IC-240Introducing multiple preferences (including conflicting ones) in a conversation improves LLM adherence to the original preference

Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, Kaixiang Lin

SourceDo LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs

Counterintuitively, when additional non-conflicting or even conflicting preferences are inserted throughout a conversation, the model's accuracy in following the original (first) preference increases. For Claude 3 Sonnet with reminder prompting, accuracy rises from ~0.35 with 2 preferences to ~0.45 with 20 preferences. Conflicting preference pairs also outperform the single-preference baseline. The authors conjecture that multiple preferences reinforce the model's attention to the preference dimension. This effect was confirmed across Claude 3 Sonnet, Claude 3 Haiku, Mistral 7B, and Mistral 8x7B.

Evidence
correlational
Key metric
Claude 3 Sonnet with reminder: accuracy increases from ~0.35 (2 preferences) to ~0.45 (20 preferences); conflicting pairs outperform single-preference baseline across Claude 3 Sonnet, Claude 3 Haiku, and Mistral 7B
Caveat
Mistral 8x7B shows similar performance between conflicting and non-conflicting pairs with a slight advantage for non-conflicting, suggesting the effect is model-dependent.
Model
Claude 3 Sonnet, Haiku, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 Mistral-7B-Instruct, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b
Related findings
IC-238, IC-239, IC-241
Extraction
automatic-extraction