Model Type | text-generation, abstractive proposition segmentation |
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Use Cases |
Areas: | |
Applications: | abstractive proposition segmentation, claim extraction |
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Primary Use Cases: | grounding, retrieval, fact-checking |
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Limitations: | English only, not suitable for other languages or tasks |
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Considerations: | Guidelines for responsible use. |
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Additional Notes | Model trained on synthetically generated data and certain guards to ensure bias, safety considerations. |
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Supported Languages | English (trained for abstractive proposition segmentation) |
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Training Details |
Data Sources: | training data contains synthetically generated examples, ROSE dataset |
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Methodology: | few-shot prompting with Gemini Ultra, propositions list generated by a teacher LLM |
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Context Length: | |
Hardware Used: | |
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Safety Evaluation |
Methodologies: | Evaluation on multi-domain datasets, axis evaluation for abstractive proposition segmentation |
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Risk Categories: | |
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Responsible Ai Considerations |
Fairness: | bias mitigation guidelines provided, continuous monitoring encouraged |
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Transparency: | details summarized in model card |
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Accountability: | developers are responsible for adhering to guidelines and regulations |
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Mitigation Strategies: | Guidelines for content safety, educational resources for misuse mitigation. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | List of propositions grouped per sentence |
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