We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| fan19-hub/LEMMA | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| parse_json_markdown | Ran | fan19-hub/LEMMA/lemma.py code served (permissive licence) · get_code("b7bee86c6df7511e") |
| process_multilines_output | Ran | fan19-hub/LEMMA/utils.py code served (permissive licence) · get_code("b7df04acd2e76553") |
| source_filter | Ran | fan19-hub/LEMMA/retrieval.py code served (permissive licence) · get_code("c0ab3c66ce854513") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The rise of multimodal misinformation on social platforms poses significant challenges for individuals and societies. Its increased credibility and broader impact compared to textual misinformation make detection complex, requiring robust reasoning across diverse media types and profound knowledge for accurate verification. The emergence of Large Vision Language Model (LVLM) offers a potential solution to this problem. Leveraging their proficiency in processing visual and textual information, LVLM demonstrates promising capabilities in recognizing complex information and exhibiting strong reasoning skills. In this paper, we first investigate the potential of LVLM on multimodal misinformation detection. We find that even though LVLM has a superior performance compared to LLMs, its profound reasoning may present limited power with a lack of evidence. Based on these observations, we propose LEMMA: LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation. LEMMA leverages LVLM intuition and reasoning capabilities while augmenting them with external knowledge to enhance the accuracy of misinformation detection. Our method improves the accuracy over the top baseline LVLM by 7% and 13% on Twitter and Fakeddit datasets respectively.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.11943")
get_code_for_paper("2402.11943")
have("2402.11943")
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