arxiv_cv
Abstract: Abstract: Recent successes in image analysis with deep neural networks are achieved
almost exclusively with Convolutional Neural Networks (CNNs), typically trained
using the backpropagation (BP) algorithm. In a 2022 preprint, Geoffrey Hinton
proposed...
#Deep Learning Training Methods#Alternative Learning Algorithms#Neural Network Architectures#Computational Neuroscience#Image Analysis
arxiv_cv
Abstract: Abstract: Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data
for Earth observation but pose challenges for existing multimodal foundation
models due to two key bottlenecks: (1) limited availability of UHR training
data, and ...
#Multimodal AI#Large Language Models#Remote Sensing#Computer Vision#Earth Observation
arxiv_cl
Abstract: Abstract: We derive and investigate two DPO variants that explicitly model the
possibility of declaring a tie in pair-wise comparisons. We replace the
Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and
Kupper and by Davids...
#Reinforcement Learning from Human Feedback (RLHF)#AI Alignment#Preference Learning#Natural Language Generation#Model Optimization
arxiv_ml
Abstract: Abstract: Large Language Models (LLMs) can sometimes degrade into repetitive loops,
persistently generating identical word sequences. Because repetition is rare in
natural human language, its frequent occurrence across diverse tasks and
contexts in L...
#LLM Behavior Analysis#Model Interpretability#Natural Language Generation Issues#Machine Learning Training Dynamics#Attention Mechanisms
arxiv_cl
Abstract: Abstract: Typical search agents concatenate the entire interaction history into the LLM
context, preserving information integrity but producing long, noisy contexts,
resulting in high computation and memory costs. In contrast, using only the
current ...
#Agent Systems#Memory in AI#Reinforcement Learning#Natural Language Processing#Search Algorithms
arxiv_cv
Abstract: Abstract: Background: Alzheimer's disease (AD) diagnosis heavily relies on amyloid-beta
positron emission tomography (Abeta-PET), which is limited by high cost and
limited accessibility. This study explores whether Abeta-PET spatial patterns
can be p...
#Medical Imaging#Generative AI#Alzheimer's Disease#Machine Learning#Multimodal Learning#Biomarkers
arxiv_ml
Abstract: Abstract: Although synthetic data has changed various aspects of information retrieval
(IR) pipelines, the main training paradigm remains: contrastive learning with
binary relevance labels, where one positive document is compared against
several nega...
#Information Retrieval#Learning to Rank#Synthetic Data Generation#Deep Learning for IR#LLM Applications
arxiv_cv
Abstract: Abstract: Complex chart understanding tasks demand advanced visual recognition and
reasoning capabilities from multimodal large language models (MLLMs). However,
current research provides limited coverage of complex chart scenarios and
computation-in...
#Natural Language Processing#Computer Vision#Multimodal AI#Large Language Models#Data Generation#Visual Reasoning
arxiv_cl
Abstract: Abstract: Multi-personality generation for LLMs, enabling simultaneous embodiment of
multiple personalization attributes, is a fundamental challenge. Existing
retraining-based approaches are costly and poorly scalable, while decoding-time
methods oft...
#LLM Personalization#Conditional Text Generation#Efficient Inference#Decoding Strategies#Controllable Generation
arxiv_cl
Abstract: Abstract: Large Language Models (LLMs) have made significant strides in problem-solving
by incorporating reasoning processes. However, this enhanced reasoning
capability results in an increased number of output tokens during inference,
leading to hig...
#Large Language Models#Efficient AI#Machine Learning Optimization#Knowledge Distillation#Reasoning in AI
arxiv_cl
Abstract: Abstract: Standard training for Multi-modal Large Language Models (MLLMs) involves
concatenating non-textual information, like vision or audio, with a text
prompt. This approach may not encourage deep integration of modalities,
limiting the model's a...
#Multimodal Learning#Instruction Tuning#Semantic Reasoning#Model Evaluation#Audio Processing
arxiv_cl
Abstract: Abstract: Peer review is central to academic publishing, but the growing volume of
submissions is straining the process. This motivates the development of
computational approaches to support peer review. While each review is tailored
to a specific pa...
#Natural Language Processing#Information Extraction#Academic Publishing#Machine Learning Applications#Text Mining
arxiv_cl
Abstract: Abstract: With the growing computational demands of large language models (LLMs),
efficient inference has become increasingly critical for practical deployment.
Depth pruning has emerged as a promising approach for reducing the
computational costs of...
#Model Compression#Efficient Deep Learning#LLM Inference#Neural Network Architecture Search#Hardware Acceleration
arxiv_cl
Abstract: Abstract: Question generation (QG) is a natural language processing task with an
abundance of potential benefits and use cases in the educational domain. In
order for this potential to be realized, QG systems must be designed and
validated with pedag...
#Educational AI#Natural Language Generation#Pedagogy and AI#LLM Applications#Automated Assessment
arxiv_cl
Abstract: Abstract: To enhance the reasoning capabilities of large language models (LLMs),
self-consistency has become a popular approach, combining multiple samplings
with majority voting. However, current methods are computationally expensive
and time-consum...
