Reasoning in large language models: a dive into NLP logic

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), demonstrating outstanding capabilities across various applications. Beyond text generation, LLMs are gaining increasing attention for their potential to perform reasoning tasks, a quality that could bring them closer to human intelligence.

The underlying logic for a simple question answering. Source: Can LLMs Reason with Rules?

Reasoning—the ability to draw informed conclusions and make decisions based on given information—is essential for understanding complex situations and, subsequently, effective problem-solving. As large language models evolve, researchers are exploring whether these models can emulate human reasoning meaningfully, enhancing their decision-making accuracy and reliability.

The rise of reasoning applications in NLP includes commonsense, mathematical reasoning, and even logical deduction. Models capable of these tasks are trained on vast datasets and fine-tuned to understand relationships and apply knowledge across different contexts to derive sound conclusions. However, moving beyond pattern recognition to achieve true logical reasoning remains a breakthrough few models have reached.

Two broad types of language reasoning with specific task examples. Source: A Survey of Reasoning with Foundation Models

New challenges include ensuring that models generate coherent text and work through problems in a reliable and interpretable way. Techniques like Сhain-of-Thought prompting, which encourages step-by-step reasoning, show promise but still leave room for growth in replicating the depth and flexibility of human logic.

The importance of helpful iterative prompting by the humans in the loop for the reasoning capabilities of LLMs. Graphic adapted from xkcd.com Source: Can Large Language Models Reason and Plan? 

As we dive deeper into large language models' reasoning abilities, it’s clear that this area of AI holds vast potential for transforming fields like autonomous systems, decision-making tools, and virtual assistants. This article explores the current state of reasoning abilities in LLMs, examining how they are designed to think more logically and outlining possibilities that lie ahead.

What is Reasoning in LLMs?

Reasoning in large language models (LLMs) represents a transformative step in artificial intelligence. It enables these models to process inputs and generate likely true answers, as well as to interpret and infer meaning from complex sets of information. Unlike traditional programming, where outcomes rely on precisely coded instructions, reasoning capabilities in LLMs support dynamic problem-solving and nuanced decision-making.

Moral reasoning in LLMs offers a solid alternative to the traditional bottom-up approach, where a model’s judgment may be vulnerable to bias. Source: Rethinking Machine Ethics

As reasoning becomes a core focus in advancing large language models, specialized models have emerged to tackle this challenge with remarkable success. One such example is EURUS, a suite of large language models fine-tuned for reasoning. Built from Mistral-7B and CodeLlama-70B LLMs, EURUS ranks among the best open-source models on benchmarks for mathematical reasoning, code generation, and logical reasoning.

EURUS-7B evaluation results on LeetCode and TheoremQA benchmarks prove it’s comparable with ten times larger baselines. Source: Advancing LLM Reasoning Generalists with Preference Trees

Key Aspects of LLM Reasoning

To enable reasoning in large language models (LLMs), researchers focus on foundational skills that allow these models to navigate complex information and respond in ways that resemble human cognitive processes. 

An example of an initial prompt template for an LLM to generate reasoning answers. Source: Do LLMs Exhibit Human-Like Reasoning? 

Reasoning in LLMs relies on more than just processing data—it involves synthesizing insights, applying learned knowledge to new contexts, and precisely handling multifaceted queries. Here are some key aspects that enhance an LLM's reasoning abilities, each serving a unique role in elevating the model’s capacity for true analytical depth.

Inference

Inference in LLMs involves deriving new insights or knowledge from the information at hand. Unlike basic retrieval or recall, inference allows the model to recognize relationships, make informed assumptions, and bridge information gaps to reach well-founded conclusions.

The example demonstrates how inference works by integrating multiple pieces of information to form new conclusions. Source: Natural Language Reasoning, A Survey

For example, given the statement 'All dogs can bark, and this animal is a dog,' an LLM with strong inferential reasoning skills would deduce that 'this animal can bark' and demonstrate confidence in this conclusion.

Problem-Solving

Problem-solving in LLMs involves a sophisticated process of analyzing and responding to challenges by identifying optimal solutions based on the input data.

For instance, when a well-trained LLM faces complex queries containing unclear or conflicting details, it adapts by considering alternative interpretations and adjusting the response. This adaptability is crucial in tasks that don’t have any straightforward answers, as it requires the model to weigh pros and cons or integrate broader context to get to a coherent solution.

