V2 Fewfeed [best]

The remainder of the paper is organized as follows. Section 2 reviews related work. Section 3 outlines the system architecture. Section 4 details the v2 feature set. Section 5 presents a usage guide with end‑to‑end examples. Section 6 reports experimental results on three benchmark suites. Section 7 discusses best practices and limitations. Section 8 concludes and points to future work.

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| Feature | Description | API Highlights | Benefits | |---|---|---|---| | | Supports nested prompts ( parent → child → leaf ) with inheritance of slots and formatting. | PromptSpec.load(yaml_path) , PromptNode.add_child() | Reuse common scaffolding (e.g., “You are a helpful assistant”) across tasks. | | Dynamic Curriculum Learning | Two modes: offline (pre‑computed difficulty) and online (model‑in‑the‑loop). | CurriculumScheduler(mode="online") | Faster convergence, mitigates catastrophic forgetting. | | Plug‑in Data Providers | Uniform DataProvider base class for custom generators (LLM‑based, rule‑based, crowd‑sourced). | @register_provider(name="self_instruct") | Extensible to any synthetic data pipeline. | | Multimodal Slot Types | Slots can now hold Image , Audio , Video , or Tensor objects. | slot_type: image , image_path: "img_url" | Enables few‑shot vision‑language, audio‑language tasks. | | Versioned Prompt Repositories | Built‑in Git‑compatible storage for prompt specifications. | PromptRepo.clone(url, rev="v2.1") | Reproducibility across experiments. | | Telemetry & Logging | Structured JSON logs of prompt rendering, difficulty scores, and model latency. | Telemetry.enable(level="info") | Easier debugging and audit. | | CLI & Web UI | fewfeed CLI for quick prototyping; optional Flask‑based UI for visual curriculum inspection. | fewfeed run --config cfg.yaml | Lowers entry barrier for non‑programmers. | The remainder of the paper is organized as follows

| Module | Responsibility | Key Interfaces | |---|---|---| | | Parses hierarchical prompts, resolves placeholders (slots) with concrete examples, and assembles final prompts for a given model. | PromptEngine.load(prompt_spec) , PromptEngine.render(example_set) | | CurriculumScheduler | Estimates example difficulty (via model confidence, entropy, or external heuristics) and yields a curriculum —a sequence of example subsets. | CurriculumScheduler.fit(example_pool) , CurriculumScheduler.iterate() | | DataSource (abstract) | Provides a uniform iterator over raw examples. Sub‑classes implement file, DB, API, or synthetic generation logic. | DataSource.read() | | ModelAdapter (abstract) | Normalizes model‑specific API calls (completion, embedding, multimodal tokenization). | ModelAdapter.infer(prompt) | | SlotResolver | Binds concrete data (e.g., "input" ) to prompt placeholders, supports conditional logic and transformations. | SlotResolver.fill(slot_map) | Section 4 details the v2 feature set