shikiw/OPERA
OPERA is a technique to reduce hallucinations in multimodal large language models by penalizing over-trust and allocating retrospection during inference.
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OPERA addresses the hallucination problem in multimodal LLMs through two novel mechanisms: an over-trust penalty that discourages excessive reliance on language model priors, and a retrospection-allocation strategy that enables the model to review and verify its visual commitments. The method is training-free and can be applied during inference to existing MLLMs without additional fine-tuning. It achieves state-of-the-art results on hallucination benchmarks.
Frequently asked
- What is shikiw/OPERA?
- OPERA is a technique to reduce hallucinations in multimodal large language models by penalizing over-trust and allocating retrospection during inference.
- Is OPERA open source?
- Yes — shikiw/OPERA is open source, released under the MIT license.
- What language is OPERA written in?
- shikiw/OPERA is primarily written in Python.
- How popular is OPERA?
- shikiw/OPERA has 412 stars on GitHub.
- Where can I find OPERA?
- shikiw/OPERA is on GitHub at https://github.com/shikiw/OPERA.