This page contains one-sentence summaries of cs.AI/ML/CV/CL papers announced on 2024-08-05 generated by the compressor, my personal LLM-based project.
http://arxiv.org/abs/2408.01423v1
Compressor summary: The text proposes a new Prompt Recursive Search (PRS) framework that improves large language models' NLP performance by adjusting prompts based on problem complexity and structure.
http://arxiv.org/abs/2408.01420v1
Compressor summary: The paper studies how large language models can be influenced to generate harmful or undesired behaviours, such as leaking information or spreading fake news, through a process called jailbreaking, and proposes a new alignment strategy called E-RLHF that aims to increase the likelihood of safe responses.
http://arxiv.org/abs/2408.01419v1
Compressor summary: DebateQA is a new dataset for evaluating LLM chatbots' ability to answer debatable questions with multiple perspectives, using two metrics: Perspective Diversity and Dispute Awareness.
http://arxiv.org/abs/2408.01417v1
Compressor summary: The paper introduces ICCA, a framework to evaluate if large language models adapt their communication efficiency during interactions like humans do, and finds that current models don't show this ability without prompting.
http://arxiv.org/abs/2408.01416v1
Compressor summary: The paper proposes a unified perspective on interpretability research using causal mediation analysis and suggests focusing on discovering new mediators with better trade-offs between human-interpretability and compute-efficiency.
http://arxiv.org/abs/2408.01415v1
Compressor summary: COND P-DIFF is a method that generates high-performance neural network parameters based on task conditions using an autoencoder and a conditional latent diffusion model.
http://arxiv.org/abs/2408.01408v1
Compressor summary: The paper derives the backpropagation algorithm for graph convolutional networks using matrix calculus, shows its effectiveness in node classification and link prediction tasks, and discusses its potential for explainability and sensitivity analysis.
http://arxiv.org/abs/2408.01402v1
Compressor summary: LPDT is a new method for offline reinforcement learning that uses pre-trained language models and low-rank adaptation to improve performance and task distinction with prompts.
http://arxiv.org/abs/2408.01394v1
Compressor summary: The paper proposes a method to improve multilingual neural machine translation by exploiting semantic and linguistic features from multiple languages using disentangling learning and linguistic encoder tasks.
http://arxiv.org/abs/2408.01384v1
Compressor summary: NOLO learns a video navigation policy from context videos without finetuning or re-training, using optical flow and offline reinforcement learning.
http://arxiv.org/abs/2408.01382v1
Compressor summary: Shapley compositions use the Aitchison geometry to explain multiclass probabilistic predictions by quantifying the contribution of each feature's value, satisfying axiomatic properties like linearity and efficiency.
http://arxiv.org/abs/2408.01380v1
Compressor summary: The text discusses using a group of specialized large language models to perform tasks more efficiently than one single model, reducing the need for fine-tuning and improving robustness.
http://arxiv.org/abs/2408.01375v1
Compressor summary: The text discusses how participatory biomedical studies can improve representation in datasets by using a computational approach to allocate recruitment resources and shows its effectiveness in simulated studies.
http://arxiv.org/abs/2408.01374v1
Compressor summary: The paper introduces a new algorithm that combines line search and gradient methods for faster and more efficient parameter updates in neural networks.
http://arxiv.org/abs/2408.01372v1
Compressor summary: The MorpMamba model combines spatial-spectral tokens, morphology blocks, and self-attention to achieve efficient and effective hyperspectral image classification, surpassing CNN and Transformer models.
http://arxiv.org/abs/2408.01370v1
Compressor summary: Event cameras track objects using maps from other sensors, and adding inertial signals improves performance in changing lighting and dynamics.
http://arxiv.org/abs/2408.01367v1
Compressor summary: This paper shows that deep transformers can handle an unlimited number of context tokens and approximate continuous in-context mappings with high precision using a fixed embedding dimension, number of heads, and MLP layers.
http://arxiv.org/abs/2408.01355v1
Compressor summary: The paper introduces Hallu-PI, a benchmark to evaluate hallucination in large language models on perturbed images with various types of hallucinations and tasks.
http://arxiv.org/abs/2408.01332v1
Compressor summary: HMDN is a model that can handle diverse distributions by efficiently capturing hierarchical relationships among different types of distributions, improving recommendation performance.
http://arxiv.org/abs/2408.01331v1
Compressor summary: UnifiedNN is a framework for efficiently training multiple neural network models simultaneously on the cloud, reducing memory and training time costs without sacrificing accuracy.
http://arxiv.org/abs/2408.01323v1
Compressor summary: FANNO is a framework that uses a large language model to create diverse and high-quality instruction datasets without the need for manual annotations or costly API calls.
http://arxiv.org/abs/2408.01319v1
Compressor summary: The paper explores the applications, advantages, limitations, and future directions of multimodal large language models (MLLMs) that integrate diverse data types in AI systems.
