Artificial intelligence (AI) has significantly impacted various fields. Large language models (LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-
Large Language models can potentially generate content that may be harmful, biased, or misaligned with what users actually want or expect. Alignment refers to theprocess of aligning an LLM's behavior with human preferences and ethical principles. It aims to mitigate risks associated with model behav...
In this paper, we propose a novel approach using a maximum entropy model for named entity alignment. To ease the training of the maximum e... D Feng,Y Lü,Z Ming - Conference on Empirical Methods in Natural Language Processing 被引量: 101发表: 2004年 Ontology and Model Alignment as a ...
[arxiv] Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment.2024.01 [arxiv] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs.2024.01 [arxiv] Clue-Guided Path Exploration: An Efficien...
CSA大型语言模型(LLM)威胁分类 Large Language Model (LLM) Threats Taxonomy.docx,Large Language Model (LLM) Threats Taxonomy The permanent and official location for the AI Controls Framework Working Group is https://research/working-groups/ai-controls ? 2
OpenAI continues to build extremely large language models, aiming to enhance the model’s capabilities in handling multimodal data, as well as providing APIs for the development of real-world applications. Despite the mainstream popularity and adoption, real-world applications in finance utilizing their...
自从GPT、EMLO、BERT的相继提出,以Pre-training + Fine-tuning 的模式在诸多自然语言处理(NLP)任务中被广泛使用,其先在Pre-training阶段通过一个模型在大规模无监督语料上预先训练一个预训练语言模型(Pre-trained Language Model,PLM),然后在Fine-tuning阶段基于训练好的语言模型在具体的下游任务上再次进行微调(Fine-...
Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. (HEALTH) 3, 1–23 (2021). CAS Google Scholar Liu, F., Shareghi, E., Meng, Z., Basaldella, M. & Collier, N. Self-Alignment Pretraining for Biomedical Entity Representations...
This approach improves the model’s overall performance without the need for additional labeled examples.3 Large Language Model as Attributed Training Data Generator Figure 1: The overall workflow of AttrPrompt. In this section, we present the design of our proposed method, AttrPrompt. This ...
The network is trained using a loss function with three components: (1) a language model predicting a missing human or primate amino acid using the surrounding multiple alignment as input; (2) a 3D convolutional “fill-in-the-blank” model predicting a missing amino acid in the 3D structure...