Tree-of-Thought strategy to solve complex problems with LLMs. Source: Large Language Model Guided Tree-of-Thought

Effective problem-solving also requires the model to adapt based on outcomes from prior interactions, refining its approach iteratively. Analyzing and adjusting responses makes LLMs especially valuable for applications requiring logical analysis, managing uncertainty, or generating creative solutions.

A strong approach can enhance problem-solving even in small language models (SLMs), as rStar demonstrates, a self-play mutual reasoning method. It significantly improves reasoning capabilities without requiring fine-tuning or superior models by breaking down problem-solving into a self-play generation-discrimination process.

The rStar approach in action: the target SLM generates candidate reasoning trajectories, while a second SLM acts as a discriminator, providing unsupervised feedback on each trajectory based on partial hints. The target SLM selects a final reasoning trajectory as the solution using this feedback. Source: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers 

Understanding Context

Understanding context goes beyond grasping the immediate meaning of words as it involves capturing the relationships between ideas, the flow of discourse, and the subtleties that shape meaning over multiple interactions. This means that the model must recognize details from previous exchanges or documents to maintain a consistent narrative or accurate response.

An example of a ground-truth benchmark generation process for evaluating LLMs for context understanding for the specific context of Environmental Impact Statement documents. Source: Examining Long-Context LMMs for Environmental Review Document Comprehension

Complexity Handling

Complex reasoning requires engaging in multi-step deductions, managing intricate details, and systematically weighing conflicting pieces of information. Fields like legal reasoning, medical diagnostics, and scientific research rely on a multi-layered approach that integrates diverse facts, probabilities, and hypotheses sequentially.

For example, consider an LLM tasked with solving a multi-step puzzle based on clues scattered across various statements. To find the correct solution, the model must interpret each piece of information accurately and deduce relationships among them, ensuring coherence throughout its reasoning process. 

Logic scaffolding uncovering challenging reasoning space for LLMs, referring to the rule length. Source: Can LLMs Reason with Rules?

In such tasks, an effective LLM engages in layered thinking—a process of deducing, verifying, and integrating conclusions step-by-step to ensure that each component aligns with the overall answer. This approach is especially relevant when multiple plausible solutions exist, requiring the model to balance contextual cues and probabilities to derive the most likely answer.

Examples of indirect reasoning for complex problems regarding mathematical proof and factual reasoning. Source: Large Language Models as an Indirect Reasoner

Frameworks like hybrid thinking models have been developed to enhance LLM performance in such complex reasoning tasks. These models dynamically switch between fast (intuitive) and slow (deliberate) reasoning modes based on task difficulty, supposedly enabling the model to adjust its analytical depth according to complexity.

Overview of The Hybrid Thinking Approach. Source: HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows

However, hybrid thinking remains a debated approach. Critics argue it risks oversimplification, as models may not always accurately identify which problems demand deeper reasoning. Additionally, reliance on predefined modes could restrict the model's adaptability in contexts that blur the lines between simple and complex tasks.

Expanding the Potential of Reasoning in LLMs

Reasoning capabilities enable large language models (LLMs) to interact with information in ways beyond straightforward text generation. In complex domains like finance, engineering, and education, where precise reasoning is essential, these models’ ability to infer, adapt, and manage layered information may significantly enhance their value. 

Strategic reasoning with LLMs. Source: LLM as a Mastermind

By examining the steps leading to a conclusion, reasoning allows LLMs to check their answers' reliability and reduce the "black box" effect often associated with AI models. This approach offers the potential for a more transparent, traceable path to each answer, allowing users to follow how a model arrived at its conclusion. As reasoning in LLMs advances, these capabilities promise to redefine applications across high-stakes fields.

Types of Reasoning

LLM reasoning covers several distinct categories, each addressing unique tasks contributing to the model’s ability to interpret, deduce, and infer meaning effectively. Researchers and developers can strategically design models suited for specific applications and problem-solving needs by categorizing these reasoning types. 

The illustration showcases comparative experiments highlighting deductive versus inductive reasoning methods. Source: Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs

Each reasoning type contributes to the overall performance, accuracy, and interpretability of large language models’ outcomes, enabling the models to handle complex queries, process logic, and even tackle abstract thinking.

Deductive Reasoning

Deductive reasoning relies on applying known general principles to specific instances, making it especially valuable in contexts where conclusions need to follow logically from established rules or facts. 

