Yongbin Liu

Cui Long, Yongbin Liu, Chunping Ouyang et al.

Large Language Models (LLMs) have exhibited remarkable proficiency in natural language understanding, prompting extensive exploration of their potential applications across diverse domains. In the medical domain, open-source LLMs have demonstrated moderate efficacy following domain-specific fine-tuning; however, they remain substantially inferior to proprietary models such as GPT-4 and GPT-3.5. These open-source models encounter limitations in the comprehensiveness of domain-specific knowledge and exhibit a propensity for 'hallucinations' during text generation. To mitigate these issues, researchers have implemented the Retrieval-Augmented Generation (RAG) approach, which augments LLMs with background information from external knowledge bases while preserving the model's internal parameters. However, document noise can adversely affect performance, and the application of RAG in the medical field remains in its nascent stages. This study presents the Bailicai framework: a novel integration of retrieval-augmented generation with large language models optimized for the medical domain. The Bailicai framework augments the performance of LLMs in medicine through the implementation of four sub-modules. Experimental results demonstrate that the Bailicai approach surpasses existing medical domain LLMs across multiple medical benchmarks and exceeds the performance of GPT-3.5. Furthermore, the Bailicai method effectively attenuates the prevalent issue of hallucinations in medical applications of LLMs and ameliorates the noise-related challenges associated with traditional RAG techniques when processing irrelevant or pseudo-relevant documents.

Jiahao Ying, Yixin Cao, Kai Xiong et al.

This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will not only help to understand LLMs' decision mechanism but also benefit real-world applications, such as retrieval-augmented generation (RAG). Drawing on cognitive theory, we target the first scenario of decision-making styles where there is no superiority in the conflict and categorize LLMs' preference into dependent, intuitive, and rational/irrational styles. Another scenario of factual robustness considers the correctness of prompt and memory in knowledge-intensive tasks, which can also distinguish if LLMs behave rationally or irrationally in the first scenario. To quantify them, we establish a complete benchmarking framework including a dataset, a robustness evaluation pipeline, and corresponding metrics. Extensive experiments with seven LLMs reveal their varying behaviors. And, with role play intervention, we can change the styles, but different models present distinct adaptivity and upper-bound. One of our key takeaways is to optimize models or the prompts according to the identified style. For instance, RAG models with high role play adaptability may dynamically adjust the interventions according to the quality of retrieval results -- being dependent to better leverage informative context; and, being intuitive when external prompt is noisy.