The LLM Hallucination Index - RAG Special focuses on introducing a unique index for assessing the hallucination phenomenon of large language models (LLMs). This index covers up to 22 leading models, and its evaluation process is comprehensive and meticulous, especially for tasks based on Retrieval-Augmented Generation (RAG).
In the evaluation process, three different lengths of context tests are included to fully examine the models' performance under different situations. First is the short context test, which is set to have a context containing fewer than 5k tokens. This short context scenario simulates some relatively simple task scenarios with more concentrated information, reflecting the model's ability to handle concise information.
Next is the medium context test, which is defined as a range between 5k and 25k tokens. This interval of testing can be seen as a test of the model's ability to handle tasks with medium amounts of information, involving more abundant and complex information, requiring the model to better integrate and understand this information to avoid hallucination phenomena.
Finally, there is the long context test, which spans from 40k to 100k tokens. The long context test simulates those complex task scenarios with extremely rich information that require the model to remember and process for a long time. Through this test, one can fully understand the model's ability to cope with a large amount of information and whether it can maintain accuracy in processing long sequences of information without hallucination.
Through this series of rigorous tests with different lengths of context, the website provides detailed performance data of each model under different context lengths. These data not only intuitively show the strengths and weaknesses of each model but also provide a strong basis for further analysis of the models' performance characteristics. Moreover, after in-depth analysis and summary of these data, some interesting trends have been found.
One trend is that open-source models are gradually approaching the performance of proprietary models. This indicates that with the continuous development and progress of technology, the ability of open-source models to handle various tasks has been significantly improved, and the gap between open-source and proprietary models is gradually narrowing. This is a positive signal for the entire industry, meaning that more researchers and developers can use open-source models for innovation and exploration, promoting the popularization and development of technology.
Another trend is that models may not perform worse in long context tests than in short context tests. This finding breaks the previous perception that models may have a disadvantage when processing long text information. It indicates that some advanced models have the ability to maintain good performance in processing long sequences of information, better adapting to complex practical application scenarios, providing possibilities for the model's application in a wider range of fields.