Jinge Wu, Yunsoo Kim, Honghan Wu
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| knowlab/halt-medvqa | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_chunk | Ran | knowlab/halt-medvqa/model_vqa_halt.py pointer only (licence: NONE) · get_code("42a46570620cd9fa") |
| split_list | Ran | knowlab/halt-medvqa/model_vqa_halt.py pointer only (licence: NONE) · get_code("076c252c52cbb161") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare. However, these models are not extensively tested on the hallucination phenomenon in clinical settings. Here, we created a hallucination benchmark of medical images paired with question-answer sets and conducted a comprehensive evaluation of the state-of-the-art models. The study provides an in-depth analysis of current models' limitations and reveals the effectiveness of various prompting strategies.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.05827")
get_code_for_paper("2401.05827")
have("2401.05827")
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