Leaderboard
K-FinHallu
Tests whether an LLM can detect hallucinations in multi-turn Korean financial RAG dialogues. The taxonomy is built on answerability: contradictory, unverifiable, irrelevant, false refusal and false acceptance, with justified abstention counted as correct.
Best reported
0.860
Gemini 2.5 Flash
binary detection F1
28 May 2026
K-FinHallu arXiv v1 (2026-05-28)
binary detection F1, higher is better. 10 models.
Updated 28 May 2026
- Gemini 2.5 Flash0.860
- Gemini 2.5 Pro0.849
- gpt-50.823
- Llama-3.3-70B0.756
- Llama-3.1-70B0.735
- GPT-4o0.733
- Qwen3-32B0.732
- EXAONE-4.0-32B0.728
- Qwen3-14B0.707
- Llama-3.1-8B0.674
K-FinHallu arXiv v1 (2026-05-28)
binary detection F1, higher is better. 10 models.
Updated 28 May 2026
- 1Gemini 2.5 Flash0.860
- 2Gemini 2.5 Pro0.849
- 3gpt-50.823
- 4Llama-3.3-70B0.756
- 5Llama-3.1-70B0.735
- 6GPT-4o0.733
- 7Qwen3-32B0.732
- 8EXAONE-4.0-32B0.728
- 9Qwen3-14B0.707
- 10Llama-3.1-8B0.674
| 1 | Gemini 2.5 Flash | 0.860 | test split | Paper Table 3 Reported by the authors, 28 May 2026 | |
|---|---|---|---|---|---|
| 2 | Gemini 2.5 Pro | 0.849 | test split | Paper Table 3 Reported by the authors, 28 May 2026 | |
| 3 | gpt-5 | OpenAI | 0.823 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 4 | Llama-3.3-70B | Meta | 0.756 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 5 | Llama-3.1-70B | Meta | 0.735 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 6 | GPT-4o | OpenAI | 0.733 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 7 | Qwen3-32B | Qwen | 0.732 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 8 | EXAONE-4.0-32B | LG | 0.728 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 9 | Qwen3-14B | Qwen | 0.707 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |
| 10 | Llama-3.1-8B | Meta | 0.674 | test split | Paper Table 3 Reported by the authors, 28 May 2026 |