DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs
approach complex problems. Instead of directly producing an answer for a given
input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly
"thinking" about a problem before providing an answer. This reasoning process
is publicly available to the user, creating endless opportunities for studying
the reasoning behaviour of the model and opening up the field of Thoughtology.
Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning,
our analyses on DeepSeek-R1 investigate the impact and controllability of
thought length, management of long or confusing contexts, cultural and safety
concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such
as human-like language processing and world modelling. Our findings paint a
nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning,
where extra inference time can impair model performance. Furthermore, we find a
tendency for DeepSeek-R1 to persistently ruminate on previously explored
problem formulations, obstructing further exploration. We also note strong
safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning
counterpart, which can also compromise safety-aligned LLMs.
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Siva Reddy
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DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs
approach complex problems. Instead of directly producing an answer for a given
input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly
"thinking" about a problem before providing an answer. This reasoning process
is publicly available to the user, creating endless opportunities for studying
the reasoning behaviour of the model and opening up the field of Thoughtology.
Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning,
our analyses on DeepSeek-R1 investigate the impact and controllability of
thought length, management of long or confusing contexts, cultural and safety
concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such
as human-like language processing and world modelling. Our findings paint a
nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning,
where extra inference time can impair model performance. Furthermore, we find a
tendency for DeepSeek-R1 to persistently ruminate on previously explored
problem formulations, obstructing further exploration. We also note strong
safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning
counterpart, which can also compromise safety-aligned LLMs.
DeepSeek-R1 generates detailed reasoning chains, offering insights into model behavior and cognitive phenomena, with findings highlighting performance nuances and safety concerns.
DeepSeek-R1 reasoning chains Thoughtology cognitive phenomena language processing world modelling nature of thought sweet spot ruminative behavior safety vulnerabilities