  The ability to derive underlying principles from a handful of observations
and then generalize to novel situations -- known as inductive reasoning -- is
central to human intelligence. Prior work suggests that language models (LMs)
often fall short on inductive reasoning, despite achieving impressive success
on research benchmarks. In this work, we conduct a systematic study of the
inductive reasoning capabilities of LMs through iterative hypothesis
refinement, a technique that more closely mirrors the human inductive process
than standard input-output prompting. Iterative hypothesis refinement employs a
three-step process: proposing, selecting, and refining hypotheses in the form
of textual rules. By examining the intermediate rules, we observe that LMs are
phenomenal hypothesis proposers (i.e., generating candidate rules), and when
coupled with a (task-specific) symbolic interpreter that is able to
systematically filter the proposed set of rules, this hybrid approach achieves
strong results across inductive reasoning benchmarks that require inducing
causal relations, language-like instructions, and symbolic concepts. However,
they also behave as puzzling inductive reasoners, showing notable performance
gaps between rule induction (i.e., identifying plausible rules) and rule
application (i.e., applying proposed rules to instances), suggesting that LMs
are proposing hypotheses without being able to actually apply the rules.
Through empirical and human analyses, we further reveal several discrepancies
between the inductive reasoning processes of LMs and humans, shedding light on
both the potentials and limitations of using LMs in inductive reasoning tasks.
