Hypotheses to Theories: Rule Learning for LLMs

Ref: arxiv.org/pdf/...
The core intuition behind Hypotheses-to-Theories (HtT) is that while LLMs can generate incorrect rules due to the implicit nature of their knowledge, they can also produce correct rules with a reasonable probability. This capability stems from their vast training data and ability to learn patterns. HtT leverages this potential by explicitly extracting, verifying, and organizing these correct rules into a readily accessible library for the LLM to use during reasoning.
HtT brings about a significant shift from the current paradigm of LLMs relying solely on implicit knowledge to a more structured and reliable approach that incorporates explicit rule-based reasoning. This transition offers several key benefits:
Reduced Hallucinations: HtT minimizes the risk of LLMs generating incorrect reasoning steps or "hallucinating" information by grounding their deductions in verified rules.
Improved Accuracy and Reliability: By utilizing a library of tested rules, HtT enhances the accuracy and consistency of LLM reasoning across various tasks, leading to more reliable outputs.
Enhanced Transparency and Explainability: The explicit nature of rule-based reasoning enables better understanding and interpretation of the LLM's thought process. This transparency is crucial for building trust and debugging potential issues.
Potential for Knowledge Transfer: The learned rule libraries can potentially be transferred and reused across different LLMs and tasks, facilitating more efficient knowledge acquisition and sharing.
HtT fundamentally changes the landscape of LLM reasoning by moving beyond the limitations of implicit knowledge and introducing a more structured and reliable approach based on explicit rule learning. This shift empowers LLMs to perform more accurate and transparent reasoning, paving the way for more sophisticated and trustworthy AI systems.
The Hypotheses-to-Theories (HtT) framework involves two distinct stages:
1. Induction Stage: This stage focuses on learning a library of rules.
The process begins by prompting an LLM to generate potential rules for answering questions based on provided training examples [1, 2]. These rules are extracted using regular expressions, assuming the LLM follows the provided example templates [3].
The generated rules are then verified by comparing the LLM's predictions against the ground truth answers [2, 3]. This process helps identify correct and incorrect rules without requiring explicit rule annotations.
Rules are filtered based on their coverage (number of occurrences in the training set) and confidence (average accuracy of examples where the rule is applied) [4].
Finally, a rule library is constructed using rules exceeding a minimal coverage and confidence threshold [4]. This ensures that only frequently used and reliably accurate rules are included.
2. Deduction Stage: This stage involves applying the learned rules to solve new problems.
The constructed rule library is prepended to the LLM's prompt [5]. This provides the LLM with a readily available set of rules to use during the reasoning process.
The examples provided to the LLM are modified to demonstrate retrieving rules from the provided library when necessary [5]. This encourages the LLM to utilize known and verified rules instead of generating potentially incorrect ones.
By combining these two stages, the HtT framework effectively leverages the LLM's ability to generate potential rules and enhances its reasoning capabilities by providing a verified and readily accessible knowledge base.
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