Preference-Paired Fine-Tuning (PFT) lets LLMs handle conflicting and dynamic individual preferences better than single-preference methods, reaching 96.6% accuracy on the new VCD dataset and 44.76% gains in user alignment with limited history.
The prediction is evaluated against the gold set Ss,p of all choices annotated with preference p, while the wrong set can be defined as Ss,p′
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning
Preference-Paired Fine-Tuning (PFT) lets LLMs handle conflicting and dynamic individual preferences better than single-preference methods, reaching 96.6% accuracy on the new VCD dataset and 44.76% gains in user alignment with limited history.