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Symbolic Correlation Dataset for LLMs

Exploring the Relationship between Knowledge and Language

By FBL (From Huggingface) [source]

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About this dataset

The fblgit/tree-of-knowledge dataset is a comprehensive and valuable resource designed specifically for investigating the intricate relationship between knowledge and language within Large Language Models (LLMs). This dataset has been meticulously curated to facilitate the exploration of symbolic correlation by providing a wide range of input prompts, corresponding instructions, and output prompts.

At its core, the dataset comprises a CSV file titled train.csv that houses an abundance of information across three distinct columns: instruction, input, and output. The instruction column serves as a set of guidelines or directives given to the language model, specifying the desired task or prompt. On the other hand, the input column contains specific textual data supplied as input to the language model based on these instructions. Finally, the output column consolidates and showcases the generated responses produced by these LLMs in direct response to both instructions and inputs provided.

With this wealth of data at your disposal, researchers and practitioners can delve deep into understanding how knowledge is internalized within these powerful language models. By examining how LLMs leverage their vast linguistic capabilities to correlate with various types of structured information embodied in both instruction and input prompts, it becomes possible to unravel intricate patterns between knowledge representation and expression through natural language generation.

By presenting an extensive collection of diversified instructional contexts coupled with corresponding inputs and outputs in an organized format, researchers can effectively analyze how different LLM architectures process information within varying knowledge domains. Consequently, this exceptional resource fundamentally contributes towards advancing our insights into harnessing these transformative models for richer human-machine interactions while empowering further advancements in natural language processing research

How to use the dataset

How to use this dataset for Symbolic Correlation in LLMs

Understanding the Dataset

The dataset is stored in a CSV file titled train.csv and contains three columns: instruction, input, and output. Each column serves a specific purpose:

• Instruction: This column contains the instructions or prompts given to the language model. It provides guidance on what kind of information or task is expected from the model.

• Input: This column contains the input data provided to the language model based on the given instruction. It can include text, symbols, or any other format that represents data relevant to the prompt.

• Output: This column contains the output generated by the language model in response to the given instruction and input. It represents what the model produces as its response.

Exploring Symbolic Correlation

To explore symbolic correlation in LLMs using this dataset, follow these steps:

• Read each row of train.csv sequentially.
• Analyze each instruction with its corresponding input and output.
• Consider how well LLMs are able to understand and generate accurate responses based on different levels of knowledge represented by symbolic correlations.
• Observe how LLMs handle various types of prompts related to symbolism and correlation.
• Investigate whether LLMs can effectively utilize prior knowledge through their generated outputs.

Learning From Examples

To gain insights from this dataset, examine specific examples within it:

• Select an instruction that interests you.
• Review both its corresponding input prompt and generated output response from an LLM.
• Reflect upon how well symbolic correlation has been established between inputs and outputs across different examples.
• Evaluate whether subtle changes in input prompts lead to notable differences in LLM-generated outputs.

Expanding the Dataset

This dataset can be expanded and enriched by incorporating different types of prompts and responses related to symbolic correlation in LLMs. By adding more diverse examples, you can explore the capabilities and limitations of LLMs when it comes to understanding and harnessing knowledge.

Sharing Your Findings

As you dive into this dataset, feel free to share your findings, observations, experiments, or any insights you gather. By leveraging community knowledge and collaboration on platforms like Kaggle, we can collectively deepen our understanding of symbolic correlation in LLMs.

Remember: This dataset is a valuable resource for exploring

Research Ideas

• Fine-tuning language models: This dataset can be used to fine-tune existing language models by training them on the provided instructions, input prompts, and corresponding output prompts. This can help improve the performance of the models in generating accurate and relevant responses.
• Studying knowledge representation: Researchers can use this dataset to study how large language models represent and correlate knowledge with language. By analyzing the generated outputs for different inputs and instructions, insights can be gained into how these models comprehend and encode different types of information.
• Evaluating model capabilities: The dataset can also be utilized for evaluating the capabilities of large language models in understanding complex instructions and generating appropriate responses. By comparing the generated outputs with human-provided outputs, researchers can assess how well these models perform in various tasks requiring knowledge inference and reasoning

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit FBL (From Huggingface).