Skip to content

Latest commit

 

History

History
104 lines (68 loc) · 3.42 KB

README.md

File metadata and controls

104 lines (68 loc) · 3.42 KB
Raw

Synthetic Instruction Generation for Targeted Scientific Fields

This project offers a pipeline for generating diverse, high-quality instructions tailored to specific scientific domains, along with their corresponding question-answer pairs. The pipeline comprises four key steps:

  1. Step 1: Domain Keyword Probability Table Creation
  2. Step 2: Scientific Task Description Collection
  3. Step 3: Instruction Generation
  4. Step 4: Quality Filtering

Step 1: Domain Keyword Probability Table Creation

For each domain (e.g., alloys, biomedicine, materials), collect relevant documents and store them in /reference_papers/<domain_name>. To build a probability table of domain-specific keywords, execute the following script:

python helper/parse_pdfs.py

This will parse the PDFs and generate word_frequency_table.txt for each domain, capturing word-level distributions from domain literature.

Step 2: Scientific Task Description Collection

We provide predefined scientific tasks, including:

  1. Table Extraction
  2. Entity Extraction
  3. Multiple Choice Questions
  4. True-or-False Questions
  5. Molecule Translation
  6. Molecule Extraction

These tasks leverage domain-specific keywords for instruction diversity. Molecule Translation and Molecule Extraction tasks, in particular, use a list of molecular formulas to guide the process.

Step 3: Instruction Generation

To generate instructions for a specific domain, use:

python gen_synthetic_data.py --k <number_of_keywords> --num_samples <number_of_samples> --domain <domain_name> --task <task_name> --save_results --temperature <sampling_temperature>

Parameters:

  • k: Number of keywords to sample (default: 20)
  • num_samples: Number of samples to generate
  • domain: The domain for which instructions are being generated
  • task: Task type (choose from 'table_extraction', 'multiple_choice', 'T_F', 'entity_extraction', 'molGen_wocontext', 'molGen_wcontext')
  • temperature: Sampling temperature (default: 3)

Make sure to configure your Azure endpoints and API keys in gen_synthetic_data.py. Then, as an example, you can run:

python gen_synthetic_data.py --k 20 --num_samples 1 --domain example_domain --task table_extraction --save_results --temperature 3

Results

The generated instructions and question-answer pairs will be saved in the ./results/ folder, following this naming convention:

results/{domain}/{task}_sample{num_samples}_k{k}_t{temperature}_synthetic_data.json

Example content:

[
    {
        "answer": "b) James Wakasa",
        "text": "James Wakasa, an eminent researcher at Brightmanfound, recently published a comprehensive report on the advancements in nanocrystal applications..."
    }
]

Step 4: Quality Filtering

We implement two main methods for quality control: deduplication and LLM-based filtering.

To run deduplication:

python helper/dedup.py

After deduplication, you can score the generated instructions using the LLM-based filtering method:

python helper/infer_sft.py --input_file "deduped_results" --output_file "sft-data-scored.json"

Customizing Tasks

To add new tasks, create a prompt template and few-shot examples similar to prompts/table_extract_prompt.py. Then, implement task-specific generation functions like those in utils/mc_tf_table_entity_gen.py. Finally, update gen_synthetic_data.py to support the new task.