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

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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