Results for "tool-augmented LLM"

Enables external computation or lookup.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Models trained to decide when to call tools.

Agent calls external tools dynamically.

Central log of AI-related risks.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

The text (and possibly other modalities) given to an LLM to condition its output behavior.

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

Architecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce hallucinations.

Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.

Constraining outputs to retrieved or provided sources, often with citation, to improve factual reliability.

Prompt augmented with retrieved documents.

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Measures a model’s ability to fit random noise; used to bound generalization error.

Updating beliefs about parameters using observed evidence and prior distributions.

Neural networks that operate on graph-structured data by propagating information along edges.

GNN using attention to weight neighbor contributions dynamically.

Formal model linking causal mechanisms and variables.

Interleaving reasoning and tool use.

Sampling-based motion planner.

Ability to correctly detect disease.

Testing AI under actual clinical conditions.

Simulating adverse scenarios.

Maximum expected loss under normal conditions.