Personalized Reasoning: Just-In-Time Personalization and Why LLMs Fail
  At It
Current large language model (LLM) development treats task-solving and
preference alignment as separate challenges, optimizing first for objective
correctness, then for alignment to aggregated human preferences. This paradigm
fails in human-facing applications where solving a problem correctly is
insufficient if the response mismatches the user's needs. This challenge
intensifies in just-in-time scenarios where no prior user interaction history
exists due to cold-start conditions or privacy constraints. LLMs need to
identify what they don't know about user preferences, strategically elicit
preference values through questioning, then adapt their reasoning processes and
responses accordingly -- a complicated chain of cognitive processes which we
term personalized reasoning. We introduce PREFDISCO, an evaluation methodology
that transforms static benchmarks into interactive personalization tasks using
psychologically-grounded personas with sparse preferences. Our framework
creates scenarios where identical questions require different reasoning chains
depending on user context, as optimal explanation approaches vary by individual
expertise and preferences while maintaining factual accuracy. Evaluation of 21
frontier models across 10 tasks reveals 29.0% of naive personalization attempts
produce worse preference alignment than generic responses, yet generic
responses also fail to serve individual user needs effectively. These findings
suggest personalized reasoning requires dedicated development rather than
emerging naturally. PREFDISCO establishes personalized reasoning as a
measurable research frontier and reveals fundamental limitations in current
LLMs' interactive capabilities, providing a foundation for developing systems
that can adapt to individual users in education, healthcare, and technical
domains where personalization is critical.
68e3a7d873e20ab57784219c
Shuyue Stella Li
claimed_verified
2025-10-06T12:48:08.711Z
655c3ece953cf1ef5e2b5abd
/avatars/406ee355a78171dba86d7dca3ee56699.svg
Stella Li
user
stellalisy
68e3a7d873e20ab57784219d
Avinandan Bose
68e3a7d873e20ab57784219e
Faeze Brahman
68e3a7d873e20ab57784219f
Simon Shaolei Du
68e3a7d873e20ab5778421a0
Pang Wei Koh
68e3a7d873e20ab5778421a1
Maryam Fazel
68e3a7d873e20ab5778421a2
Yulia Tsvetkov
Personalized Reasoning: Just-In-Time Personalization and Why LLMs Fail
  At It
Current large language model (LLM) development treats task-solving and
preference alignment as separate challenges, optimizing first for objective
correctness, then for alignment to aggregated human preferences. This paradigm
fails in human-facing applications where solving a problem correctly is
insufficient if the response mismatches the user's needs. This challenge
intensifies in just-in-time scenarios where no prior user interaction history
exists due to cold-start conditions or privacy constraints. LLMs need to
identify what they don't know about user preferences, strategically elicit
preference values through questioning, then adapt their reasoning processes and
responses accordingly -- a complicated chain of cognitive processes which we
term personalized reasoning. We introduce PREFDISCO, an evaluation methodology
that transforms static benchmarks into interactive personalization tasks using
psychologically-grounded personas with sparse preferences. Our framework
creates scenarios where identical questions require different reasoning chains
depending on user context, as optimal explanation approaches vary by individual
expertise and preferences while maintaining factual accuracy. Evaluation of 21
frontier models across 10 tasks reveals 29.0% of naive personalization attempts
produce worse preference alignment than generic responses, yet generic
responses also fail to serve individual user needs effectively. These findings
suggest personalized reasoning requires dedicated development rather than
emerging naturally. PREFDISCO establishes personalized reasoning as a
measurable research frontier and reveals fundamental limitations in current
LLMs' interactive capabilities, providing a foundation for developing systems
that can adapt to individual users in education, healthcare, and technical
domains where personalization is critical.
PREFDISCO evaluates large language models' personalized reasoning capabilities by transforming static benchmarks into interactive tasks with sparse user preferences, revealing significant limitations in current models' ability to adapt to individual needs.
large language model personalized reasoning PREFDISCO psychologically-grounded personas sparse preferences interactive personalization tasks reasoning chains user context preference alignment generic responses