http://arxiv.org/abs/2408.01311v1
Compressor summary: TopoNAS is a model-agnostic method that simplifies the search space for one-shot Neural Architecture Search, reducing time and memory usage while maintaining accuracy.
http://arxiv.org/abs/2408.01308v1
Compressor summary: DefinitionEMB uses definitions from Wiktionary to create isotropic and meaningful token embeddings for PLMs that maintain robustness during fine-tuning and improve performance on various tasks.
http://arxiv.org/abs/2408.01307v1
Compressor summary: This paper proposes a new method for analyzing IoT data called DSAD, which improves identification of significant predictors and ensures uniform model performance across distributed nodes.
http://arxiv.org/abs/2408.01297v1
Compressor summary: Key points: - Multivariate decision trees are machine learning tools for classification and regression - Optimal binary trees are obtained by solving a biobjective optimization problem - The paper proposes two cut-based MILO formulations for designing optimal binary classification trees - The models use minimal infeasible subsystems to derive cutting planes - The models have theoretical and empirical advantages over existing methods Summary: The paper presents novel cut-based MILO formulations for designing optimal binary classification trees, which are machine learning tools that balance classification accuracy and tree complexity using minimal infeasible subsystems as cutting planes.
http://arxiv.org/abs/2408.01294v1
Compressor summary: Feature Clock is a new tool that simplifies the explanation of high-dimensional data in two-dimensional plots, making it easier to understand complex relationships.
http://arxiv.org/abs/2408.01293v1
Compressor summary: The paper presents a novel method for underwater object detection using Detectron2, image enhancement, and channel stabilization techniques to improve accuracy in detecting marine trash.
http://arxiv.org/abs/2408.01291v1
Compressor summary: TexGen is a novel framework for generating high-quality 3D textures from textual descriptions using multi-view sampling, attention guidance, and noise resampling.
http://arxiv.org/abs/2408.01099v1
Compressor summary: Key points: - The paper proposes a new efficient parameter tuning method (CoLoRA) and pre-training strategy (PROD) for low-level computer vision tasks such as image restoration. - CoLoRA fine-tunes only a small amount of parameters for each task using low-rank adaptation and contribution-based method. - PROD uses random order degradations to extend the capability of pre-trained models and improve performance and robustness. Summary: The paper introduces CoLoRA, a novel efficient parameter tuning method that adapts to different image restoration tasks with low-rank adaptation and contribution-based method, and PROD, a pre-training strategy that uses random order degradations to enhance the performance and robustness of pre-trained models.
http://arxiv.org/abs/2408.01094v1
Compressor summary: The paper proposes a new perspective on the bi-encoder architecture for neural search by separating the encoding and searching operations to address its limitations and improve performance.
http://arxiv.org/abs/2408.01091v1
Compressor summary: The text introduces a new benchmark for testing large multimodal models' ability to handle self-contradictory instructions and proposes a method to improve their performance in recognizing conflicting commands.
http://arxiv.org/abs/2408.01090v1
Compressor summary: Neuromorphic dataflow is a tailored dataflow model for neuromorphic hardware that allows general-purpose programs to run efficiently and flexibly on spiking neural networks.
http://arxiv.org/abs/2408.01089v1
Compressor summary: The paper proposes a novel approach called mini-batch Prototypical Partial Optimal Transport (m-PPOT) for universal domain adaptation, which partially aligns two distributions and distinguishes "known" and "unknown" samples using reweighted losses.
http://arxiv.org/abs/2408.01088v1
Compressor summary: The text studies how large language models can help dialogue systems bridge information gaps by understanding natural language expressions and connecting them to internal knowledge, using a new corpus called BridgeKG.
http://arxiv.org/abs/2408.01084v1
Compressor summary: Adaptive contrastive decoding improves open-domain question answering by handling noisy contexts better than previous methods.
http://arxiv.org/abs/2408.01080v1
Compressor summary: The paper introduces FCDFusion, a fast and accurate image fusion method that preserves color information without color space transformations and with low computational cost.
http://arxiv.org/abs/2408.01077v1
Compressor summary: PhysMamba is a dual-stream time-frequency interactive model that uses Cross-Attention State Space Duality to improve information exchange and feature complementarity for robust remote heart rate monitoring in noisy real-world environments.
http://arxiv.org/abs/2408.01076v1
Compressor summary: Our method uses text embeddings to capture semantic similarity and integrate semantic guidance within and across tasks for continual learning with DNNs, improving performance on general and fine-grained datasets.
http://arxiv.org/abs/2408.01072v1
Compressor summary: This paper explains self-play in reinforcement learning, its preliminaries, classifications, applications, challenges, and future directions.
http://arxiv.org/abs/2408.01063v1
Compressor summary: The study proposes a new method to improve feature extraction from mobile app reviews using encoder-only Transformer models with extended pre-training and instance selection techniques.
http://arxiv.org/abs/2408.01051v1
Compressor summary: The workshop will create a community of researchers, synthesize a roadmap for contestable AI, and facilitate interdisciplinary dialogue on opportunities and challenges in AI value chains.
