🎯 Dependent Parsing

Context-sensitive Grammars, Type-dependent Syntax, Proof-carrying Parsers, Verified Parsing

C++26: range support for std:optional
sandordargo.com·8h·
Discuss: Lobsters, Hacker News
💧Liquid Types
PsycholexTherapy: Simulating Reasoning in Psychotherapy with Small Language Models in Persian
arxiv.org·1d
💻Programming languages
Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
arxiv.org·1d
🧠Machine Learning
Seeing the Big Picture: Evaluating Multimodal LLMs' Ability to Interpret and Grade Handwritten Student Work
arxiv.org·19h
🤖Advanced OCR
Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study
arxiv.org·1d
📜Digital Philology
Visual Representations inside the Language Model
arxiv.org·1d
🧮Vector Embeddings
Cactus Language • Semantics 1
inquiryintoinquiry.com·2d
🔢Denotational Semantics
Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study
arxiv.org·1d
🤖AI Curation
Understanding Retrieval Augmentation for Long-Form Question Answering
arxiv.org·1d
🔍Information Retrieval
On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records
arxiv.org·1d
🔍Information Retrieval
Approximate Gaussianity Beyond Initialisation in Neural Networks
arxiv.org·19h
🕸️Tensor Networks
🚀 Hello DEV Community! I’m Thiyagarajan Varadharajan – Python Full Stack Developer | AI Enthusiast
dev.to·23h·
Discuss: DEV
🌀Brotli Internals
Meta-Awareness Enhances Reasoning Models: Self-Alignment Reinforcement Learning
arxiv.org·1d
🧠Intelligence Compression
Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
arxiv.org·1d
🧠Machine Learning
Operationalizing Data Minimization for Privacy-Preserving LLM Prompting
arxiv.org·1d
💻Local LLMs
Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions
arxiv.org·1d
🧠Machine Learning
Revealing Interconnections between Diseases: from Statistical Methods to Large Language Models
arxiv.org·1d
🧠Machine Learning
Decoding Emotion in the Deep: A Systematic Study of How LLMs Represent, Retain, and Express Emotion
arxiv.org·1d
💻Local LLMs
Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
arxiv.org·2d
🎙️Whisper
MedReflect: Teaching Medical LLMs to Self-Improve via Reflective Correction
arxiv.org·1d
💻Local LLMs