evidentiality_qa
Evidentiality-guided Generator for Knowledge-Intensive NLP Tasks
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Evidentiality_qa is a Python-based model that integrates evidentiality into training for knowledge-intensive NLP tasks, such as open question answering, fact verification, and knowledge-enhanced dialogue, using multi-tas
Good fit when
- Use evidentiality_qa when you need to improve performance on knowledge-intensive NLP tasks that require the model to generate answers based on evidence from retrieved passages.
- Consider evidentiality_qa for tasks where the quality of evidence supporting the generated output is critical, such as in fact verification or knowledge-enhanced dialogue systems.
Avoid when
- Avoid using evidentiality_qa if your task does not involve knowledge-intensive NLP tasks that require evidence-based reasoning.
- Do not use evidentiality_qa if you lack the necessary silver evidentiality data for training, as the model relies on this data to predict evidentiality and generate answers.
- Requirements:
- Min 8 GB RAM; The model requires access to silver evidentiality data for training.; Training involves a multi-step process including training a base Fusion-in-Decoder model and a separate evidentiality labeling model.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
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- As of Sep 18, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
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Install
pip install evidentiality_qaSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Implementation of a model that integrates evidentiality into training for knowledge-intensive NLP tasks, including open question answering, fact verification, and knowledge-enhanced dialogue.
Capability facts
- Languages
- python
Source: github.language · Sep 18, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
CUDA_VISIBLE_DEVICES=0 python test_reader.py \Source link
Tags
README
Evidentiality guided Generator This is the official implementation of the following paper: Akari Asai, Matt Gardner and Hannaneh Hajishirzi. Evidentiality guided Generation for Knowledge Intensive NLP Tasks. In Proc. NAACL. 2021. In this paper, we introduce Evidentiality guided G...
For agents
This page has a .md twin and JSON over the API.