Deductive rules formal definition for a proposed generative process for synthetic reasoning questions. Source: Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples

For example, in structured knowledge-based tasks, deductive reasoning allows the model to uphold logical consistency and draw factually grounded conclusions. This capability is fundamental for applications in legal reasoning, scientific analysis, and other domains where accurate, rule-based conclusions are essential.

Inductive Reasoning

Inductive reasoning enables LLMs to draw generalized conclusions based on given patterns, allowing them to form broader assumptions from specific examples. Unlike deductive reasoning, which strictly follows known rules, inductive reasoning involves making educated guesses about new data based on past observations. 

An overview of the SolverLearner framework, explicitly designed for inductive reasoning. It follows a two-step approach, separating the learning of input-output mapping functions from their application in inference. Source: Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs

This reasoning type is powerful in dynamic, data-rich environments where rules are not explicitly defined. However, the conclusions here may vary in reliability because the process is based on probability rather than certainty.

Abductive Reasoning

Abductive reasoning involves inferring the most likely explanation or hypothesis for a set of observations, making it a crucial tool for models operating in uncertain or incomplete information environments. This reasoning type focuses on generating plausible explanations that best fit the available evidence rather than deriving conclusions based on strict rules or annotated data.

For example, if an LLM encounters the observation of wet sidewalks, it may conclude that it has rained, even though there could be other explanations, such as a street cleaner passing by or a spilled drink.

The difference between deductive and abductive reasoning. Source: CauseJudger: Identifying the Cause with LLMs for Abductive Logical Reasoning

This reasoning approach is particularly valuable in real-world applications that require quick, contextually relevant conclusions. Examples include diagnostic systems in medicine, fault detection in engineering, and natural language understanding tasks. However, it is essential to approach each conclusion critically, as abductive reasoning does not guarantee accuracy and cannot be taken as absolute truth.

Analogical Reasoning

Analogical reasoning enables models to identify similarities between different concepts, contexts, or scenarios, helping them make inferences or generate insights based on analogous relationships. By drawing parallels, the model can use its understanding of one situation to shed light on another, even when surface details differ.

For example, if a model encounters a structure in an argument about climate policy, it may compare it to a similar argument structure in economics, applying what it has learned about relationships, cause-effect patterns, or logical flow.

The Thought Propagation method allows for more robust reasoning by leveraging similarities between different problem scenarios. Source: Thought Propagation: An Analogical Approach to Complex Reasoning with LLMs 

Practical Reasoning

Practical reasoning involves a model recommending actions based on understanding its user’s goals, constraints, and possible outcomes. This approach mimics human decision-making processes, where a person considers various options, weighs potential consequences, and selects an action aligned with their objectives.

For example, in a customer support application, an LLM equipped with practical reasoning could find a way to respond to a customer complaint by balancing the goal of resolving the issue with maintaining customer satisfaction while following company policies.

Approaches to LLM Reasoning

Various approaches have been developed to enhance LLM reasoning capabilities. Understanding these methodologies is key to leveraging their full potential, though it’s essential to recognize that no single approach fully encapsulates all aspects of reasoning.

Traditional Prompting Techniques

Traditionally, LLMs have relied on prompting to guide their reasoning processes. This involves crafting specific prompts instructing the model on the desired reasoning task. 

For example, asking the model a question or providing a scenario can trigger its reasoning abilities. While prompting can work well in more straightforward scenarios, it struggles with layered reasoning tasks, where prompts alone may not sufficiently stimulate the necessary step-by-step logical processes. 

Chain-of-Thought Prompting

Chain-of-thought prompting (CoT) encourages LLMs to break down reasoning tasks into smaller, manageable steps. CoT improves the outputs’ accuracy and interpretability by simulating a more human-like thought process. 

In the pipeline for Zero-shot-CoT, researchers use a prompt to extract a full reasoning path from an LLM and then another prompt to extract the answer in the correct format from the reasoning text. Source: Large Language Models are Zero-Shot Reasoners

However, while the chain-of-thought approach offers more depth than traditional prompting, it still faces limitations, especially when models encounter tasks outside their training scope.

Fine-Tuning and Training Techniques

Developers can instill specific logical patterns and relevant nuances by exposing models to various reasoning tasks during training. However, creating such tailored datasets can be resource-intensive, and overfitting to particular reasoning tasks may reduce the model’s generalization capacity. Fine-tuning also has the challenge of capturing the full range of reasoning skills needed for real-world applications.

Few-Shot and Zero-Shot Learning

Few-shot and zero-shot learning allow LLMs to perform reasoning tasks with minimal training examples, relying on prior knowledge and context to make informed guesses in unfamiliar situations. 

