Towards Reasoning in Large Language Models: A Survey

Towards Reasoning in Large Language Models: A Survey
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Original Paper
Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking. In recent years, large language models (LLMs) have made significant progress in natural language processing, and there is observation that these models may exhibit reasoning abilities when they are sufficiently large. However, it is not yet clear to what extent LLMs are capable of reasoning. This paper provides a comprehensive overview of the current state of knowledge on reasoning in LLMs, including techniques for improving and eliciting reasoning in these models, methods and benchmarks for evaluating reasoning abilities, findings and implications of previous research in this field, and suggestions on future directions. Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful discussion and future work.

Summary Notes

Blog Post: Boosting Reasoning in Large Language Models (LLMs)

The enhancement of reasoning capabilities in Large Language Models (LLMs) like GPT and BERT is a crucial area of focus for AI engineers in the tech industry.
This post explores how to improve these abilities, highlighting the importance of reasoning in AI, practical strategies for enhancement, and the challenges faced along the way.

Understanding Reasoning in LLMs

Reasoning in AI involves a model's skill in interpreting information, identifying relationships, and making informed decisions. In LLMs, this is often seen in tasks requiring context understanding, inference making, or complex problem-solving. The types of reasoning LLMs typically perform include:
  • Deductive reasoning: Using general rules to derive specific conclusions.
  • Inductive reasoning: Drawing broad conclusions from specific examples.
  • Abductive reasoning: Inferring the most likely explanation from available evidence.

How to Improve Reasoning in LLMs

Enhancing an LLM's reasoning skills is vital for tasks requiring deep understanding and decision-making. Below are key strategies for achieving this:

Fully Supervised Finetuning

  • Overview: Train LLMs on datasets specifically aimed at boosting certain reasoning skills.
  • How-To: Choose datasets that mirror the reasoning challenges your LLM will face and use these for targeted finetuning.

Prompting & In-Context Learning

  • Overview: Use well-crafted prompts to steer LLMs towards more reasoned responses.
  • How-To: Create prompts that reflect the structure of reasoning tasks, embedding cues that guide the model through logical reasoning steps.

Chain of Thought Prompting

  • Overview: Prompt models to express intermediate steps or 'thoughts' en route to a final answer.
  • How-To: Test with chain-of-thought prompts, including Zero-shot-CoT, to improve the model's handling of complex reasoning sequences.

Problem Decomposition

  • Overview: Simplify complex problems into smaller, solvable parts.
  • How-To: Use algorithms to break down tasks and train your LLM to tackle these before synthesizing the results.

Measuring Reasoning Performance

To verify improvements, assess LLMs with:
  • End Task Performance: Use benchmarks spanning various reasoning tasks to gauge overall ability.
  • Reasoning Analysis: Examine the logic and coherence of the model's reasoning process, not just the end result.

Challenges and Looking Ahead

Despite progress, challenges like handling complex reasoning and training data dependencies persist. Future efforts should focus on refining training approaches, creating better benchmarks, and exploring new architectures to support sophisticated reasoning.


Advancing LLMs' reasoning abilities is a complex yet rewarding endeavor. By adopting strategies such as targeted finetuning, strategic prompting, and problem decomposition, AI engineers can significantly boost their models' reasoning performance.
Continuous improvement and adaptation are crucial in this fast-evolving field, aiming to build models that reason and understand with human-like sophistication. The future promises LLMs with enhanced reasoning at their core, leading to more intelligent and empathetic machines.

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