반응형 NLP572 [2023-11-18] 오늘의 자연어처리 ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks Abstract:Large language models have shown promising performance in code generation benchmarks. However, a considerable divide exists between these benchmark achievements and their practical applicability, primarily attributed to real-world programming's reliance on pre-existing libraries. Instead of evaluat.. 2023. 11. 18. [2023-11-17] 오늘의 자연어처리 SiRA: Sparse Mixture of Low Rank Adaptation Abstract:Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters, where all parameters are used to adapt certain task. We found this less effective empirically using the example of LoRA that introducing more trainable parameters .. 2023. 11. 17. [2023-11-16] 오늘의 자연어처리 Extrinsically-Focused Evaluation of Omissions in Medical Summarization Abstract:The goal of automated summarization techniques (Paice, 1990; Kupiec et al, 1995) is to condense text by focusing on the most critical information. Generative large language models (LLMs) have shown to be robust summarizers, yet traditional metrics struggle to capture resulting performance (Goyal et al, 2022) in more .. 2023. 11. 16. [2023-11-15] 오늘의 자연어처리 Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models Abstract:Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document representations. Inspired by recent advances in in-conte.. 2023. 11. 15. 이전 1 ··· 11 12 13 14 15 16 17 ··· 143 다음 반응형