A new approach to AI reasoning optimization
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Introduction
Multiple facts are needed to answer a multi-hop question-answering (QA), which is essential for complex reasoning and explanations in Large Language Models (LLMs). QA quantifies and objectively tests intelligent system reasoning. Due to their unambiguous correct solutions, QA tasks reduce subjectivity and human bias in evaluation. QA functions can evaluate deductive reasoning, inductive reasoning, and abductive reasoning, which involves formulating the most plausible answer from partial knowledge.
We face several challenges in improving the model’s reasoning processes. One of the most important demands is model interpretability and explainability. Large AI models, especially deep neural networks, are hard to understand, which makes it hard to evaluate them accurately and come up with human-friendly explanations for their decisions and conclusions. Another important goal for improving the reasoning process is to ensure that reasoning processes are robust to minor variations in input or context, as well as to develop models that can generalize reasoning skills across different domains and types of questions.
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