#Large Language Models#Efficient AI#Machine Learning Optimization#Reasoning in AI#Inference Techniques
arxiv_cl
Abstract: Abstract: Agentic workflows have become the dominant paradigm for building complex AI
systems, orchestrating specialized components, such as planning, reasoning,
action execution, and reflection, to tackle sophisticated real-world tasks.
However, sys...
#AI System Design#Agent-based Systems#Explainable AI (XAI)#Optimization#Machine Learning Theory
arxiv_cv
Abstract: Abstract: Large multimodal models (LMMs) often suffer from severe inference
inefficiency due to the large number of visual tokens introduced by image
encoders. While recent token compression methods, such as pruning and merging,
have shown promise in...
#Multimodal AI#Large Language Models#Model Compression#Inference Optimization#AI Benchmarking
arxiv_cl
Abstract: Abstract: The use of large language models (LLMs) is becoming common in political
science and digital media research. While LLMs have demonstrated ability in
labelling tasks, their effectiveness to classify Political Content (PC) from
URLs remains un...
#Political Science#Digital Media Analysis#Natural Language Processing#LLM Evaluation#Computational Social Science
arxiv_cl
Abstract: Abstract: Most text retrievers generate \emph{one} query vector to retrieve relevant
documents. Yet, the conditional distribution of relevant documents for the
query may be multimodal, e.g., representing different interpretations of the
query. We fir...
#Information Retrieval#Natural Language Processing#Machine Learning Architectures#Vector Search#Representation Learning
arxiv_cl
Abstract: Abstract: Current large language models excel at broad, general-purpose tasks, but
consistently underperform when exposed to highly specialized domains that
require deep cultural, linguistic, and subject-matter expertise. In particular,
traditional m...
#Specialized AI Models#Computational Linguistics#Medical AI#Digital Health#Cross-lingual NLP
arxiv_cl
Abstract: Abstract: Adapting visual programming or prompting large language models (LLMs) to
generate executable code for visual tasks like visual question answering (VQA)
for specialized tasks or domains remains challenging due to high annotation and
inferenc...
#Visual Reasoning#Program Synthesis#LLM Adaptation#Efficient AI#Data Augmentation
arxiv_cl
Abstract: Abstract: While LLMs excel at general tasks, they struggle in specialized domains like
finance, requiring diverse skills in domain knowledge, mathematical reasoning,
and multilingual processing. Merging domain-specific Continual Pre-training
(CPT) "e...
#Large Language Models#Model Compression and Merging#Domain Specialization#Transfer Learning#Natural Language Processing
arxiv_ml
Abstract: Abstract: The number of studies that combine Evolutionary Machine Learning and
self-supervised learning has been growing steadily in recent years.
Evolutionary Machine Learning has been shown to help automate the design of
machine learning algorithms...
#Machine Learning Automation#Representation Learning#Data Efficiency#Algorithm Design#Survey of ML Techniques
arxiv_cl
Abstract: Abstract: Large language models (LLMs) exhibit complementary strengths across domains
and come with varying inference costs, motivating the design of multi-agent LLM
systems where specialized models collaborate efficiently. Existing approaches
predom...
#Multi-Agent Systems#Large Language Models#Reinforcement Learning#Resource Management#AI System Optimization
arxiv_cl
Abstract: Abstract: Current evaluation paradigms for large language models (LLMs) represent a
critical blind spot in AI research--relying on opaque numerical metrics that
conceal fundamental limitations in spatial reasoning while providing no
intuitive underst...
#LLM Evaluation#Spatial Reasoning#Multimodal AI#AI Benchmarking#Human-AI Interaction
arxiv_cl
Abstract: Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize
task completion, often overlooking resource efficiency and adaptability. This
neglects a crucial capability: agents' ability to devise and adjust
cost-optimal plan...
#AI Agents#Planning and Reasoning#LLM Tool Use#Resource Optimization#Benchmarking
arxiv_cl
Abstract: Abstract: We evaluate whether persona-based prompting improves Large Language Model
(LLM) performance on macroeconomic forecasting tasks. Using 2,368
economics-related personas from the PersonaHub corpus, we prompt GPT-4o to
replicate the ECB Survey ...
#Economics#Econometrics#Artificial Intelligence#Large Language Models#Forecasting Methods
arxiv_cl
Abstract: Abstract: As model context lengths continue to grow, concerns about whether models
effectively use the full context length have persisted. While several carefully
designed long-context evaluations have recently been released, these
evaluations tend t...
#Large Language Models#Context Window Management#Reasoning Capabilities#Benchmark Design#Natural Language Understanding
arxiv_cv
Abstract: Abstract: The automation of workflows in advanced microscopy is a key goal where
foundation models like Language Models (LLMs) and Vision-Language Models (VLMs)
show great potential. However, adapting these general-purpose models for
specialized scie...
#Machine Learning#Foundation Models#Domain Adaptation#Scientific AI#Microscopy#Ptychography#Low-Data Learning
arxiv_cl
Abstract: Abstract: Statistical analysis of corpora provides an approach to quantitatively
investigate natural languages. This approach has revealed that several power
laws consistently emerge across different corpora and languages, suggesting
universal mechan...
#Linguistics#Computational Linguistics#Statistical Language Modeling#Cognitive Science#Information Theory