OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination
  Detection with Weakly Supervised Data
Chengcheng Wei Ze Chen Songtan Fang Jiarong He Max Gao
This paper mainly describes a unified system for hallucination detection of
LLMs, which wins the second prize in the model-agnostic track of the
SemEval-2024 Task 6, and also achieves considerable results in the model-aware
track. This task aims to detect hallucination with LLMs for three different
text-generation tasks without labeled training data. We utilize prompt
engineering and few-shot learning to verify the performance of different LLMs
on the validation data. Then we select the LLMs with better performance to
generate high-quality weakly supervised training data, which not only satisfies
the consistency of different LLMs, but also satisfies the consistency of the
optimal LLM with different sampling parameters. Furthermore, we finetune
different LLMs by using the constructed training data, and finding that a
relatively small LLM can achieve a competitive level of performance in
hallucination detection, when compared to the large LLMs and the prompt-based
approaches using GPT-4.