Advancing Reasoning in Large Language Models: Promising Methods and Approaches
- Published
- Source
- arXiv
- Paper number
- 032
- Field
- Reasoning / Survey
- arXiv ID
- 2502.03671
Key points
- Large language models, or LLMs, often lack robust and systematic reasoning ability, which makes them struggle with complex tasks such as logical deduction, mathematical problem solving, and multi-step reasoning.
- These shortcomings lead to serious problems such as factual inaccuracy, inconsistency, and hallucination, which limit the reliability of LLMs in critical applications.
- Integrating the data-driven paradigm of modern LLMs with the structured reasoning of classical AI remains an ongoing and difficult challenge.
- This paper conducts a comprehensive literature review that systematically collects and analyzes prior work on improving LLM reasoning.
- It classifies the many reasoning improvement techniques into three major groups: prompting strategies, structural innovations, and learning-based approaches.
- The survey also reviews the benchmarks and metrics used to evaluate LLM reasoning and identifies open problems and future research directions.
Paper links
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