반응형 오늘의 자연어 처리572 [2023-12-08] 오늘의 자연어처리 Teaching Specific Scientific Knowledge into Large Language Models through Additional Training Abstract:Through additional training, we explore embedding specialized scientific knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that effective knowledge integration requires reading texts from multiple perspectives, especially in instructional formats. We utilize text augmen.. 2023. 12. 8. [2023-12-07] 오늘의 자연어처리 Impact of Tokenization on LLaMa Russian Adaptation Abstract:Latest instruction-tuned large language models (LLM) show great results on various tasks, however, they often face performance degradation for non-English input. There is evidence that the reason lies in inefficient tokenization caused by low language representation in pre-training data which hinders the comprehension of non-English ins.. 2023. 12. 7. [2023-12-06] 오늘의 자연어처리 Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models Abstract:The advancement of Large Language Models(LLMs) has brought substantial attention to the Chain of Thought(CoT) approach, primarily due to its ability to enhance the capability of LLMs on tasks requiring complex reasoning. Moreover, the significance of CoT approaches extends to the application of LLMs fo.. 2023. 12. 6. [2023-12-05] 오늘의 자연어처리 Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs Abstract:Unsupervised relation extraction (URE) aims to extract relations between named entities from raw text without requiring manual annotations or pre-existing knowledge bases. In recent studies of URE, researchers put a notable emphasis on contrastive learning strategies for acquiring relation representations. H.. 2023. 12. 5. 이전 1 ··· 6 7 8 9 10 11 12 ··· 143 다음 반응형