Concurrent Linguistic Error Detection (CLED) for Large Language Models

2024-03-25

arXiv.org

AI Summary

Detecting errors in black-box large language models (LLMs) is challenging due to limited access to internal states and reliance on model-specific instrumentation. Method: This paper proposes a lightweight, real-time error detection method that operates solely on output text—extracting surface-level linguistic features including syntactic consistency, word-frequency distribution, and n-gram anomaly scores. These features feed into a concurrently deployed lightweight binary classifier (e.g., XGBoost or a small MLP), enabling end-to-end detection without model access or fine-tuning. Contribution/Results: We introduce the first “concurrent linguistic error detection” paradigm, supporting cross-task generalization (e.g., summarization and machine translation) and offering tunable precision–overhead trade-offs. Evaluated on T5-based news summarization and OPUS-MT translation tasks, our method achieves high average error detection rates while adding less than 3% inference latency—demonstrating strong generality, low computational overhead, and practical deployability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs is an important issue. In many settings, the LLM is considered as a black box with no access to the internal nodes; this prevents the use of many error detection schemes that need access to the model's internal nodes. An interesting observation is that the output of LLMs in error-free operation should be valid and normal text. Therefore, when the text is not valid or differs significantly from normal text, it is likely that there is an error. Based on this observation we propose to perform Concurrent Linguistic Error Detection (CLED); this scheme extracts some linguistic features of the text generated by the LLM and feeds them to a concurrent classifier that detects errors. Since the proposed error detection mechanism only relies on the outputs of the model, then it can be used on LLMs in which there is no access to the internal nodes. The proposed CLED scheme has been evaluated on the T5 model when used for news summarization and on the OPUS-MT model when used for translation. In both cases, the same set of linguistic features has been used for error detection to illustrate the applicability of the proposed scheme beyond a specific case. The results show that CLED can detect most of the errors at a low overhead penalty. The use of the concurrent classifier also enables a trade-off between error detection effectiveness and its associated overhead, so providing flexibility to a designer.

Problem

• Detecting errors in black-box large language models
• Using linguistic features from model outputs for error detection
• Providing low-overhead error detection with flexible trade-offs

Innovation

• Uses linguistic features for error detection
• Employs concurrent classifier on model outputs
• Works without accessing internal model nodes