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Explains how position bias, verbosity bias, and sycophancy distort LLM evaluation. Measure swap consistency, detect length effects.
How RETRO trains language models with retrieval from scratch, using chunked cross-attention to integrate a 2T-token database and cut parameter needs 25x.
Build LLM-as-Judge evaluation pipelines: prompt design, judge model selection, calibration against human annotations, and bias mitigation.
Process reward models score individual reasoning steps instead of final answers alone. Covers training data, credit assignment, math tasks, and limitations.
Explains how Constitutional AI trains safer LLMs using constitutional principles, AI-driven critique and revision, and RLAIF preference labeling.
Examines o1-style reasoning models, test-time compute scaling, process reward models, and open research questions shaping the frontier of AI reasoning.
Examines systematic reasoning failures in LLMs including spurious correlations, reasoning shortcuts, negation failures.
Explains how LLMs solve math problems, from grade-school word problems to competition math. Topics include chain-of-thought, process reward models, GRPO.
Explains how process reward models score each reasoning step, how verification-guided search selects correct chains.
Explains how self-consistency, tree of thought, least-to-most prompting, and decomposition strategies improve language model reasoning accuracy and reliability.
Examines the MBPP benchmark for Python code generation evaluation. Topics include crowd-sourced programming tasks, pass@k metrics, execution-based testing.
Explains how chain-of-thought prompting enables language models to reason step by step. Topics include few-shot CoT, zero-shot CoT, self-consistency.
Explains how HumanEval evaluates LLM code generation using functional correctness and the pass@k metric.
Examines LLM text generation for content creation, writing assistance, and code. Topics include quality dimensions, constraint verification, prompt design.
Examines deductive, inductive, abductive, and causal reasoning in LLMs, including how transformers support inference chains and where reasoning breaks down.
Build intelligent dialogue systems with LLMs, covering conversation management, slot filling, memory strategies, and chatbot evaluation techniques.
Apply model merging to combine task fine-tunes, blend styles, compose capabilities, and evaluate merged models using normalized scores and Pareto analysis.
Explains how structured pruning removes entire attention heads, transformer layers, and feed-forward neurons to create smaller, faster models.
Explains how weight pruning reduces neural network size by removing redundant parameters.
Build multi-layer guardrail systems for LLM applications, covering input validation, output safety, PII detection, topic enforcement.
Explains how differential privacy protects training data in language models, from the mathematical guarantee to DP-SGD, privacy budgets.
Explains how knowledge distillation transfers a large teacher model's learned knowledge to a smaller student through soft label supervision.
Measure and communicate LLM confidence through calibration, verbalized uncertainty, semantic entropy, and trust-calibrated user interfaces.
Covers continual learning evaluation with forward transfer, backward transfer, forgetting metrics.
Examines architecture-based continual learning: progressive networks, PackNet, HAT masks, PathNet.
Explains how experience replay buffers, pseudo-rehearsal, and generative replay prevent catastrophic forgetting in continual learning systems.
Explains how transformer models power sentiment analysis, topic classification, and intent detection.
Explains how classification-based and rule-based content filters protect language model deployments.
Explains how Elastic Weight Consolidation and Synaptic Intelligence protect critical parameters to prevent catastrophic forgetting in continual learning.
Design reliable LLM evaluations with representative test sets, suitable metrics, and controls for contamination, overfitting, and misleading averages.
Covers the continual learning problem, why neural networks catastrophically forget sequential tasks, and the three canonical learning scenarios.
Explains how sparse autoencoders decompose language model activations into interpretable features using overcomplete dictionaries, sparsity constraints.
Explains how language models link generated claims to source documents, evaluate citation accuracy with NLI, and measure attribution precision and recall.
Covers LLM cost management with token-based cost modeling, optimization techniques like caching and quantization, cost allocation.
Monitor LLM output quality in production: track BLEU, BERTScore, and semantic metrics, detect drift with KS tests and CUSUM, catch regressions with A/B testing.
Monitor production LLM systems with metrics collection, structured logging, dashboard design, alerting rules, and concept drift detection.
Explains how the logit lens projects transformer hidden states into vocabulary space to reveal how predictions evolve.
LLaVA connects a frozen vision encoder to a language model through a projection layer. Covers visual instruction tuning, architecture, data, and evaluation.
Extract attention weights from transformer models, visualize head patterns, measure head entropy, and understand the key caveats.
Covers LLM caching strategies including prompt caching with KV reuse, semantic caching with embedding similarity, cache invalidation policies.
Route LLM requests intelligently across model tiers using rule-based, difficulty-based, and cascade strategies, plus A/B testing to validate routing decisions.
Auto-scale LLM serving infrastructure using GPU utilization, queue depth, and TTFT metrics. Topics include horizontal vs vertical scaling.
Covers LLM throughput optimization with continuous batching, batch size tuning, parallelism strategies, and measuring tokens-per-second under real workloads.
Covers LLM serving latency from TTFT to TPOT. Topics include KV cache management, continuous batching, speculative decoding, streaming responses.
Serve language models in production: weight loading strategies, tensor parallelism, request batching, rate limiting.
Evaluate AI agents with task completion metrics, trajectory analysis, and safety testing. Topics include WebArena, SWE-bench, GAIA, and OSWorld benchmarks.
Explains how AI agents manage short-term context and long-term memory. Examines vector databases, retrieval algorithms.
Explains how layer probing maps linguistic properties across BERT layers, from surface features in lower layers to semantic content in upper layers.
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