Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning (2025)

Abstract: Deductive and inductive reasoning are fundamental components of human cognition, and in daily life, people often apply these types of reasoning unconsciously. While previous studies have extensively examined the deductive and inductive reasoning abilities of Large Language Models (LLMs) in rule-based and math-related tasks, little attention has been given to their role in procedural planning——an area that holds considerable relevance for real-world applications. To fill this gap, we present DIRPP (Deductive and Inductive Reasoning in Procedural Planning) in this paper, a benchmark designed to assess the deductive and inductive reasoning abilities of various LLMs within the context of procedural planning. Based on the benchmark, we initially observe that LLMs demonstrate excellent deductive reasoning capabilities in procedural planning but show suboptimal performance in inductive reasoning. To enhance their inductive reasoning abilities, we further propose a novel and effective method called IMSE (Induction through Multiple Similar Examples), which enables LLMs to generate multiple similar procedural plans and then perform inductive reasoning based on these examples. Through various experiments, we find that the proposed method can significantly improve the inductive reasoning capabilities of LLMs.

## introduction in recent years, advances in large language models (llms), such as gpt-4 (openai, 2024) and deepseek @xcite , have completely revolutionized the field of natural language processing. llms perform well on a wide variety of reasoning tasks @xcite , including logical reasoning tasks @xcite . * dianbo sui and dianhui chu are the corresponding authors. 1 resources of this paper can be found at https://github. com/kangjiabaohit/dirpp . deductive reasoning and inductive reasoning are the basic components of logical reasoning. people in daily life always use these two types of reasoning unconsciously. deductive reasoning involves drawing specific conclusions from general principles under certain conditions. in contrast, inductive reasoning moves in the opposite direction. inferences from the observed to the unobserved, or to general laws, are known as inductive inferences (henderson, 2024). deductive reasoning and inductive reasoning are considered crucial for achieving artificial intelligence @xcite . some research @xcite @xcite ) has suggested that mixing deductive and inductive reasoning is not conducive to effective analysis. as a result, they have studied these two types of reasoning separately. for example, @xcite synthesizes 15 typical reasoning datasets and evaluates a wide variety of llms across inductive, deductive, abductive, and mixed-form reasoning settings. @xcite examines the inductive and deductive capabilities of llms in the context of programming. @xcite separates inductive and deductive reasoning to investigate which one is more important for the reasoning ability of llms. it is worth noting that much of the recent work (seals and shalin, 2024; @xcite @xcite on inductive and deductive reasoning abilities of llms is confined to rule-based or mathematically oriented tasks, as these tasks facilitate the separation of inductive and deductive reasoning, enabling more focused studies. however, exploring and probing the inductive and deductive reasoning abilities of llms in procedural planning-a field closely tied to real-life applications @xcite procedural planning @xcite entails breaking down a high-level goal into a series of coherent, logical, and goal-directed steps (e.g., "taking a shower" → "1. prepare the bathroom; 2. set the water temperature; 3. undress; ..."). it represents a form of structured general knowledge commonly used in daily life, with significant implications for both smarter ai systems and executable robotic systems @xcite . it is important to note that both inductive and deductive reasoning play a crucial role in enhancing the effectiveness of procedural planning. specifically, inductive reasoning enables the system to generalize from observed patterns and past experiences @xcite , allowing it to predict the most likely sequence of actions for new, unseen goals. this capability is vital for adapting to diverse tasks and improving planning efficiency. in contrast, deductive reasoning ensures the logical consistency and correctness of the planning process by enabling the system to deduce necessary steps based on predefined rules or knowledge @xcite . this guarantees that the generated plans will achieve the specific goals without unnecessary steps or contradictions. figure 1 illustrates an example that demonstrates both deductive and inductive reasoning in procedural planning. in this paper, we explore the deductive and inductive capabilities of llms in procedural planning. to achieve this, we firstly propose a benchmark called dirpp. specifically, each example in dirpp includes an abstract goal and an abstract procedural plan to achieve it, along with a specific goal and its corresponding specific procedural plan. based on goals from coscript @xcite , we leverage gpt-4o-mini to complete the construction of our dataset. next, we further introduce two metrics (the achievement rate and preference index) for dirpp to quantitatively assess the performance of llms. through pilot experimental results, we find that all llms demonstrate strong deductive abilities, while their inductive capabilities are comparatively weaker. to address this, we then propose a novel approach aimed at enhancing the inductive abilities of llms. specifically, we first ask gpt-4o-mini to generate several related goals similar to the specific goal. then, we instruct the evaluation model to generate procedural plans for these related goals. finally, we enable the model to generalize from these multiple similar procedural plans, rather than relying on a single plan. via various experiment, we find that our proposed method is effective. to sum up, our contributions are as follows: ## related work deductive and inductive reasoning. cognitive science holds that deductive and inductive reasoning are fundamental concepts for understanding human thought processes @xcite . in common cognitive models, these two types of reasoning are considered complementary: inductive reasoning generates hypotheses from observations, while deductive reasoning tests them @xcite . with llms making significant progress in a wide range of reasoning tasks @xcite al., 2023; bian et al., 2024; imani et al., 2023), there has been growing interest in their underlying reasoning capabilities. extensive research has focused on the logical reasoning abilities of llms. for example, cai et al. (2024) simulate human thought processes by enabling llms to first summarize and then deduce, enhancing their reasoning abilities. gendron et al. (2024) highlight that guiding models to follow causal reasoning paths improves their inductive reasoning capabilities. yang et al. ## task definitions in this section, we formalize the tasks of deductive and inductive reasoning in procedural planning to help clarify the subsequent content. procedural planning. a procedural plan is a sequence of steps (@xmath0, s 2 , . . . , s |s| }, e.g., "{ gather ingredients, preheat oven, ...}") designed to achieve a goal (g) @xcite , e.g.,"make a cake". the procedural planning generation task is defined as m : @xmath1, where m represents a language model. ## deductive and inductive reasoning in procedural planning in this section, we present our complete benchmark. we begin by outlining the construction process of our dataset, followed by a detailed explanation of the metrics used for evaluating deductive and inductive reasoning tasks. finally, we assess a range of llms, leveraging their few-shot in-context learning ability. ## induction through multiple similar examples results in the pilot experiment show that llms' deductive reasoning abilities in procedural planning have reached an excellent level, while their make a sundea gpt-4 make a chocolate sundea make a strawberry sundea models generated example 1 generated example 2 example improved procedural planning models output: an improved procedural plan make a banana sundea step 2 the model generates corresponding procedural plans for the specific goals, creating new examples. step 3 the model observes multiple examples and generates an improved procedural plan. input: an abstract goal and an example step 1 gpt-4 generates specific goals based on an abstract goal. inductive reasoning abilities remain sub-optimal. in this section, we introduce a novel and effective approach to enhance the inductive reasoning capabilities of llms. ## conclusion in this work, we introduce a benchmark, dirpp, designed to explore deductive and inductive reasoning in procedural planning for llms. our findings indicate that while llms demonstrate strong deductive reasoning capabilities, their inductive reasoning abilities requires improvement. to address this, we propose a novel and effective method, imse, which enables the model to generate multiple similar examples and generalize based on these examples, thereby enhancing its inductive reasoning capability. we hope that our work will inspire future research into reasoning within the context of procedural planning.