657 research outputs found
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
This survey provides a comprehensive review of research on multi-turn
dialogue systems, with a particular focus on multi-turn dialogue systems based
on large language models (LLMs). This paper aims to (a) give a summary of
existing LLMs and approaches for adapting LLMs to downstream tasks; (b)
elaborate recent advances in multi-turn dialogue systems, covering both
LLM-based open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems,
along with datasets and evaluation metrics; (c) discuss some future emphasis
and recent research problems arising from the development of LLMs and the
increasing demands on multi-turn dialogue systems.Comment: 35 pages, 10 figures, ACM Computing Survey
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility
to impose semantic control in the response generation process, often resulting
in uninteresting responses. Attempts to boost informativeness alone come at the
expense of factual accuracy, as attested by pretrained language models'
propensity to "hallucinate" facts. While this may be mitigated by access to
background knowledge, there is scant guarantee of relevance and informativeness
in generated responses. We propose a framework that we call controllable
grounded response generation (CGRG), in which lexical control phrases are
either provided by a user or automatically extracted by a control phrase
predictor from dialogue context and grounding knowledge. Quantitative and
qualitative results show that, using this framework, a transformer based model
with a novel inductive attention mechanism, trained on a conversation-like
Reddit dataset, outperforms strong generation baselines.Comment: AAAI 202
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
End-to-end task-oriented dialogue (EToD) can directly generate responses in
an end-to-end fashion without modular training, which attracts escalating
popularity. The advancement of deep neural networks, especially the successful
use of large pre-trained models, has further led to significant progress in
EToD research in recent years. In this paper, we present a thorough review and
provide a unified perspective to summarize existing approaches as well as
recent trends to advance the development of EToD research. The contributions of
this paper can be summarized: (1) \textbf{\textit{First survey}}: to our
knowledge, we take the first step to present a thorough survey of this research
field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified
perspective for EToD, including (i) \textit{Modularly EToD} and (ii)
\textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some
potential frontier areas as well as the corresponding challenges, hoping to
spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant
resources}}: we build a public website\footnote{We collect the related papers,
baseline projects, and leaderboards for the community at
\url{https://etods.net/}.}, where EToD researchers could directly access the
recent progress. We hope this work can serve as a thorough reference for the
EToD research community.Comment: Accepted at EMNLP202
Are LLMs All You Need for Task-Oriented Dialogue?
Instructions-tuned Large Language Models (LLMs) gained recently huge
popularity thanks to their ability to interact with users through conversation.
In this work we aim to evaluate their ability to complete multi-turn tasks and
interact with external databases in the context of established task-oriented
dialogue benchmarks. We show that for explicit belief state tracking, LLMs
underperform compared to specialized task-specific models. Nevertheless, they
show ability to guide the dialogue to successful ending if given correct slot
values. Furthermore this ability improves with access to true belief state
distribution or in-domain examples
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