InstructionFollowing

Instructionfollowing is the capability of a system to interpret and execute user-provided instructions as intended. In AI and automation, it involves understanding natural language requests, decomposing tasks when needed, choosing appropriate steps or tools, and delivering results that conform to stated constraints or preferences. It spans language models, autonomous agents, robotics, and software automation, and depends on clear prompts, sufficient context, and mechanisms to handle ambiguity.

Approaches to improve instruction following include instruction tuning, where models are trained on datasets framed as instructions with demonstrations of desired outputs; reinforcement learning from human feedback to align responses with user intent; and prompt engineering to structure inputs in ways that reduce misinterpretation. Additional methods emphasize task decomposition, tool use and planning, and safety layers that allow refusals or safe redirections for disallowed requests. Robustness techniques address ambiguity, varying phrasing, and context length limitations.

Evaluation of instruction following uses benchmarks that measure accuracy in adhering to instructions, fidelity of outputs to constraints, and safety or policy compliance. Metrics may assess adherence to formatting, factual correctness, and resilience to prompt variations or adversarial prompts. Datasets often present a wide range of tasks from classification and reasoning to data manipulation and coding.

Applications and challenges include virtual assistants, customer support, code generation, data analysis, and automation workflows. While instruction following enables more capable and user-friendly systems, it faces limitations such as misinterpretation of ambiguous prompts, hallucinations, and vulnerability to prompt injection or goal drift. Ongoing work focuses on alignment, safe behavior, and transparent reporting of model capabilities and boundaries.