Natural Language Processing

Text understanding and language tasks

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Llms

[R] Is autoresearch really better than classic hyperparameter tuning?

We did experiments comparing Optuna & autoresearch. Autoresearch converges faster, is more cost-efficient, and even generalizes bette...

Reddit - Machine Learning · 1 min ·
Nlp

Automate IOS devices through XCUITest with droidrun.

Automate iOS apps with XCUITest and Droidrun using just natural language. You send the command to Droidrun, and the agent starts the task...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

[P] Trained a small BERT on 276K Kubernetes YAMLs using tree positional encoding instead of sequential

I trained a BERT-style transformer on 276K Kubernetes YAML files, replacing standard positional encoding with learned tree coordinates (d...

Reddit - Machine Learning · 1 min ·

All Content

[2602.21598] Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
Nlp

[2602.21598] Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access

This article explores the challenges of retrieving public service information in low-resource environments, focusing on food pantry acces...

arXiv - AI · 3 min ·
[2602.21553] Revisiting RAG Retrievers: An Information Theoretic Benchmark
Llms

[2602.21553] Revisiting RAG Retrievers: An Information Theoretic Benchmark

This paper presents MIGRASCOPE, a new framework for evaluating RAG retrievers, emphasizing the need for systematic benchmarks and metrics...

arXiv - Machine Learning · 4 min ·
[2602.21543] Enhancing Multilingual Embeddings via Multi-Way Parallel Text Alignment
Machine Learning

[2602.21543] Enhancing Multilingual Embeddings via Multi-Way Parallel Text Alignment

This paper presents a method for enhancing multilingual embeddings through multi-way parallel text alignment, demonstrating improved cros...

arXiv - AI · 3 min ·
[2602.21522] One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models
Llms

[2602.21522] One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models

The paper introduces NOBEL, a large language model that unifies non-invasive brain decoding by integrating EEG, MEG, and fMRI signals, en...

arXiv - AI · 4 min ·
[2602.21456] Revisiting Text Ranking in Deep Research
Llms

[2602.21456] Revisiting Text Ranking in Deep Research

The paper 'Revisiting Text Ranking in Deep Research' explores the effectiveness of text ranking methods in deep research settings, focusi...

arXiv - AI · 4 min ·
[2602.21447] Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG
Machine Learning

[2602.21447] Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

The paper presents a novel framework, MMA-RAG^T, for enhancing the security of multimodal agentic retrieval-augmented generation systems ...

arXiv - Machine Learning · 4 min ·
[2602.21441] Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
Llms

[2602.21441] Causal Decoding for Hallucination-Resistant Multimodal Large Language Models

This article presents a novel causal decoding framework aimed at reducing object hallucination in multimodal large language models (MLLMs...

arXiv - Machine Learning · 3 min ·
[2602.21399] FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
Machine Learning

[2602.21399] FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning

The paper presents FedVG, a novel gradient-guided aggregation framework for federated learning that enhances model performance by address...

arXiv - Machine Learning · 4 min ·
[2602.21374] Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
Llms

[2602.21374] Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

This study explores the use of small language models for extracting clinical information from low-resource languages, focusing on a priva...

arXiv - Machine Learning · 4 min ·
[2602.21379] MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
Machine Learning

[2602.21379] MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation

MrBERT introduces a family of multilingual encoders optimized for various domains, achieving state-of-the-art results in specific tasks w...

arXiv - Machine Learning · 3 min ·
[2602.21372] The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging
Machine Learning

[2602.21372] The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging

This article presents an entropy-adaptive model merging technique for medical imaging that addresses challenges posed by heterogeneous do...

arXiv - Machine Learning · 4 min ·
[2602.21232] Urban Vibrancy Embedding and Application on Traffic Prediction
Machine Learning

[2602.21232] Urban Vibrancy Embedding and Application on Traffic Prediction

This paper presents a novel method for traffic prediction using Urban Vibrancy embeddings derived from real-time population data, enhanci...

arXiv - Machine Learning · 3 min ·
[2602.21225] Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal
Machine Learning

[2602.21225] Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal

This paper explores architecture-agnostic curriculum learning for document understanding, demonstrating efficiency gains in training time...

arXiv - Machine Learning · 4 min ·
[2602.21223] Measuring Pragmatic Influence in Large Language Model Instructions
Llms

[2602.21223] Measuring Pragmatic Influence in Large Language Model Instructions

This article explores how pragmatic framing in large language model instructions influences their behavior, introducing a framework to me...

arXiv - AI · 3 min ·
[2602.21222] Task-Aware LoRA Adapter Composition via Similarity Retrieval in Vector Databases
Llms

[2602.21222] Task-Aware LoRA Adapter Composition via Similarity Retrieval in Vector Databases

This paper presents a novel framework for dynamic LoRA adapter composition using similarity retrieval in vector databases, enabling effic...

arXiv - Machine Learning · 4 min ·
[2602.21219] Reasoning-Based Personalized Generation for Users with Sparse Data
Llms

[2602.21219] Reasoning-Based Personalized Generation for Users with Sparse Data

This article presents GraSPer, a novel framework designed to enhance personalized text generation for users with sparse data, addressing ...

arXiv - AI · 3 min ·
[2602.21220] Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation
Ai Agents

[2602.21220] Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation

The paper presents a novel memory system for AI agents, utilizing continuous fields governed by partial differential equations to enhance...

arXiv - Machine Learning · 3 min ·
[2602.21217] Applied Sociolinguistic AI for Community Development (ASA-CD): A New Scientific Paradigm for Linguistically-Grounded Social Intervention
Nlp

[2602.21217] Applied Sociolinguistic AI for Community Development (ASA-CD): A New Scientific Paradigm for Linguistically-Grounded Social Intervention

The paper introduces Applied Sociolinguistic AI for Community Development (ASA-CD), a paradigm that leverages AI and linguistics to addre...

arXiv - AI · 3 min ·
[2602.21216] EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning
Llms

[2602.21216] EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning

This article presents a study on enhancing EQ-5D classification using biomedical entity-enriched pre-trained language models and multiple...

arXiv - AI · 4 min ·
[2602.22067] Semantic Partial Grounding via LLMs
Llms

[2602.22067] Semantic Partial Grounding via LLMs

The paper proposes SPG-LLM, a novel approach for semantic partial grounding in AI planning that utilizes large language models (LLMs) to ...

arXiv - AI · 3 min ·
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