The latest and most incredible advancement in the domain of artificial intelligence is the development of Large Language Models (LLMs). The very famous ChatGPT developed by OpenAI, which is based on the GPT 3.5 and GPT 4 architecture, is very useful and mainly makes headlines to generate content and answer questions like a human would. His ability to mimic humans in generating creative and accurate content allows him to immerse himself in problem solving in almost every industry. With the addition of Chain-of-Thought (CoT) prompts, the impact of LLMs like GPT 3.5 has enhanced, leading to significant changes in the information processing industry. CoT enhances LLMs and helps them generate more complete and elaborate thought processes in a series of intermediate steps.
Although CoT offers many advantages, its emphasis on intermediate stages of reasoning occasionally causes hallucinations and compound errors, making it difficult for models to generate consistent and accurate reasoning processes. Many efforts have been made to enable LLMs to perform explicit and rigorous deductive reasoning by drawing inspiration from how humans engage in deliberate deductive logical reasoning procedures to solve problems. To address these challenges, a team of researchers has introduced the Natural Program, a natural language-based deductive reasoning format that uses the inherent power of natural language to achieve deductive reasoning.
The team has mentioned that this approach splits the reasoning verification process into several sequential sub-processes. Each thread is only provided with the context and premises required for the particular step, and decomposition makes the verification process more accessible. The authors have used publicly available models such as OpenAI’s GPT-3.5-turbo (175B) to perform tests on common sense arithmetic datasets to show the effectiveness of their program-based natural verification technique. The results demonstrated how well their strategy worked in increasing the reliability of reasoning processes produced by large language models.
The natural program format allows language models to generate precise reasoning steps, ensuring that later steps build more rigorously on earlier steps. Language models perform self-verification of reasoning step by step using this structure, and the resulting reasoning stages are more rigorous and reliable as a verification procedure is built into each level of deductive reasoning.
Some of the key contributions mentioned by the team are:
- With the introduction of the Natural Program format, the team has proposed a framework for rigorous deductive reasoning, which is suitable for verification and can be produced simply by learning in context.
- It has been shown that extensive deductive reasoning processes written in the proposed Natural Program format can be reliably self-verified by using step-by-step sub-processes that only cover the context and prerequisite premises.
- Through experiments, the team has demonstrated how effectively the framework improves the accuracy, reliability, and interpretability of LLM-generated reasoning stages and solutions.
In conclusion, this framework seems promising for improving the deductive reasoning capabilities of language models.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
