2,124 research outputs found
Is ChatGPT a Good Multi-Party Conversation Solver?
Large Language Models (LLMs) have emerged as influential instruments within
the realm of natural language processing; nevertheless, their capacity to
handle multi-party conversations (MPCs) -- a scenario marked by the presence of
multiple interlocutors involved in intricate information exchanges -- remains
uncharted. In this paper, we delve into the potential of generative LLMs such
as ChatGPT and GPT-4 within the context of MPCs. An empirical analysis is
conducted to assess the zero-shot learning capabilities of ChatGPT and GPT-4 by
subjecting them to evaluation across three MPC datasets that encompass five
representative tasks. The findings reveal that ChatGPT's performance on a
number of evaluated MPC tasks leaves much to be desired, whilst GPT-4's results
portend a promising future. Additionally, we endeavor to bolster performance
through the incorporation of MPC structures, encompassing both speaker and
addressee architecture. This study provides an exhaustive evaluation and
analysis of applying generative LLMs to MPCs, casting a light upon the
conception and creation of increasingly effective and robust MPC agents.
Concurrently, this work underscores the challenges implicit in the utilization
of LLMs for MPCs, such as deciphering graphical information flows and
generating stylistically consistent responses.Comment: Accepted by Findings of EMNLP 202
DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion
Diffusion models have emerged as the new state-of-the-art family of deep
generative models, and their promising potentials for text generation have
recently attracted increasing attention. Existing studies mostly adopt a single
encoder architecture with partially noising processes for conditional text
generation, but its degree of flexibility for conditional modeling is limited.
In fact, the encoder-decoder architecture is naturally more flexible for its
detachable encoder and decoder modules, which is extensible to multilingual and
multimodal generation tasks for conditions and target texts. However, the
encoding process of conditional texts lacks the understanding of target texts.
To this end, a spiral interaction architecture for encoder-decoder text
diffusion (DiffuSIA) is proposed. Concretely, the conditional information from
encoder is designed to be captured by the diffusion decoder, while the target
information from decoder is designed to be captured by the conditional encoder.
These two types of information flow run through multilayer interaction spirally
for deep fusion and understanding. DiffuSIA is evaluated on four text
generation tasks, including paraphrase, text simplification, question
generation, and open-domain dialogue generation. Experimental results show that
DiffuSIA achieves competitive performance among previous methods on all four
tasks, demonstrating the effectiveness and generalization ability of the
proposed method.Comment: Work in Progres
Radiative decays of the heavy-quark-spin molecular partner of
With the assumptions that the discovered at LHCb is a
hadronic molecule, using a nonrelativistic effective field theory we calculate
the radiative partial widths of with being
a shallow bound state and the heavy-quark-spin partner of
. The rescattering effect with the pole is
taken into account. The results show that the isoscalar
rescattering can increase the tree-level decay width of by about , while decrease that of
by a similar amount. The two-body
partial decay widths of the into and
are also calculated, and the results are about and
, respectively. Considering that the needs to be
reconstructed from the or final state in an experimental
measurement, the four-body partial widths of the into
and are explicitly calculated, and we find that
the interference effect between different intermediate states is
small. The total radiative decay width of the is predicted to be
about . Adding the hadronic decay widths of , the total width of the is finally predicted to be
keV.Comment: 27 pages, 13 figures. arXiv admin note: text overlap with
arXiv:2211.0247
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