README.md

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
multilinguality:
- monolingual
tags:
- nlg
- generation
- drone
- data-to-text
- agent
pretty_name: drone
size_categories:
- n<1K

configs:
- config_name: default
  data_files:
    - split: train
      path: "all_train.csv"
    - split: val
      path: "all_val.csv"
    - split: test
      path: "all_test.csv"
  default: true
---


# Dataset Card for **Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (RAMP)**

**[Hugging Face Dataset](https://huggingface.co/datasets/tonyhong/ramp)** | **[GitHub Repository](https://github.com/tony-hong/ramp)** | **[paper](https://aclanthology.org/2024.lrec-main.1224v2.pdf)** | **[Gitlab Repository](https://gitlab.com/forfrt/drone/-/tree/main?ref_type=heads)** 

<!-- Provide a quick summary of the dataset. -->

RAMP provides a prepared version of a low-resource **data-to-text** corpus for **drone handover message generation**: structured sensor records (status + time-step object lists) paired with natural-language “handover” messages describing critical situations. The release includes raw/filtered splits and domain-specific subsets (e.g., *urban, rural, ocean, desert, island, factory, disturbance, misc*), suitable for training and evaluating retrieval-augmented and prompt-tuned models.

---

## Dataset Details

### Dataset Links

- **Paper (LREC-COLING 2024):** *Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation*
- **Code & Data:** GitHub repo (experiments)
- **HF Dataset:** `tonyhong/ramp` (CSV files with train/val/test + subsets)

### Dataset Description

The dataset targets **low-resource data-to-text (D2T)** generation where models verbalize *structured* inputs into faithful messages. Instances pair:

- **Input:** a drone **status** dictionary (e.g., wind speed, battery level, altitude, pilot experience, etc.) and a time-ordered list of **time-step objects** near the flight path (type, distance, moving/in-path flags, timestamps).
- **Output:** a **handover message** (English) that surfaces only *critical* information (e.g., “Risk of physical damage! There is a castle in the drone’s flight path at a distance of 2.5 m.”)

The RAMP paper reports a **low-resource** setup with **1.6K data points** (input–output pairs). Inputs average **541 tokens** (range 274–2481), and outputs average **149 tokens** (range 29–1263), reflecting long, information-dense inputs common in real-time settings. The dataset is organized to support **retrieval-augmented** few-shot prompting and **modular prompt-tuning**.

- **Curated by:** Ruitao Feng, Xudong Hong, Mayank Jobanputra, Mattes Warning, Vera Demberg  
- **Language(s) (NLP):** English  
- **License:** Apache License 2.0

> **Provenance:** The content ultimately derives from a drone sensor/utterance corpus introduced by Chang et al. (LREC 2022). RAMP repackages/extends the resource with splits, filtered variants, and files that support retrieval-augmented and modular-prompt workflows.

---

## Dataset Structure

The dataset is distributed as **CSV** files. You’ll find:

- **Top-level splits**
  - `all_raw_train.csv`, `all_raw_val.csv`, `all_raw_test.csv`
  - Filtered counterparts: `*_filtered_with_oneshot.csv`
- **Domain subsets** (each with `train/val/test`): `urban_*`, `rural_*`, `ocean_*`, `desert_*`, `island_*`, `factory_*`, `disturbance_*`, `misc_*`
- **Auxiliary files:** e.g., `DroneDataset_keywords_paraphrase_latest - Sheet1.csv` (keywords/paraphrases), and compact “drone_v*” CSVs for minimal examples.

### Data Fields (columns)

> Field names below reflect the `all_*` CSVs; JSON is provided as strings.

- **`summary`** *(string)* — The handover message text. Often contains multiple segments with inline timestamps separated by `[SEP]`.
- **`status`** *(JSON as string)* — A single time-invariant status dict for the 10-s snapshot (e.g., wind speed, battery level, altitude, pilot experience, criticality flags).
- **`timestep`** *(JSON as string)* — A list of detected objects per second with attributes: `name`, `Type`, `Moving`, `InPath`, `Distance`, `time_stamp`, `ID_obj`.
- **`related_status`** *(JSON as string)* — A *reduced* set of status attributes most relevant to the handover (critical attributes).
- **`related_timestep`** *(JSON as string)* — A *reduced* set of time-step object info relevant to the handover.
- **`related_sensor_data`** *(JSON as string)* — Bundles `status` + `timestep` for convenience (subsetted to relevant parts).
- **`templates`** *(string)* — Template-like text variants used for retrieval/one-shot prompting (if present).
- **`link`** *(string URL)* — Pointer to a short video snapshot (Google Drive) illustrating the scenario (may be unavailable/archived).
- **`source`** *(string/int)* — Internal identifier/index for traceability.

> Notes: Some CSVs include long JSON strings; use robust CSV readers (`quotechar` and `escapechar` set appropriately). Filtered files remove noisy rows and provide a consistent one-shot example alongside each item for RAMP-style prompting.

### Splits

- **Train/Validation/Test:** Provided explicitly (`all_raw_*`).  
- **Environment-specific splits:** Each environment (e.g., `urban_test.csv`) mirrors the global schema and supports domain generalization studies.

