1,002 research outputs found
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction
Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs
representing emotions and their causes in a document. Existing methods tend to
overfit spurious correlations, such as positional bias in existing benchmark
datasets, rather than capturing semantic features. Inspired by recent work, we
explore leveraging large language model (LLM) to address ECPE task without
additional training. Despite strong capabilities, LLMs suffer from
uncontrollable outputs, resulting in mediocre performance. To address this, we
introduce chain-of-thought to mimic human cognitive process and propose the
Decomposed Emotion-Cause Chain (DECC) framework. Combining inducing inference
and logical pruning, DECC guides LLMs to tackle ECPE task. We further enhance
the framework by incorporating in-context learning. Experiment results
demonstrate the strength of DECC compared to state-of-the-art supervised
fine-tuning methods. Finally, we analyze the effectiveness of each component
and the robustness of the method in various scenarios, including different LLM
bases, rebalanced datasets, and multi-pair extraction.Comment: 13 pages, 5 figure
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Biomedical entity linking (BioEL) has achieved remarkable progress with the
help of pre-trained language models. However, existing BioEL methods usually
struggle to handle rare and difficult entities due to long-tailed distribution.
To address this limitation, we introduce a new scheme NN-BioEL, which
provides a BioEL model with the ability to reference similar instances from the
entire training corpus as clues for prediction, thus improving the
generalization capabilities. Moreover, we design a contrastive learning
objective with dynamic hard negative sampling (DHNS) that improves the quality
of the retrieved neighbors during inference. Extensive experimental results
show that NN-BioEL outperforms state-of-the-art baselines on several
datasets.Comment: Accepted by ICASSP 202
YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6
We develop an all-in-one computer vision toolbox named EasyCV to facilitate
the use of various SOTA computer vision methods. Recently, we add YOLOX-PAI, an
improved version of YOLOX, into EasyCV. We conduct ablation studies to
investigate the influence of some detection methods on YOLOX. We also provide
an easy use for PAI-Blade which is used to accelerate the inference process
based on BladeDISC and TensorRT. Finally, we receive 42.8 mAP on COCO dateset
within 1.0 ms on a single NVIDIA V100 GPU, which is a bit faster than YOLOv6. A
simple but efficient predictor api is also designed in EasyCV to conduct
end2end object detection. Codes and models are now available at:
https://github.com/alibaba/EasyCV.Comment: 5 pages, 5 figure
Intelligent Omni Surfaces assisted Integrated Multi Target Sensing and Multi User MIMO Communications
Drawing inspiration from the advantages of intelligent reflecting surfaces
(IRS) in wireless networks,this paper presents a novel design for intelligent
omni surface (IOS) enabled integrated sensing and communications (ISAC). By
harnessing the power of multi antennas and a multitude of elements, the
dual-function base station (BS) and IOS collaborate to realize joint active and
passive beamforming, enabling seamless 360-degree ISAC coverage. The objective
is to maximize the minimum signal-tointerference-plus-noise ratio (SINR) of
multi-target sensing, while ensuring the multi-user multi-stream
communications. To achieve this, a comprehensive optimization approach is
employed, encompassing the design of radar receive vector, transmit beamforming
matrix, and IOS transmissive and reflective coefficients. Due to the non-convex
nature of the formulated problem, an auxiliary variable is introduced to
transform it into a more tractable form. Consequently, the problem is
decomposed into three subproblems based on the block coordinate descent
algorithm. Semidefinite relaxation and successive convex approximation methods
are leveraged to convert the sub-problem into a convex problem, while the
iterative rank minimization algorithm and penalty function method ensure the
equivalence. Furthermore,the scenario is extended to mode switching and time
switching protocols. Simulation results validate the convergence and superior
performance of the proposed algorithm compared to other benchmark algorithms.Comment: 30 pages, 7 figure
Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks
In this paper, we propose a state-of-the-art downlink communication
transceiver design for transmissive reconfigurable metasurface (RMS)-enabled
simultaneous wireless information and power transfer (SWIPT) networks.
Specifically, a feed antenna is deployed in the transmissive RMS-based
transceiver, which can be used to implement beamforming. According to the
relationship between wavelength and propagation distance, the spatial
propagation models of plane and spherical waves are built. Then, in the case of
imperfect channel state information (CSI), we formulate a robust system
sum-rate maximization problem that jointly optimizes RMS transmissive
coefficient, transmit power allocation, and power splitting ratio design while
taking account of the non-linear energy harvesting model and outage probability
criterion. Since the coupling of optimization variables, the whole optimization
problem is non-convex and cannot be solved directly. Therefore, the alternating
optimization (AO) framework is implemented to decompose the non-convex original
problem. In detail, the whole problem is divided into three sub-problems to
solve. For the non-convexity of the objective function, successive convex
approximation (SCA) is used to transform it, and penalty function method and
difference-of-convex (DC) programming are applied to deal with the non-convex
constraints. Finally, we alternately solve the three sub-problems until the
entire optimization problem converges. Numerical results show that our proposed
algorithm has convergence and better performance than other benchmark
algorithms
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Recently, diffusion-based deep generative models (e.g., Stable Diffusion)
have shown impressive results in text-to-image synthesis. However, current
text-to-image models often require multiple passes of prompt engineering by
humans in order to produce satisfactory results for real-world applications. We
propose BeautifulPrompt, a deep generative model to produce high-quality
prompts from very simple raw descriptions, which enables diffusion-based models
to generate more beautiful images. In our work, we first fine-tuned the
BeautifulPrompt model over low-quality and high-quality collecting prompt
pairs. Then, to ensure that our generated prompts can generate more beautiful
images, we further propose a Reinforcement Learning with Visual AI Feedback
technique to fine-tune our model to maximize the reward values of the generated
prompts, where the reward values are calculated based on the PickScore and the
Aesthetic Scores. Our results demonstrate that learning from visual AI feedback
promises the potential to improve the quality of generated prompts and images
significantly. We further showcase the integration of BeautifulPrompt to a
cloud-native AI platform to provide better text-to-image generation service in
the cloud.Comment: emnlp 202
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