74 research outputs found
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Being able to predict people's opinions on issues and behaviors in realistic
scenarios can be helpful in various domains, such as politics and marketing.
However, conducting large-scale surveys like the European Social Survey to
solicit people's opinions on individual issues can incur prohibitive costs.
Leveraging prior research showing influence of core human values on individual
decisions and actions, we propose to use value-injected large language models
(LLM) to predict opinions and behaviors. To this end, we present Value
Injection Method (VIM), a collection of two methods -- argument generation and
question answering -- designed to inject targeted value distributions into LLMs
via fine-tuning. We then conduct a series of experiments on four tasks to test
the effectiveness of VIM and the possibility of using value-injected LLMs to
predict opinions and behaviors of people. We find that LLMs value-injected with
variations of VIM substantially outperform the baselines. Also, the results
suggest that opinions and behaviors can be better predicted using
value-injected LLMs than the baseline approaches.Comment: EMNLP 2023 main paper accepte
Extracting Implicitly Asserted Propositions in Argumentation
Argumentation accommodates various rhetorical devices, such as questions,
reported speech, and imperatives. These rhetorical tools usually assert
argumentatively relevant propositions rather implicitly, so understanding their
true meaning is key to understanding certain arguments properly. However, most
argument mining systems and computational linguistics research have paid little
attention to implicitly asserted propositions in argumentation. In this paper,
we examine a wide range of computational methods for extracting propositions
that are implicitly asserted in questions, reported speech, and imperatives in
argumentation. By evaluating the models on a corpus of 2016 U.S. presidential
debates and online commentary, we demonstrate the effectiveness and limitations
of the computational models. Our study may inform future research on argument
mining and the semantics of these rhetorical devices in argumentation.Comment: EMNLP 202
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and
one user at a time, dialogue systems are increasingly expected to communicate
with multiple users simultaneously who make decisions collaboratively. To
facilitate development of such systems, we release the Multi-User MultiWOZ
dataset: task-oriented dialogues among two users and one agent. To collect this
dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat
between two users that is semantically and pragmatically consistent with the
original user utterance, thus resulting in the same dialogue state and system
response. These dialogues reflect interesting dynamics of collaborative
decision-making in task-oriented scenarios, e.g., social chatter and
deliberation. Supported by this data, we propose the novel task of multi-user
contextual query rewriting: to rewrite a task-oriented chat between two users
as a concise task-oriented query that retains only task-relevant information
and that is directly consumable by the dialogue system. We demonstrate that in
multi-user dialogues, using predicted rewrites substantially improves dialogue
state tracking without modifying existing dialogue systems that are trained for
single-user dialogues. Further, this method surpasses training a medium-sized
model directly on multi-user dialogues and generalizes to unseen domains.Comment: To Appear in EMNLP-Findings 202
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