IC-239Implicit preference forms (choice-based and persona-driven) are significantly harder for LLMs to follow than explicit preferences at the same context length
Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, Kaixiang Lin
When user preferences are revealed implicitly through a two-turn choice-based dialogue or a 4-8 turn persona-driven conversation rather than stated explicitly, all six open-source models show substantially lower preference-following accuracy even at short context lengths. For example, at ~0.2k tokens with zero-shot, Claude 3 Sonnet scores around 80% on explicit preferences but only ~41% on implicit choice-based and ~19% on implicit persona-driven. The gap persists across all models and context lengths, indicating that preference inference adds a distinct difficulty beyond long-context retrieval.
The implicit preference dialogues were constructed such that only one option adheres to the target preference, which the authors note does not guarantee 100% accurate inference from the multiple-choice selection.