FactGroundEval is a research repository for evaluating factual grounding and entity-level consistency in multimodal AI outputs.
Modern image-text and multimodal generation systems can produce fluent responses even when the content is only weakly supported by the available evidence. In many applications, that is the real problem: not whether the output sounds natural, but whether it is actually grounded. FactGroundEval is built around that distinction.
The repository focuses on evaluation settings where factual support matters. That includes cases where a model names the wrong entity, introduces details not justified by the image, mixes visual evidence with unsupported assumptions, or produces outputs that are semantically plausible but still factually unreliable.
FactGroundEval is designed around questions such as:
- Is the generated text supported by the image and context?
- Are people, objects, or locations identified correctly?
- Can unsupported details be separated from ordinary fluency errors?
- How should entity-level mistakes be measured?
- What kinds of evaluation tasks are useful for grounded multimodal systems?
These questions are related, but they are not identical. A repository that treats all of them as one generic caption-quality problem usually loses the distinction that matters most for factual reliability.
The current scope of FactGroundEval includes:
- multimodal factual grounding
- entity-level verification
- mismatch analysis between images and generated text
- unsupported claim analysis
- failure-mode categorization for image-text systems
- evaluation tasks for grounded multimodal generation
The emphasis is intentionally evaluative rather than purely generative. This repository is more concerned with measuring and analyzing reliability than with packaging a single end-to-end model.
FactGroundEval is being developed as an evaluation-first workspace. Depending on the stage of the project, the repository may include:
- scripts for factual consistency checking
- task definitions for grounded image-text assessment
- examples of entity-level mismatch cases
- baseline scoring or comparison utilities
- documentation for evaluation criteria
- notes on common failure categories
The repository is meant to help researchers inspect why a system failed, not only whether it failed. That makes it more useful for error analysis, iterative model improvement, and evaluation design.
FactGroundEval should be understood as an active research project with a concrete and usable scope. The core direction is already established, but the evaluation framework is still being expanded through additional tasks, metrics, and examples.
In practice, this means the repository already serves a real research purpose while remaining open to continued refinement.
FactGroundEval may be useful if you are working on:
- multimodal generation
- grounded captioning or grounded description tasks
- factual verification for image-text systems
- entity-aware evaluation
- trustworthy AI evaluation in multimodal settings
The project favors interpretable evaluation over opaque scoring. When possible, the repository is organized to make factual failures visible and categorizable, rather than collapsing them into a single summary number.