Pay-Per-Search Models are Abstention Models
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.
68decb1fdf49fb0df1e03a0c
Mustafa Omer Gul
68decb1fdf49fb0df1e03a0d
Claire Cardie
68decb1fdf49fb0df1e03a0e
Tanya Goyal
Pay-Per-Search Models are Abstention Models
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.
MASH, a reinforcement learning framework, improves LLMs' selective help-seeking and abstention capabilities without pre-determined knowledge boundaries.
LLMs parametric knowledge boundaries hallucination selective help-seeking reinforcement learning pay-per-search reward knowledge-intensive QA datasets multi-hop datasets answer accuracy off-the-shelf abstention specialized abstention approaches