LLM research
New research simulates prediction markets within LLMs to generate calibrated confidence signals, offering a novel approach to reduce hallucinations and improve output reliability.
forensic linguistics
New research examines how large language models are transforming forensic linguistics, creating both powerful detection tools and unprecedented challenges for authorship attribution and AI text identification.
mechanistic interpretability
New research reveals how GPT-2's layers divide labor between lexical and contextual processing during sentiment analysis, advancing our understanding of transformer internals.
AI safety
Researchers tackle AI safety with new methods to detect when chatbots subtly escalate conversations toward uncomfortable territory, addressing manipulation risks in synthetic interactions.
vision models
Z.ai debuts GLM-4.6V, an open-source multimodal vision model with native tool-calling capabilities for complex reasoning tasks and automated workflows.
AI Alignment
Researchers propose a scalable self-improving framework for open-ended LLM alignment that leverages collective agency principles to address evolving AI safety challenges.
LLM research
New research introduces a self-critique and refinement training approach that teaches LLMs to identify and correct their own summarization errors, reducing hallucinations and improving factual consistency.
AI detection
New research reveals linguistic markers that distinguish LLM-generated fake news sites from human journalism, offering robust detection methods against adversarial manipulation.
AI detection
New research reveals that iterative paraphrasing significantly degrades AI text detection accuracy, raising critical questions about the future of distinguishing human from machine-generated content.
AI Research
Academic researchers systematically analyze the types and patterns of bugs produced by large language models when generating code, offering insights into AI reliability limitations.
AI Research
New research uses large language models to systematically quantify errors in published AI papers, uncovering patterns of mistakes that could impact the reliability of AI research findings.
LLM Verification
Researchers introduce BEAVER, an efficient deterministic verification system for large language models that ensures reliable and consistent output validation for AI safety applications.