4 papers
MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications
Qing He, Dongsheng Bi, Jianrong Lu +20
The proliferation of Large Language Models (LLMs) presents transformative potential for healthcare, yet practical deployment is hindered by the absence of frameworks that assess re…
EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory
Ye Shen, Dun Pei, Yiqiu Guo +6
Despite recent advances in understanding and leveraging long-range conversational memory, existing benchmarks still lack systematic evaluation of large language models(LLMs) across…
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models
YiQiu Guo, Yuchen Yang, Zhe Chen +5
The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. E…
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications
Zhe Chen, Yusheng Liao, Shuyang Jiang +4
Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer he…