without any prompt engineering, by just modifying the greedy decoding strategy to explore alternative top-k decoding paths @xcite . this finding, though suggesting that the models reason intrinsically, can also be interpreted as the models not reasoning at all. instead, they select from alternative sequence paths learned during training, and tuning the input prompt or adjusting decoding allows for selecting a different path than the one leading to the direct answer. following this important finding, rather than focusing on how the output of the model is decoded, the focus should be on studying the model's ability to generalise and keep this behaviour labelled as "reasoning" when addressing problems of different nature and involving more complex and realistic reasoning than the ones that are commonly studied in the literature foot_1 . this aspect has been discussed in recent work @xcite , where the reported results show that with minimal variations of the standard versions of the tasks included in the most popular benchmarks used for reasoning, the performance of llms drops significantly. these findings challenge the claims that llms can do reasoning and that it allows them to improve their performance in a broad range of tasks. in addition to cot and (multi-hop) qa-related tasks, other datasets and tasks to evaluate reasoning in llms have been proposed. proofwriter @xcite presents a dataset to evaluate deductive logical reasoning through formal logic problems. copa @xcite and its multilingual version xcopa @xcite are two datasets created to evaluate causal reasoning by providing situations and asking to select the most likely outcome according to a cause-effect relationship. in this same direction, swag @xcite and hellaswag @xcite introduce two datasets to evaluate commonsense reasoning inference featuring adversarially generated scenarios in which models need to determine the most plausible option. the anli @xcite dataset is proposed to investigate abductive reasoning. again, the models are challenged to identify plausible outcomes for incomplete information scenarios. folio @xcite consists of a collection of first order logic statements to evaluate the reasoning capabilities of llms. the models are asked to determine the truth values of a set of conclusions given some premises which are presented in both, natural language and first-order logic statements. from the reported results, it is possible to observe how llms struggle to solve this task. finally, it is also worth mentioning other recent approaches, which have proposed the assessment of the reasoning capacities of llms based on games such as minesweeper, grid puzzles, sudoku or crosswords among others @xcite @xcite . we can observe, however, that despite being focused on reasoning-related tasks, none of them address the problem of non-constrained reasoning in natural language (e.g., in argumentation), which is a fundamental aspect for evaluating and understanding the actual natural language reasoning capabilities of llms. ## theoretical background arguments combine premises and conclusions to create complex reasoning structures. argument theory distinguishes different structural combinations of premises and conclusions: serial (premises and/or conclusions are supported by premises themselves) @xcite , linked (multiple premises support a conclusion together in a combined inferential step) @xcite , convergent (multiple premises independently support the conclusion), and divergent (same premise supports more than one conclusion). with these four types of argument structure, it is possible to analyse and understand argumentation in similar ways as multi-hop and cot reasoning are commonly studied. these structures are also well-documented in foundational texts on argumentation theory @xcite . an important challenge in the evaluation of llms on argumentation skills is to ensure that the reasoning capacities are assessed instead of the dialogue generation abilities. their training enables llms to create credible textual output based on probable token combinations. in an argument continuation task without sufficient limitations, the model will produce a probable continuation based on the input text. in this case, it is difficult to evaluate the appropriateness of the created continuation. our approach solves the evaluation challenge by introducing a multiple-choice task, a setup similar to the ones lsat tests already used to measure the reasoning abilities of llms in logic games @xcite . by asking for one or more elements from a complex argumentative graph structure, the model needs to identify the correct continuation among a choice of options from the same argumentative context. this requires the ability to follow and reconstruct an implicit reasoning path. tasks targeting larger chunks of argumentative elements require a model to choose an appropriate sub-structure as continuation, which demands deep understanding of necessary intermediate reasoning steps, similar to complex multi-hop q&a tasks. illustrative examples can be found in ## method aimed at providing, for the first time, a method to consistently evaluate the natural language reasoning capabilities of llms in argumentation, we formulate argument-component selection, consisting of a series of sixteen different argumentative reasoning tasks grouped into four different types of argumentative structures. this way, the proposed method allows us to evaluate the natural language reasoning capabilities of llms by asking them to build and reconstruct natural language arguments. ## conclusion this paper pushes forward the boundaries of knowledge on the reasoning capabilities of llms, a controversial and widely debated topic in the last years. we do so by asking a simple yet relevant question, can llms parse and understand argumentative reasoning structures? given that argumentation is the natural way of reasoning in natural language, if llms can reason, they should be able to parse, understand, and build natural language arguments. our results show that not only llms are not capable of understanding argumentative reasoning structures (let's not forget that this means reasoning in natural language), but also cases where a slightly more challenging argumentative structure is used, they perform worse than a random baseline. this highlights the need to develop challenging tasks to evaluate natural language reasoning, and also question the reasoning capabilities of llms, as it has been recently suggested in the literature. while we do not position argumentative reasoning as the sole measure of reasoning ability, it complements existing approaches by offering a valuable lens through which to assess llms in more realistic forms of natural language reasoning. 405b parameter version of llama and o1 only on a limited subset of art multiple-choice questions. nevertheless, the reported results indicate important trends, revealing that despite showing a slight increase in performance, they are still not capable of addressing tasks involving complex reasoning.