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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. |