LLM system with LoRA fine-tuning and few-shot prompting wins reference-free financial misinformation detection task at 95.4% public and 96.3% private accuracy.
InProceedings of the 33rd ACM Interna- tional Conference on Multimedia, MM ’25, 13874–13880
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models
LLM system with LoRA fine-tuning and few-shot prompting wins reference-free financial misinformation detection task at 95.4% public and 96.3% private accuracy.