| --- |
| language: |
| - en |
| pretty_name: CLIFT |
| size_categories: |
| - 5K<n<10K |
| task_categories: |
| - text-generation |
| tags: |
| - benchmark |
| - evaluation |
| - synthetic |
| - reasoning |
| - in-context-learning |
| - transfer-learning |
| - structured-output |
| --- |
| |
| <div align="center"> |
|
|
| **CLIFT: Contextual Learning across Inference, Format, and Transfer** |
|
|
| A structured benchmark of **5,160** synthetic instances that stress-test whether models **learn latent rules from context**. |
|
|
| [](https://github.com/LongarMD/CLIFT) |
| [](https://huggingface.co/datasets/longarmd/CLIFT) |
|
|
| </div> |
|
|
| --- |
|
|
| ## At a glance |
|
|
| | | | |
| | :--- | :--- | |
| | **Instances** | 5,160 | |
| | **Design** | Full factorial over **task × format × application × difficulty**, with **10** i.i.d. draws per cell | |
| | **Seed** | `42` | |
| | **Language** | English prompts and instructions | |
| | **Modality** | Text (completion-style `prompt` → string `target`) | |
|
|
| ### Load in Python |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("longarmd/CLIFT", split="train") |
| # Each row: prompt, target, task, format, application, difficulty, latent_structure, ... |
| ``` |
|
|
| If the Hub loader is misconfigured, use the **Files** tab JSONL and stream with `json.loads` per line—the schema matches the table below. |
|
|
| --- |
|
|
| ## Why CLIFT? |
|
|
| Modern LMs are often evaluated on fixed formats or single-shot skills. **CLIFT** targets a narrower but critical capability: **contextual learning**—inferring a **hidden structure** from the prompt (examples, traces, or specs), then answering under **controlled variation** along three axes: |
|
|
| 1. **Inference** — *What* must be learned (lookup rules, algorithms, spatial transforms, small dynamical systems, …). |
| 2. **Format** — *How* that knowledge is presented (demonstrations, natural language, execution traces, formal specs). |
| 3. **Transfer / application** — *How* the model must use it (forward prediction, inverse reasoning, articulation, OOD probes, planning, structural probes—**task-dependent** subsets). |
|
|
| Together, these axes yield a **dense evaluation matrix** suited for diagnostics, ablations, and comparing training or prompting strategies—not a single leaderboard score in isolation. |
|
|
| --- |
|
|
| ## Task families |
|
|
| Instances are grouped into **four families** spanning **10** canonical tasks: |
|
|
| | Family | Tasks | |
| | :--- | :--- | |
| | **Functional mappings** | `lookup_table`, `arithmetic_rule`, `conditional_rule` | |
| | **Algorithmic** | `insertion_sort`, `max_subarray`, `binary_search`, `naive_string_matcher` | |
| | **Spatial** | `spatial_translation` | |
| | **Dynamic structures** | `affine_dynamics_2d`, `register_machine_2d` | |
|
|
| **Formats** (all tasks use this set where applicable): `demonstration`, `natural_language`, `trace`, `formal_spec`. |
|
|
| **Difficulty**: integer levels **1**, **2**, **3** (structure complexity scales with level). |
|
|
| **Application** varies by task (e.g. affine/register tasks use dedicated OOD-suffixed probes). The shipped matrix matches the open-source generator defaults in [`clift.common`](https://github.com/LongarMD/CLIFT/blob/main/src/clift/common.py). |
|
|
| --- |
|
|
| ## Dataset structure |
|
|
| Data are distributed as **JSONL**: one JSON object per line, Hugging Face–friendly and line-diffable. A companion **`manifest.json`** (in the source repo) records generator kwargs, expected row count, and a **SHA-256** over the canonical payload for integrity checks. |
|
|
| ### Fields (per instance) |
|
|
| | Field | Type | Description | |
| | :--- | :--- | :--- | |
| | `instance_id` | int | Stable index within the snapshot | |
| | `task` | string | One of the 10 canonical task names | |
| | `format` | string | Presentation format | |
| | `application` | string | Probe / application axis | |
| | `difficulty` | int | 1–3 | |
| | `prompt` | string | Model input (completion-style) | |
| | `target` | string | Reference answer (exact match is the primary check) | |
| | `latent_structure` | object | **Gold structure** for analysis & tooling (not shown to the model) | |
| | `instruct` | bool | Whether instruct/chat-style export was used | |
| | `messages` | array (optional) | OpenAI-style chat turns, when enabled at export | |
| | `metadata` | object (optional) | Extra fields when present | |
|
|
| > **Note:** `latent_structure` is intentionally included for **research and scoring pipelines**. Treat it as **held-out supervision** for training—do not condition generation on it unless your experimental design explicitly allows it. |
