LLM Evaluation
New research introduces CARE, a confounder-aware aggregation method that improves LLM evaluation reliability by accounting for hidden variables that skew benchmark results.
Explainable AI
Learn how to implement SHAP-IQ for understanding feature importance and interaction effects in AI models, enabling transparent decision breakdowns essential for trustworthy systems.
LLM Benchmarking
Move past 'it sounds good' evaluations with five systematic benchmarking approaches for measuring LLM performance across accuracy, reasoning, and real-world tasks.
AI Interpretability
Modern AI systems achieve remarkable results but remain fundamentally opaque. The interpretability crisis threatens trust, safety, and accountability across all AI applications.
AI Safety
Researchers propose combining self-consistency sampling with conformal calibration to certify AI agent reliability without requiring access to internal model weights or architecture details.
Neural Networks
New framework converts opaque neural network decisions into interpretable mathematical expressions, enabling better model verification and understanding of AI behavior.
Content Moderation
New research proposes combining ML-assisted sampling with LLM labeling to measure policy-violating content at scale, offering a methodological breakthrough for detecting synthetic media and deepfakes.
AI Agents
Moving beyond simple accuracy, these five metrics—task success rate, tool usage accuracy, context coherence, response latency, and safety compliance—reveal what truly matters when assessing AI agents.
LLM Interpretability
New research introduces ADAPT, a hybrid optimization technique that combines discrete and continuous methods to visualize and understand internal features of large language models.
LLM fine-tuning
New research introduces proxy methods that preserve gradient influence signals while dramatically reducing computational costs for selecting optimal training data in large language model fine-tuning.
LLM
New research applies software product line variability modeling to systematically optimize LLM inference hyperparameters like temperature and sampling strategies.
AI Security
IARPA's TrojAI program releases final report on detecting trojan attacks in AI systems, covering image classifiers, NLP models, and reinforcement learning with implications for synthetic media security.