Summary of Concurrent Linguistic Error Detection (cled) For Large Language Models, by Jinhua Zhu et al.
Concurrent Linguistic Error Detection (CLED) for Large Language Models
by Jinhua Zhu, Javier Conde, Zhen Gao, Pedro Reviriego, Shanshan Liu, Fabrizio Lombardi
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Concurrent Linguistic Error Detection (CLED) scheme is an efficient method for detecting errors in Large Language Models (LLMs). By analyzing the output text of LLMs in error-free operation, CLED can identify invalid or abnormal text, indicating potential errors. This approach does not require access to the internal nodes of the model, making it applicable to a wide range of LLMs. The scheme uses concurrent classification to detect errors while providing flexibility for designers to balance detection effectiveness with computational overhead. Evaluation on the T5 and OPUS-MT models shows that CLED can effectively detect most errors at a relatively low cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are widely used, but their reliability is crucial. One way to ensure this is by detecting errors in LLMs. Since many LLMs are “black boxes” without internal access, we developed a new method called Concurrent Linguistic Error Detection (CLED). This method looks at the output text of an error-free model and checks if it’s normal or valid. If not, there might be an error! We tested CLED on two models and found that it can detect most errors without using too many computing resources. |
Keywords
* Artificial intelligence * Classification * T5