---

## Uses

### Direct Use

- **Data-to-Text Generation:** Train/evaluate models (T5/Flan-T5/LED/others) on long, structured inputs to generate faithful handover messages.
- **Retrieval-Augmented Prompting:** Use the *filtered_with_oneshot* files or the `templates`/`related_*` columns to build **RAG-style** prompts (attribute-similar examples).
- **Hallucination Analysis:** Evaluate faithfulness via metrics referencing both input and output (e.g., PARENT).  
- **Domain Generalization:** Use the environment splits to test seen/unseen domain transfer.

### Out-of-Scope Use

- **Operational decision-making for real drones:** This resource is **research-only**; do not deploy generated text for safety-critical control.  
- **Privacy-sensitive analytics:** No personal data is included; it is not intended for identifying individuals or locations.

---

## Dataset Creation

### Curation Rationale

RAMP packages a **low-resource** D2T task that stresses **faithfulness** under long, structured inputs. The files facilitate **retrieval-augmented** few-shot prompting and **modular prompt tuning** (attribute-aware routing) to reduce hallucinations.

### Source Data

- **Origin:** Drone sensor/utterance corpus introduced by Chang et al. (LREC 2022), comprising 10-s snapshots across **8 environments** (*disturbance, urban, rural, ocean, desert, island, factory, misc*) with paired handover messages.
- **Attributes:** ~**25** status/scene attributes (e.g., altitude, drone speed, battery level, visibility) plus per-second object lists (type, distance, moving/in-path).

### Data Collection and Processing

- **Status & Time-step Extraction:** Manually annotated status + object lists per video snapshot (1 Hz).  
- **Criticality Mapping:** Description Logic (DL) rules/expressions identify **critical** attribute-value pairs; these appear in `related_status`/`related_timestep`.  
- **Preprocessing for RAMP:** CSV packaging, filtered variants, and prompts/templates to support **retrieval** of attribute-similar examples and **modular** prompt routing.  
- **Statistics (RAMP setup):** Inputs avg ~**540.8** tokens; outputs avg ~**148.5** tokens; total ~**1.6k** pairs.

### Who are the source data producers?

- **Videos & Sensor Records:** Collected/curated by the original drone dataset authors (Chang et al., 2022).  
- **Handover Messages:** Authored by the original dataset annotators; RAMP includes them verbatim plus paraphrase/templates where indicated.

---

## Annotations

### Annotation process

The **original** dataset includes human-authored handover messages and DL-based content selection cues. RAMP adds no new manual labels; it surfaces **relevant subsets** (`related_*`) and templated examples to support retrieval-augmented prompting. See the paper for details.

### Who are the annotators?

Original dataset annotators (per Chang et al., 2022). RAMP curators: the RAMP paper authors.

---

## Personal and Sensitive Information

No personal or sensitive information is included. Links may point to scenario videos of environments/objects without identifiable persons. No worker IDs or personal metadata are included.

---

## Bias, Risks, and Limitations

- **Domain specificity:** Drone scenarios; transfer to unrelated domains may be limited.  
- **Language:** English-only messages.  
- **Long inputs:** Models with short context windows can truncate inputs; use long-context architectures (e.g., LED) or careful chunking.  
- **Hallucinations:** Despite DL cues and retrieval, faithful grounding is non-trivial—evaluate with input-aware metrics and human review.  
- **Licensing of linked media:** Some `link` URLs point to externally hosted videos; availability and terms may vary.

---

## How to Load

```python
from datasets import load_dataset

ds = load_dataset("tonyhong/ramp")
train = ds["train"]            # or use config/splits as hosted

from datasets import load_dataset

ds = load_dataset("tonyhong/ramp")
train = ds["train"]            # or use config/splits as hosted

# Tip: If the viewer/loader errors on CSV quoting, download locally and load
# with a robust parser (e.g., pandas with engine="python" and proper
# quotechar/escapechar).

```


## Citation

**RAMP paper (LREC-COLING 2024)**  
Ruitao Feng, Xudong Hong, Mayank Jobanputra, Mattes Warning, and Vera Demberg. 2024. *Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation.*

**Upstream dataset (LREC 2022)**  
Ernie Chang, Alisa Kovtunova, Stefan Borgwardt, Vera Demberg, Kathryn Chapman, and Hui-Syuan Yeh. 2022. *Logic-Guided Message Generation from Raw Real-Time Sensor Data.*

```bibtex
@inproceedings{feng2024ramp,
  title={Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation},
  author={Feng, Ruitao and Hong, Xudong and Jobanputra, Mayank and Warning, Mattes and Demberg, Vera},
  booktitle={Proceedings of LREC-COLING 2024},
  year={2024}
}

@inproceedings{chang2022drone,
  title={Logic-Guided Message Generation from Raw Real-Time Sensor Data},
  author={Chang, Ernie and Kovtunova, Alisa and Borgwardt, Stefan and Demberg, Vera and Chapman, Kathryn and Yeh, Hui-Syuan},
  booktitle={Proceedings of LREC 2022},
  pages={6899--6908},
  year={2022}
}
```


## Dataset Card Authors

Xudong Hong (maintainer); with contributions from Ruitao Feng, Mayank Jobanputra, Mattes Warning, Vera Demberg.

## Dataset Card Contact

[email protected]

---

## Disclaimer

RAMP repackages data originating from a drone sensor/utterance corpus. The CSVs may contain long JSON strings; handle parsing carefully. Linked videos are provided for academic/research use; availability is not guaranteed. **Do not** use this dataset to operate real drones or for any safety-critical decision making.