Understanding Reasoning in Large Language Models

The ability to reason is a fundamental aspect of human intelligence, enabling logical and systematic thinking to reach conclusions or make decisions. This includes inference, argument evaluation, and drawing logical conclusions. Large Language Models (LLMs) have demonstrated impressive capabilities, but their capacity for true reasoning remains an active area of research. This post explores different types of reasoning, techniques to elicit reasoning in LLMs, how we measure it, and key findings from recent studies, based on a survey of the field, including insights from Towards Reasoning in Large Language Models: A Survey.

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Different Types of Reasoning

The reasoning is not a monolithic concept—it includes various approaches:

Deductive Reasoning

Deductive reasoning involves drawing specific conclusions based on general premises. If the premises are true, the conclusion must be true.

Example:

• Premise: All humans are mortal.

• Premise: Socrates is a human.

• Conclusion: Socrates is mortal.

LLMs, when tested with benchmarks like ProofWriter and PrOntoQA, are evaluated on their ability to perform such logical deductions.

Inductive Reasoning

Inductive reasoning generalizes from observed patterns. The conclusion is probable but not guaranteed.

Example:

• Observation: Every swan I have seen so far is white.

• Conclusion: All swans are white. (But this is not necessarily true.)

Benchmarks such as BigBench and CLUTTR assess LLMs on their ability to detect patterns and generalize observations.

Abductive Reasoning

Abductive reasoning involves inferring the most plausible explanation for an observation.

Example:

• Observation: The car won’t start, and there is a puddle under the engine.

• Conclusion: The car likely has a radiator leak.

Benchmarks like αNLI and AbductionRule measure an LLM’s capability to generate reasonable explanations with incomplete data.

Analogical, Causal, and Probabilistic Reasoning

• Analogical Reasoning: Drawing parallels between two scenarios, such as comparing an atom’s structure to a solar system.

• Causal Reasoning: Identifying cause-and-effect relationships (e.g., smoking causes lung cancer).

• Probabilistic Reasoning: Making decisions based on likelihoods (e.g., the probability of rain based on weather data).

These reasoning types are tested using CommonsenseQA, SocialIQA, and PIQA, among others.

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Prompting & In-Context Learning

LLMs can "learn" reasoning patterns from a few examples (few-shot learning) and apply them to new problems. However, they struggle with complex, multi-step reasoning tasks.

Chain of Thought and Its Variants

A major breakthrough in prompting techniques is Chain-of-Thought (CoT) prompting, which encourages LLMs to articulate intermediate reasoning steps.

Example:

Question: Roger has 5 tennis balls. He buys 2 cans of tennis balls. Each can have 3 balls. How many balls does he have now?
Standard Answer: 11
CoT Answer: Roger started with 5 balls. Each can contain 3 balls, so 2 cans contain 6 balls. 5 + 6 = 11.

This explicit reasoning process improves model accuracy.

Other CoT variations include:

• Zero-Shot CoT: Prompting with “Let’s think step by step” to elicit reasoning without examples.

• Scratchpads: Intermediate computations before generating an answer.

• Code Generation for Reasoning: Framing reasoning tasks as code execution improves model performance, as demonstrated by CodeActInstruct.

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Rationale Engineering

Rationale engineering refines LLM reasoning abilities:

• Rationale Refinement: Improves prompt complexity (e.g., complexity-based prompting).

• Rationale Exploration: Samples multiple reasoning paths and selects the most consistent (self-consistency).

• Rationale Verification: Uses trained verifiers to rank and refine reasoning outputs.

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Problem Decomposition

Complex problems can be broken down into smaller, manageable steps using:

1. Decomposed Prompting: Breaking a problem into subproblems (e.g., solving a math problem step by step).

2. Successive Prompting: Iteratively solving subproblems, with each step building on the previous one.

3. Least-to-Most Prompting: Tackling simpler subproblems before solving the main problem.

LLMs like ULTRAINTERACT use Python code execution for interactive problem-solving.

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Measuring Reasoning in Large Language Models

LLMs are benchmarked on various reasoning capabilities:

• Arithmetic Reasoning: GSM8K, MathQA, and SVAMP test numerical problem-solving.

• Symbolic Reasoning: Last Letter Concatenation, Coin Flip evaluates symbolic manipulation.

• Commonsense Reasoning: CommonsenseQA, SocialIQA, SWAG, and HellaSwag test everyday reasoning.

• Logical Reasoning: ProofWriter, FOLIO, PrOntoQA, and WaNLI assess logic-based inference.

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Findings and Implications

Research into LLM reasoning has led to key insights:

• Reasoning emerges at large scales: LLMs with over 100 billion parameters show improved reasoning abilities.

• Chain of Thought prompting enhances performance: CoT improves accuracy and out-of-distribution robustness.

• LLMs exhibit human-like reasoning biases: They mirror human tendencies in decision-making and logic errors.

• Complex reasoning remains a challenge: Models struggle with long-horizon reasoning tasks, implicature, and nuanced problem-solving.

• Improving LLM reasoning: Reinforcement Learning from Human Feedback (RLHF) and self-improving methods (e.g., STaR) enhance reasoning abilities.

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Conclusion

LLMs have made significant strides in reasoning, but whether they "truly" reason remains open to debate. Advances in Chain-of-Thought prompting, rationale engineering, and problem decomposition offer promising ways to enhance reasoning abilities. Future research will focus on dynamic reasoning architectures, hybrid models, and task-specific fine-tuning to further refine LLM reasoning capabilities.

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