135 research outputs found
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
The hallucination issue is recognized as a fundamental deficiency of large
language models (LLMs), especially when applied to fields such as finance,
education, and law. Despite the growing concerns, there has been a lack of
empirical investigation. In this paper, we provide an empirical examination of
LLMs' hallucination behaviors in financial tasks. First, we empirically
investigate LLM model's ability of explaining financial concepts and
terminologies. Second, we assess LLM models' capacity of querying historical
stock prices. Third, to alleviate the hallucination issue, we evaluate the
efficacy of four practical methods, including few-shot learning, Decoding by
Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method
and the prompt-based tool learning method for a function to generate a query
command. Finally, our major finding is that off-the-shelf LLMs experience
serious hallucination behaviors in financial tasks. Therefore, there is an
urgent need to call for research efforts in mitigating LLMs' hallucination
Effective good divisibility of rational homogeneous varieties
We compute the effective good divisibility of a rational homogeneous variety,
extending an earlier result for complex Grassmannians by Naldi and Occhetta.
Non-existence of nonconstant morphisms to rational homogeneous varieties of
classical Lie type are obtained as applications.Comment: 22 pages. 2 figures. Comments are welcom
Low-Light Image Enhancement with Wavelet-based Diffusion Models
Diffusion models have achieved promising results in image restoration tasks,
yet suffer from time-consuming, excessive computational resource consumption,
and unstable restoration. To address these issues, we propose a robust and
efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
Specifically, we present a wavelet-based conditional diffusion model (WCDM)
that leverages the generative power of diffusion models to produce results with
satisfactory perceptual fidelity. Additionally, it also takes advantage of the
strengths of wavelet transformation to greatly accelerate inference and reduce
computational resource usage without sacrificing information. To avoid chaotic
content and diversity, we perform both forward diffusion and reverse denoising
in the training phase of WCDM, enabling the model to achieve stable denoising
and reduce randomness during inference. Moreover, we further design a
high-frequency restoration module (HFRM) that utilizes the vertical and
horizontal details of the image to complement the diagonal information for
better fine-grained restoration. Extensive experiments on publicly available
real-world benchmarks demonstrate that our method outperforms the existing
state-of-the-art methods both quantitatively and visually, and it achieves
remarkable improvements in efficiency compared to previous diffusion-based
methods. In addition, we empirically show that the application for low-light
face detection also reveals the latent practical values of our method
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Obtaining the ground truth labels from a video is challenging since the
manual annotation of pixel-wise flow labels is prohibitively expensive and
laborious. Besides, existing approaches try to adapt the trained model on
synthetic datasets to authentic videos, which inevitably suffers from domain
discrepancy and hinders the performance for real-world applications. To solve
these problems, we propose RealFlow, an Expectation-Maximization based
framework that can create large-scale optical flow datasets directly from any
unlabeled realistic videos. Specifically, we first estimate optical flow
between a pair of video frames, and then synthesize a new image from this pair
based on the predicted flow. Thus the new image pairs and their corresponding
flows can be regarded as a new training set. Besides, we design a Realistic
Image Pair Rendering (RIPR) module that adopts softmax splatting and
bi-directional hole filling techniques to alleviate the artifacts of the image
synthesis. In the E-step, RIPR renders new images to create a large quantity of
training data. In the M-step, we utilize the generated training data to train
an optical flow network, which can be used to estimate optical flows in the
next E-step. During the iterative learning steps, the capability of the flow
network is gradually improved, so is the accuracy of the flow, as well as the
quality of the synthesized dataset. Experimental results show that RealFlow
outperforms previous dataset generation methods by a considerably large margin.
Moreover, based on the generated dataset, our approach achieves
state-of-the-art performance on two standard benchmarks compared with both
supervised and unsupervised optical flow methods. Our code and dataset are
available at https://github.com/megvii-research/RealFlowComment: ECCV 2022 Ora
Supervised Homography Learning with Realistic Dataset Generation
In this paper, we propose an iterative framework, which consists of two
phases: a generation phase and a training phase, to generate realistic training
data and yield a supervised homography network. In the generation phase, given
an unlabeled image pair, we utilize the pre-estimated dominant plane masks and
homography of the pair, along with another sampled homography that serves as
ground truth to generate a new labeled training pair with realistic motion. In
the training phase, the generated data is used to train the supervised
homography network, in which the training data is refined via a content
consistency module and a quality assessment module. Once an iteration is
finished, the trained network is used in the next data generation phase to
update the pre-estimated homography. Through such an iterative strategy, the
quality of the dataset and the performance of the network can be gradually and
simultaneously improved. Experimental results show that our method achieves
state-of-the-art performance and existing supervised methods can be also
improved based on the generated dataset. Code and dataset are available at
https://github.com/megvii-research/RealSH.Comment: Accepted by ICCV 202
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques
for DNN-based raw image denoising. Compared with traditional RGB image
denoising, performing this task on direct camera sensor readings presents new
challenges such as how to effectively handle various Bayer patterns from
different data sources, and subsequently how to perform valid data augmentation
with raw images. To address the first problem, we propose a Bayer pattern
unification (BayerUnify) method to unify different Bayer patterns. This allows
us to fully utilize a heterogeneous dataset to train a single denoising model
instead of training one model for each pattern. Furthermore, while it is
essential to augment the dataset to improve model generalization and
performance, we discovered that it is error-prone to modify raw images by
adapting augmentation methods designed for RGB images. Towards this end, we
present a Bayer preserving augmentation (BayerAug) method as an effective
approach for raw image augmentation. Combining these data processing technqiues
with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969
in NTIRE 2019 Real Image Denoising Challenge, demonstrating the
state-of-the-art performance. Our code is available at
https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201
Realistic Noise Synthesis with Diffusion Models
Deep learning-based approaches have achieved remarkable performance in
single-image denoising. However, training denoising models typically requires a
large amount of data, which can be difficult to obtain in real-world scenarios.
Furthermore, synthetic noise used in the past has often produced significant
differences compared to real-world noise due to the complexity of the latter
and the poor modeling ability of noise distributions of Generative Adversarial
Network (GAN) models, resulting in residual noise and artifacts within
denoising models. To address these challenges, we propose a novel method for
synthesizing realistic noise using diffusion models. This approach enables us
to generate large amounts of high-quality data for training denoising models by
controlling camera settings to simulate different environmental conditions and
employing guided multi-scale content information to ensure that our method is
more capable of generating real noise with multi-frequency spatial
correlations. In particular, we design an inversion mechanism for the setting,
which extends our method to more public datasets without setting information.
Based on the noise dataset we synthesized, we have conducted sufficient
experiments on multiple benchmarks, and experimental results demonstrate that
our method outperforms state-of-the-art methods on multiple benchmarks and
metrics, demonstrating its effectiveness in synthesizing realistic noise for
training denoising models
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