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The original was posted on /r/machinelearning by /u/MagnoliaPotato on 2025-01-13 15:16:18+00:00.
Hi Everyone, I recently noticed most LLM observability providers (Arize AI, Galileo AI, LangSmith) use a simple LLM-as-a-Judge framework to detect hallucinations for deployed RAG applications. There's a ton of hallucination detection research out there like this or this survey, so I wondered why aren't any of these providers offering more advanced research-backed methods? Given the user input query, retrieved context, and LLM output, one can pass this data to another LLM to evaluate whether the output is grounded in the context. So I benchmarked this LLM-as-a-Judge framework against a couple of research methods on the HaluBench dataset - and turns out they're probably right! A strong base model with chain-of-thought prompting seems to work better than various research methods. Code here. Partial results:
| Framework | Accuracy | F1 Score | Precision | Recall |
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| Base (GPT-4o) | 0.754 | 0.760 | 0.742 | 0.778 |
| Base (GPT-4o-mini) | 0.717 | 0.734 | 0.692 | 0.781 |
| Base (GPT-4o, sampling) | 0.765 | 0.766 | 0.762 | 0.770 |
| CoT (GPT-4o) | 0.833 | 0.831 | 0.840 | 0.822 |
| CoT (GPT-4o, sampling) | 0.823 | 0.820 | 0.833 | 0.808 |
| Fewshot (GPT-4o) | 0.737 | 0.773 | 0.680 | 0.896 |
| Lynx | 0.766 | 0.780 | 0.728 | 0.840 |
| RAGAS Faithfulness (GPT-4o) | 0.660 | 0.684 | 0.639 | 0.736 |
| RAGAS Faithfulness (HHEM) | 0.588 | 0.644 | 0.567 | 0.744 |
| G-Eval Hallucination (GPT-4o) | 0.686 | 0.623 | 0.783 | 0.517 |