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--- |
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dataset_info: |
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features: |
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- name: prompt |
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dtype: string |
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- name: response |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 916486 |
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num_examples: 10000 |
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download_size: 164700 |
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dataset_size: 916486 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: apache-2.0 |
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task_categories: |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: relative-positioning |
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size_categories: |
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- 10K<n<100K |
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--- |
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### NOTE: this is a huggingface duplicate for further processing. Here's the originial readme |
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# Dataset Card for Dataset Name |
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This dataset aims to teach LLMs relative positioning (e.g. above, left from, below, etc.), |
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which in my findings most LLMs, even SOTA where not able to produce under all circumstances. |
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Will be pushing a fine-tuned mixtral-7x8B with this dataset. |
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## Dataset Details |
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### Dataset Description |
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Contains Data for relative positioning on a grid(256, 256). |
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Assumes Origin [0, 0] is in the bottom left. |
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Two Objects (Object 1, Object 2) are randomly created. |
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Answer is there relative position to one another. |
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- **Curated by:** [Antoine Angert] |
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- **Language(s) (NLP):** [English] |
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- **License:** [apache-2.0] |
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## Uses |
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### Direct Use |
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Can be used to fine-tune Language Models. |
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(Althought so far not been tested, will update) |
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## Dataset Structure |
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Features: |
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Prompt(String), Response(String) |
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## Dataset Creation |
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### Curation Rationale |
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I did some testing to see how well LLMs are able to handle positional data(2D, 3D). |
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I found that most small models (tested: llama-7B, llama-13B, mistral-7B) have very poor positional understanding. |
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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. |
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When I tried using positional reasoning with some other unrelated context, the performance of these bigger models dropped imensly. |
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This is my first attempt of trying to embed this understanding directly into the models and not throught context. |
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#### Data Collection and Processing |
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The dataset was generated using a python script. |
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## Dataset Card Authors [optional] |
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Antoine Angert |
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## Dataset Card Contact |
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Contact under: |
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antoine.angert@hsbi.de |
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