The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models
Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can represent task patterns and surface-level concepts, it remains unclear whether these models can encode high-level relational concepts and apply them to novel situations through structured comparisons. In this work, we explore this fundamental aspect using proportional and story analogies, and identify three key findings. First, LLMs effectively encode the underlying relationships between analogous entities; both attributive and relational information propagate through mid-upper layers in correct cases, whereas reasoning failures reflect missing relational information within these layers. Second, unlike humans, LLMs often struggle not only when relational information is missing, but also when attempting to apply it to new entities. In such cases, strategically patching hidden representations at critical token positions can facilitate information transfer to a certain extent. Lastly, successful analogical reasoning in LLMs is marked by strong structural alignment between analogous situations, whereas failures often reflect degraded or misplaced alignment. Overall, our findings reveal that LLMs exhibit emerging but limited capabilities in encoding and applying high-level relational concepts, highlighting both parallels and gaps with human cognition.
692eaf5537312eaa83fd899b
Taewhoo Lee
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2025-12-02T16:50:15.761Z
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Taewhoo Lee
Taewhoo
user
Taewhoo
692eaf5537312eaa83fd899c
Minju Song
692eaf5537312eaa83fd899d
Chanwoong Yoon
692eaf5537312eaa83fd899e
Jungwoo Park
692eaf5537312eaa83fd899f
Jaewoo Kang
The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models
Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can represent task patterns and surface-level concepts, it remains unclear whether these models can encode high-level relational concepts and apply them to novel situations through structured comparisons. In this work, we explore this fundamental aspect using proportional and story analogies, and identify three key findings. First, LLMs effectively encode the underlying relationships between analogous entities; both attributive and relational information propagate through mid-upper layers in correct cases, whereas reasoning failures reflect missing relational information within these layers. Second, unlike humans, LLMs often struggle not only when relational information is missing, but also when attempting to apply it to new entities. In such cases, strategically patching hidden representations at critical token positions can facilitate information transfer to a certain extent. Lastly, successful analogical reasoning in LLMs is marked by strong structural alignment between analogous situations, whereas failures often reflect degraded or misplaced alignment. Overall, our findings reveal that LLMs exhibit emerging but limited capabilities in encoding and applying high-level relational concepts, highlighting both parallels and gaps with human cognition.
LLMs can encode and apply high-level relational concepts in analogical reasoning but face limitations, particularly when relational information is missing or when transferring to new entities.
analogical reasoning LLMs task patterns surface-level concepts high-level relational concepts proportional and story analogies attributive information relational information mid-upper layers hidden representations token positions structural alignment