Investopedia Finance Instruction Tuning Dataset for Embedding Models

A dataset curated from Investopedia using a technique that scrapes unstructured data and employs an LLM to generate structured question-answer pairs. The dataset generation includes a self-verification method intended to reduce the probability of LLM hallucinations. The dataset was created by FinLang and was last updated on 2024-05-06.

Use Cases

• Fine-tuning embedding models based on structured financial question-answer pairs.
• Training instruction-following models for finance based on the described QA structure.
• Evaluating LLM self-verification techniques for data generation in the finance domain.

Strengths

• Dataset generation uses a self-verification method intended to reduce LLM hallucinations.
• Data is structured specifically for fine-tuning embedding models.
• Source material is from the authoritative finance website Investopedia.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Investopedia

Collection Method

Unstructured scraping combined with LLM generation and self-verification.

Time Range

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Freshness

Last updated 2024-05-06 08:56:20.

Geography

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