Concurrent Linguistic Error Detection (CLED): A New Methodology for Error Detection in Large Language Models

Jinhua Zhu, Javier Conde, Zhen Gao, Pedro Reviriego, Shanshan Liu, Fabrizio Lombardi

Abstract: The utilization of Large Language Models (LLMs) requires dependable operation in the presence of errors in the hardware (caused by for example radiation) as this has become a pressing concern. At the same time, the scale and complexity of LLMs limit the overhead that can be added to detect errors. Therefore, there is a need for low-cost error detection schemes. Concurrent Error Detection (CED) uses the properties of a system to detect errors, so it is an appealing approach. In this paper, we present a new methodology and scheme for error detection in LLMs: Concurrent Linguistic Error Detection (CLED). Its main principle is that the output of LLMs should be valid and generate coherent text; therefore, when the text is not valid or differs significantly from the normal text, it is likely that there is an error. Hence, errors can potentially be detected by checking the linguistic features of the text generated by LLMs. This has the following main advantages: 1) low overhead as the checks are simple and 2) general applicability, so regardless of the LLM implementation details because the text correctness is not related to the LLM algorithms or implementations. The proposed CLED has been evaluated on two LLMs: T5 and OPUS-MT. The results show that with a 1% overhead, CLED can detect more than 87% of the errors, making it suitable to improve LLM dependability at low cost.

External IDs:dblp:journals/tc/ZhuCGRLL25