5 research outputs found
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly
how to use the system by constraining the user behaviors within the system's
capabilities via strict user goals, namely "user familiarity" bias. This data
bias deepens when it combines with data-driven TOD systems, as it is impossible
to fathom the effect of it with existing static evaluations. Hence, we conduct
an interactive user study to unveil how vulnerable TOD systems are against
realistic scenarios. In particular, we compare users with 1) detailed goal
instructions that conform to the system boundaries (closed-goal) and 2) vague
goal instructions that are often unsupported but realistic (open-goal). Our
study reveals that conversations in open-goal settings lead to catastrophic
failures of the system, in which 92% of the dialogues had significant issues.
Moreover, we conduct a thorough analysis to identify distinctive features
between the two settings through error annotation. From this, we discover a
novel "pretending" behavior, in which the system pretends to handle the user
requests even though they are beyond the system's capabilities. We discuss its
characteristics and toxicity while emphasizing transparency and a fallback
strategy for robust TOD systems
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Dialogue State Tracking (DST) is critical for comprehensively interpreting
user and system utterances, thereby forming the cornerstone of efficient
dialogue systems. Despite past research efforts focused on enhancing DST
performance through alterations to the model structure or integrating
additional features like graph relations, they often require additional
pre-training with external dialogue corpora. In this study, we propose DSTEA,
improving Dialogue State Tracking via Entity Adaptive pre-training, which can
enhance the encoder through by intensively training key entities in dialogue
utterances. DSTEA identifies these pivotal entities from input dialogues
utilizing four different methods: ontology information, named-entity
recognition, the spaCy, and the flair library. Subsequently, it employs
selective knowledge masking to train the model effectively. Remarkably, DSTEA
only requires pre-training without the direct infusion of extra knowledge into
the DST model. This approach resulted in substantial performance improvements
of four robust DST models on MultiWOZ 2.0, 2.1, and 2.2, with joint goal
accuracy witnessing an increase of up to 2.69% (from 52.41% to 55.10%). Further
validation of DSTEA's efficacy was provided through comparative experiments
considering various entity types and different entity adaptive pre-training
configurations such as masking strategy and masking rate
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Large language models (LLMs) learn not only natural text generation abilities
but also social biases against different demographic groups from real-world
data. This poses a critical risk when deploying LLM-based applications.
Existing research and resources are not readily applicable in South Korea due
to the differences in language and culture, both of which significantly affect
the biases and targeted demographic groups. This limitation requires localized
social bias datasets to ensure the safe and effective deployment of LLMs. To
this end, we present KO SB I, a new social bias dataset of 34k pairs of
contexts and sentences in Korean covering 72 demographic groups in 15
categories. We find that through filtering-based moderation, social biases in
generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and
82B), and GPT-3.Comment: 17 pages, 8 figures, 12 tables, ACL 202
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as
generating offensive content and reinforcing biases, are steeply rising.
Existing works focus on coping with this concern while interacting with
ill-intentioned users, such as those who explicitly make hate speech or elicit
harmful responses. However, discussions on sensitive issues can become toxic
even if the users are well-intentioned. For safer models in such scenarios, we
present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a
large-scale Korean dataset of 49k sensitive questions with 42k acceptable and
46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA
in a human-in-the-loop manner based on real news headlines. Experiments show
that acceptable response generation significantly improves for HyperCLOVA and
GPT-3, demonstrating the efficacy of this dataset.Comment: 19 pages, 10 figures, ACL 202