http://arxiv.org/abs/2408.01046v1
Compressor summary: QUDSELECT is a new approach to QUD parsing that jointly trains models to predict anchor sentences and generate questions while considering theoretical criteria using selective decoding and instruction-tuning.
http://arxiv.org/abs/2408.01038v1
Compressor summary: UNER is a query-aware entity extraction head that collaborates with multi-modal document transformers to improve named entity recognition (NER) in visually-rich documents, addressing complex layouts, reading orders, and discontinuous entities.
http://arxiv.org/abs/2408.01031v1
Compressor summary: The POA framework trains one large model that can adapt to different sizes and tasks by randomly sampling sub-networks for self-distillation, achieving state-of-the-art performance on various vision tasks.
http://arxiv.org/abs/2408.01024v1
Compressor summary: The SemGro framework uses pretrained language models to plan tasks by grounding semantic skills in different domains, using iterative skill decomposition and reasoning capabilities.
http://arxiv.org/abs/2408.01018v1
Compressor summary: The paper proposes GNN-MolKAN and GNN-MolKAN+, new GNNs that use KAN architecture to improve molecular representations for property prediction and drug design, with benefits in performance, efficiency, and few-shot learning.
http://arxiv.org/abs/2408.01016v1
Compressor summary: The paper introduces a new traffic dataset and a model that improves traffic prediction by considering temporal and spatial relationships in the data.
http://arxiv.org/abs/2408.01014v1
Compressor summary: The authors propose a method to purify text inputs for text-to-image models, which uses attention mechanisms and erasure prompts to suppress non-compliant image features and generate safer images.
http://arxiv.org/abs/2408.01008v1
Compressor summary: TT-LoRA is a new method for compressing large language models without losing performance, making them suitable for low-resource devices.
http://arxiv.org/abs/2408.01005v1
Compressor summary: The FinSen dataset combines news articles and stock market data from 197 countries to improve financial forecasting accuracy and reliability using sentiment analysis and calibration techniques.
http://arxiv.org/abs/2408.01003v1
Compressor summary: Piculet is a training-free method that uses multiple specialized models to provide better input representations for multimodal language models, reducing hallucinations and improving performance.
http://arxiv.org/abs/2408.01000v1
Compressor summary: HARMONY is a system that uses hierarchical attention and Bayesian decision-making to efficiently allocate resources in cloud computing, saving costs and improving performance.
http://arxiv.org/abs/2408.00998v1
Compressor summary: This paper presents a novel method to adapt a text-to-image diffusion model for image-to-image translation using reference images, enabling high-quality and versatile content creation with minimal effort.
http://arxiv.org/abs/2408.00997v1
Compressor summary: The paper introduces a framework for model-free reinforcement learning agents that helps them explore grid environments safely by learning to identify and avoid potentially unsafe states using a binary classifier.
http://arxiv.org/abs/2408.00996v1
Compressor summary: IncidentNet is a deep learning model that accurately detects, locates, and estimates the severity of traffic incidents using sparse sensor data from urban intersections.
http://arxiv.org/abs/2408.00992v1
Compressor summary: This tutorial covers recent advances in fairness considerations for large language models, including case studies, bias analysis, evaluation strategies, and resources.
http://arxiv.org/abs/2408.00989v1
Compressor summary: This paper studies how malicious agents affect multi-agent systems and proposes methods to improve their resilience, finding that a hierarchical structure and additional reviewers or challengers enhance system performance.
http://arxiv.org/abs/2408.00986v1
Compressor summary: The paper introduces a verification framework for Bayesian networks that uses Boolean logic literals to check their properties and improve safety in machine learning applications.
http://arxiv.org/abs/2408.00985v1
Compressor summary: The summary sentence would be: A trained transformer network can accurately recover Richtmyer-Meshkoff instability from noisy radiographic images using self-attention layers.
http://arxiv.org/abs/2408.00981v1
Compressor summary: The paper proposes a graph matching approach to improve cross-domain named entity recognition (NER) by fusing label graphs into BERT embeddings, enhancing the model's ability to adapt from general to specific domains.
http://arxiv.org/abs/2408.00966v1
Compressor summary: The paper presents a graph-based method to analyze relationships among motivations, emotions, and actions in natural language texts, using a large food review dataset and nurture beliefs.
http://arxiv.org/abs/2408.00965v1
Compressor summary: The ESG-AI framework is a novel approach that helps investors assess the environmental and social impacts of AI applications and evaluate a company's commitment to responsible AI.
http://arxiv.org/abs/2408.00963v1
Compressor summary: The paper introduces MIS-ME, a software tool that predicts soil moisture using smartphone images and weather forecasts, outperforming traditional methods.
http://arxiv.org/abs/2408.00960v1
Compressor summary: PERSOMA is a new method that uses soft prompt embeddings to capture and adapt to users' interaction history for personalized natural language systems.