Pay-Per-Search Models are Abstention Models
Authors: name: Mustafa Omer Gul, name: Claire Cardie, name: Tanya Goyal
Organization: Cornell University

LLMs cannot reliably recognize their parametric knowledge boundaries and
often hallucinate answers to outside-of-boundary questions. In contrast, humans
recognize their limitations and can either seek external help for such
questions or abstain. In this paper, we introduce MASH (Modeling Abstention via
Selective Help-seeking), a training framework that readily extracts abstentions
from LLMs. Our key idea is that any external help-seeking by an LLM, i.e.
search tool use, can serve as a proxy for abstention if the external help
(search) is appropriately penalized while simultaneously rewarding answer
accuracy. MASH operationalizes this idea using reinforcement learning with a
pay-per-search reward.
  We run experiments on three knowledge-intensive QA datasets. Our results show
that MASH substantially improves upon the selective help-seeking performance of
prior efficient search approaches; on multi-hop datasets, MASH improves answer
accuracy by 7.6%. Furthermore, MASH demonstrates strong off-the-shelf
abstention -- it can distinguish between unanswerable/answerable questions and
selectively generate responses for answerable questions -- showcasing behavior
analogous to specialized abstention approaches. We emphasize that contrary to
prior abstention methods, MASH does not require pre-determining knowledge
boundaries to construct training data. Instead, MASH's abstentions are a
by-product of training for the auxiliary selective help-seeking task. Overall,
we show that MASH training effectively aligns search tool use with parametric
knowledge, which can be successfully leveraged for making abstention decisions.