#large language models
Large Language Models: 59 AI articles covering large language models news, analysis, and research
Articles
QueryStory Wants You to Trust What AI Tells You⭐9
QueryStory launches with a platform that uses AI to turn complex corporate data into verified narratives, helping enterprises trust AI insights.
AI Models Flub These Intelligence Tests. Can You Fare Any Better?⭐10
Think you're smarter than AI? Try these seven puzzles and brain teasers that stump AI models—see if you can outwit the machines.
Granite 4.2 LLMs: How They're Built⭐9
Explore how IBM builds Granite 4.2 LLMs: transformer architecture, reasoning, and multi-stage training for efficient AI.
KVBoost: Deviation-Guided Chunk-Level KV Cache Reuse for Efficient LLM Inference⭐8
KVBoost enables chunk-level KV cache reuse for LLMs, cutting time-to-first-token by 4.49x with 16% gains over prefix caching.
SDAD: Spec-Driven Agentic Development for the AI-Native Software Development Life Cycle⭐7
This paper formalizes SDAD, a spec-driven model for AI-native software development, examining its four stages, governance metrics, and impact on the SDLC.
Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa⭐9
Compare BERT, RoBERTa, and BART for extractive and abstractive text summarization, analyzing architectures and pretraining strategies.
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges⭐9
This systematic review examines LLM applications in mental health, covering detection, therapy support, multimodal methods, and ethical deployment challenges.
Margin-Regularized Structured Semantic Alignment for Brain⭐7
MD-SigLIP aligns brain and text embeddings via margin-regularized structured ranking, improving retrieval-based brain-language decoding.
Auxiliary Uncertainty Signals for LLM-Assisted Systematic Review⭐9
Auxiliary BERT+GCN signals improve LLM screening in systematic reviews, with full-context delivery boosting F1 and MAYBE-only routing as the most cost-efficient...
Amazon’s Rare Book Destruction for AI Training: A 2026 Perspective⭐9
Amazon destroys rare books for AI training data, raising ethical concerns about preserving human-authored texts in the race to build better models.