Datasets:
Tasks:
Feature Extraction
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
code
License:
File size: 2,129 Bytes
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---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 916486
num_examples: 10000
download_size: 164700
dataset_size: 916486
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
tags:
- code
pretty_name: relative-positioning
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
This dataset aims to teach LLMs relative positioning (e.g. above, left from, below, etc.),
which in my findings most LLMs, even SOTA where not able to produce under all circumstances.
Will be pushing a fine-tuned mixtral-7x8B with this dataset.
## Dataset Details
### Dataset Description
Contains Data for relative positioning on a grid(256, 256).
Assumes Origin [0, 0] is in the bottom left.
Two Objects (Object 1, Object 2) are randomly created.
Answer is there relative position to one another.
- **Curated by:** [Antoine Angert]
- **Language(s) (NLP):** [English]
- **License:** [apache-2.0]
## Uses
### Direct Use
Can be used to fine-tune Language Models.
(Althought so far not been tested, will update)
## Dataset Structure
Features:
Prompt(String), Response(String)
## Dataset Creation
### Curation Rationale
I did some testing to see how well LLMs are able to handle positional data(2D, 3D).
I found that most small models (tested: llama-7B, llama-13B, mistral-7B) have very poor positional understanding.
Most bigger Models (tested: gpt-3.5-turbo, gpt-4, llama-70B, mixtral-7x8B) have a fairly good positional understanding, as long as no other context is provided.
When I tried using positional reasoning with some other unrelated context, the performance of these bigger models dropped imensly.
This is my first attempt of trying to embed this understanding directly into the models and not throught context.
#### Data Collection and Processing
The dataset was generated using a python script.
## Dataset Card Authors [optional]
Antoine Angert
## Dataset Card Contact
Contact under:
[email protected] |