From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models (2505.21935v1)

Abstract: Since the advent of LLMs, efforts have largely focused on improving their instruction-following and deductive reasoning abilities, leaving open the question of whether these models can truly discover new knowledge. In pursuit of artificial general intelligence (AGI), there is a growing need for models that not only execute commands or retrieve information but also learn, reason, and generate new knowledge by formulating novel hypotheses and theories that deepen our understanding of the world. Guided by Peirce's framework of abduction, deduction, and induction, this survey offers a structured lens to examine LLM-based hypothesis discovery. We synthesize existing work in hypothesis generation, application, and validation, identifying both key achievements and critical gaps. By unifying these threads, we illuminate how LLMs might evolve from mere ``information executors'' into engines of genuine innovation, potentially transforming research, science, and real-world problem solving.

Overview of "From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with LLMs"

The paper "From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with LLMs" by Kaiyu He and Zhiyu Chen provides a comprehensive examination of how LLMs are employed in hypothesis discovery and rule learning. This work pivots on the triadic framework of abduction, deduction, and induction as proposed by Charles Sanders Peirce, offering a structured approach to understanding the capabilities and limitations of LLMs in generating new knowledge. The survey aims to address the evolving question of whether LLMs can transcend their roles as mere information executors to become engines of hypothesis-driven innovation.

Key Contributions and Insights

1. Hypothesis Discovery with LLMs: The authors categorize hypothesis discovery into three pivotal stages: generation (abduction), application (deduction), and validation (induction). The survey extensively covers methods for each stage while highlighting significant achievements achieved through the use of LLMs. Notably, the paper emphasizes the ability of LLMs, trained on vast corpora, to leverage extensive commonsense knowledge for generating innovative hypotheses, thus overcoming earlier limitations faced by symbolic AI methods.
2. Abductive Reasoning: Abductive techniques are central to hypothesis generation, requiring the formulation of explanations that account for observed phenomena. The paper details how prompt-based, RAG-based, and human-in-the-loop methods leverage LLMs to propose hypotheses. The synthesis of retrieval-augmented generation (RAG), few-shot prompting, and interactive human collaboration reflects diverse approaches towards improving the novelty, creativity, and applicability of generated hypotheses.
3. Deductive Reasoning: LLMs are tested on their ability to apply hypotheses to novel contexts, demonstrating inferential rule-following. Various methods, including fine-tuning and LLM-based formal language parsing, highlight attempts to enhance LLM performance in hypothesis-driven deductive reasoning. Contrary to general inductive reasoning, which is well-supported by formal representations, deductive reasoning in natural language scenarios remains challenging due to reliance on nuanced language interpretation.
4. Inductive Reasoning: The validation of hypotheses through induction involves updating the hypothesis based on new evidence. The paper discusses both formal and natural language representation approaches, with the latter posing significant challenges due to implicit knowledge requirements. Evaluation methods focusing on defeasible inference, such as label-based and multiple-choice tests, underscore the ongoing efforts to establish rigorous validation protocols.

Numerical Results and Bold Claims

The survey reflects bold assertions regarding the capabilities of LLMs, proposing them as potential autonomous agents capable of hypothesis-driven scientific discovery. The text emphasizes the transformative potential of LLMs in research, science, and real-world problem-solving, albeit recognizing critical gaps that impede fully iterative and robust hypothesis discovery.

Implications and Speculations

The implications of this research lie in its potential to bridge fundamental gaps in AI-driven scientific inquiry, setting the stage for future experiments that could emulate real-world scientific discovery processes more closely. The paper speculates up on the trajectory of AI advancement, suggesting that overcoming current limitations in hypothesis generation, application, and validation would position LLMs closer to achieving artificial general intelligence (AGI).

Future Directions

The paper highlights several future research directions, including the development of realistic benchmarks combining formal rigor with natural language flexibility. Moreover, it advocates for enriched environments that simulate real-world complexity, enabling LLMs to engage in proactive hypothesis discovery across iterative reasoning cycles. This would involve designing benchmarks that actively challenge LLMs beyond pre-trained knowledge and catalyze genuine innovation.

In summary, Kaiyu He and Zhiyu Chen offer a nuanced exploration of hypothesis discovery with LLMs, providing foundational insights into how these models might evolve into creative engines driving scientific and practical advancements. Their examination underscores the importance of structured frameworks and realistic benchmarking in advancing the capabilities of AI in reasoning, learning, and knowledge generation.