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Found 16 papers, 8 papers with code
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM.
Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content
What if misinformation is not an information problem at all?
Molecular mechanism of anion permeation through aquaporin 6
Moreover, we demonstrate the pH-sensing mechanism in which the protonation of H184 and H189 under low pH conditions allosterically triggers the gating of the SF region.
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (. i. e, generating large-scale harmful and misleading content).
COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability
Recently, neural network (NN)-based image compression studies have actively been made and has shown impressive performance in comparison to traditional methods.
End-to-End Learnable Multi-Scale Feature Compression for VCM
In the feature compression track of MPEG-VCM, multi-scale features extracted from images are subject to compression.
Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription
This paper presents a large-scale analysis of L2 Korean pronunciation error patterns from five different language backgrounds, Chinese, Vietnamese, Japanese, Thai, and English, by using automatic phonetic transcription.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
Selective compression learning of latent representations for variable-rate image compression
For this, we first generate a 3D importance map as the nature of input content to represent the underlying importance of the representation elements.
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.
Do Language Models Plagiarize?
Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity.
An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7. 1), BPG, and JPEG2000.
A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism
Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind.
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
We propose a context-adaptive entropy model for use in end-to-end optimized image compression.
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning.
Self-adaptive node-based PCA encodings
In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes.
