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README.md

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manual
├── constituency_tests.json
├── human_annotations.jsonl
└── schema.json

  • schema.json contains the schema that informs the definition of the different concepts in a scientific article.

    This schema was developed by the project's authors through a few iterations of discussion.

    The schema was:

    • Used to guide human annotators in labeling a subset of raw data
    • passed to the language model with instructions for annotating the raw data during in-context learning

    Defining the schema ensures consistency and standardization in identifying and categorizing scientific concepts for both humans and the language model. It provides a common framework for comparing and aggregating data across multiple papers.

  • constituency_tests.json contains the tests for constituency that were defined for clarifying any follow-up questions one may have about the schema and the information extraction/tagging process.

    The constituency tests were also developed by the project's authors and is useful for:

    • Ambiguity resolution during the manual annotations phase: We may encounter concepts that may be categorized as more than tag based on only the schema definitions
    • Ambiguity resolution for the model while generating tagged concepts: Though not utilized for now, it can potentially be used as a chain-of-thought or reasoning tool and passed to the language model along with instructions in order to have refined, less ambiguous tag predictions

    It is currently mainly used as a reference points for manual annotators when they encounter ambiguity.

  • human_annotations.jsonl contains 20 manually annotated paper titles and abstracts from the astro-ph arXiv channel stored in json lines.

    • They contain annotations done by the project's authors based on the defined schema. Constituency tests help in resolving ambiguity.
    • The annotations are used for prompt optimization by dividing it into train and dev sets:
      • the first 3 papers are used as the train set for few-shot examples and the rest are used as the dev set and passed to the language model for generating tags
      • across different sweeps of hyperparameters on the train set prefixes, the precision and recall of the predicted and grounth truth tags in the dev set are measured to identify the best hyperparameters
    • These hyperparameters are then applied to the prefixes from the train set when generating tags for the test set--which is a large raw dataset from arXiv.