These techniques are especially valuable in scenarios where labeled data is scarce. Yet, the inherent limitations of these methods lie in the reliance on model pre-training. Although practical, these approaches are often constrained by the quality and diversity of pre-existing knowledge within the model.

Advanced Reasoning Techniques in LLMs

Various advanced techniques have been developed to push beyond traditional prompting to enhance nuanced reasoning capabilities. These methodologies help models approach complex problem-solving, adapt to feedback, and even interleave reasoning with action.

Tree-of-Thought (ToT) Framework serves as a prime example of trial-and-error problem-solving in LLMs. By exploring alternative reasoning paths—similar to branches on a tree—ToT allows models to evaluate different possibilities before selecting a solution. 

Experimental results comparing different LLM-based Sudoku puzzle solvers across three sets of benchmarks. The ToT significantly outperforms Zero-Shot, One-Shot, and Few-Shot models. Source: Large Language Model Guided Tree-of-Thought

This method is particularly effective in scenarios where mapping out potential solutions is feasible but requires additional computational resources, making it less suited for rapid-response applications.

Another innovative approach, Reflexion, leverages reinforcement through linguistic feedback, enabling models to learn iteratively from past errors. However, the approach depends heavily on feedback quality, as flawed feedback can lead to compounding errors rather than improvements.

The Reflexion framework works on decision-making, programming, and reasoning tasks. Source: Reflexion: Language Agents with Verbal Reinforcement Learning

ReAct interleaves reasoning with direct action steps, allowing models to make and test inferences in dynamic, interactive settings. This approach benefits tasks requiring active engagement and real-time adaptation, such as in interactive environments or decision-support applications. ReAct showcases how reasoning and action can operate in tandem, though it also risks complicating the reasoning process if actions misalign with the intended logic.

Comparison of four prompting methods: Standard, CoT—Reason Only), Act-only, and ReAct. Source: ReAct: Synerging Reasoning and Acting in Language Models

These frameworks exemplify how LLMs can approach reasoning versatilely, tailored to different contexts and requirements. 

OpenAI’s Reasoning Models

The OpenAI o1 series models are engineered explicitly for complex reasoning through reinforcement learning. These models are designed to think critically before responding, generating extensive internal thought processes. They excel in scientific reasoning, perform outstandingly in competitive programming, and achieve Ph.D.-level accuracy on physics, biology, and chemistry benchmarks.

The o1 models utilize reasoning tokens to process prompts internally, with a context window of 128,000 tokens. During their current beta phase of the two available models—o1-preview and o1-mini—users may encounter limitations, but OpenAI is actively expanding features and access.

An example of a multi-step conversation between a user and an o1-based assistant. Source: OpenAI

For optimal performance, prompts should be straightforward, avoiding unnecessary chain-of-thought requests. As these models continue to develop, they hold significant potential for enhancing applications requiring deep reasoning and problem-solving.

LLM Reasoning Challenges and Limitations

Despite their advancements, LLM reasoning capabilities face several limitations that can hinder their effectiveness in reasoning tasks.

Contextual Limitations

LLMs may struggle to understand complex contexts, leading to misinterpretations or oversimplified conclusions. Reasoning tasks often require a deep understanding of intricate relationships, which the model may not always capture.

Ambiguity in Language

Natural language is open to interpretations, and LLMs can find it challenging to navigate ambiguous queries. This complexity can result in varied and controversial responses.

Over-Reliance on Patterns

LLMs often rely heavily on patterns observed in training data, which can limit the model's adaptability to new information.

Resource-Intensive Training

Training advanced reasoning models requires substantial computational resources and time. This can limit accessibility for smaller organizations and researchers, potentially stifling innovation in the field.

Final Thoughts

Exploring reasoning capabilities in LLMs reveals their nuanced potential and limitations. From inductive reasoning's adaptability to the contextual understanding provided by abductive reasoning, models keep improving to navigate complex tasks, still leaving space for future research. 

Advancements like the SolverLearner framework and the o1 series models indicate a promising trajectory for enhancing LLM logical reasoning skills through innovative methodologies. Yet, as we harness these capabilities, it remains crucial to critically evaluate the conclusions drawn from LLM outputs, recognizing that they may not always guarantee accuracy.

Balancing the benefits of advanced reasoning techniques with an awareness of their limitations will be essential in leveraging LLMs as practical tools in the increasingly complex information landscape.