和闭源模型相比,Llama 2 比 GPT-4 和 PaLM-2-L 还有差距 1.4 细节 Context length:4k的上下文,对于chat、summarization、understanding longer documents 等任务有较好效果,在150B tokens上进行2k和4k的对比,发现SQUAD任务性能没降低,SCROLLS上有提升(平均长度3.5k)。 Grouped-Query Attention :对于更大参数量、更大...
Based on these results, the cost for summarization withgpt-4is still 30 times more than the cost ofLlama-2-70b, even though both are about the same level of factuality. The numbers do not significantly change for a summary ratio anywhere in the 0.1(28x) to 0.3 ...
总结性文档(summarization)和在线论坛数据通常 prompt 更长, 对话式 prompt 通常较短。 与现有的开源数据集相比,我们的偏好数据具有更多的对话轮次,并且长度更长。 3.2.2 奖励建模(Reward Modeling) 奖励模型的工作机制: 输入:模型的 response 及其相应的 prompt(包括前几轮的上下文); 输出:一个标量分数,表示模型的...
Koala [9]在来自 Alpaca 的微调数据集和其他来源(如 ShareGPT、HC3、OIG、Anthropic HH 和 OpenAI WebGPT/Summarization)的大量对话示例上对 LLaMA-13B 进行了微调。与之前的模仿模型相比,Koala 在更大的数据集上进行了微调,并进行了更全面的评估。 GPT4ALL [16]在来自 GPT-3.5-turbo 的80万个聊天补全(chat ...
In this research paper, we explore the optimization for conversation summarization of the Llama 2.7 b model by quantization-aware fine-tuning, specifically exploiting QLORA quantization techniques. In natural language processing (NLP), large language models (LLMs) have become powerful tools for various...
question answering, generation, information extraction, and summarization. Fine-tuning datasets for TC-Llama 2 Table 2 List of instruction datasets for TC-Llama 2 Full size table After fine-tuning Llama-2-chat-7B and producing GI-Llama 2, we further fine-tune our model on specialized ...
ModelsSingle-doc QAMulti-doc QASummarizationFew-shot LearningCode CompletionSynthetic TaskAvg Chinese-Alpaca-2-7B-64K 44.7 28.1 14.4 39.0 44.6 5.0 29.3 Chinese-LLaMA-2-7B-64K 27.2 16.4 6.5 33.0 7.8 5.0 16.0 Chinese-Alpaca-2-13B-16K 47.9 26.7 13.0 22.3 46.6 21.5 29.7 Chinese-Alpaca-2-13B ...
When a dialogue was sent to the base 7B model, the summarization results are not proper as shown in figure 5. After fine-tuning the base model on the SAMsum dataset, the same dialogue prompts a proper summarized result as shown in figure 6. The difference in results shows that fine...
Summarization NLP·总结 150四、如何Safety1、安全“系统” 采用了一种新的系统级方法来负责任地开发和部署 Llama。将 Llama 模型视为更广泛系统的一部分,让开发人员掌握主导权。 Llama 模型将作为开发人员在设计时考虑到其独特的最终目标的系统的基础部分。
Summarization and online forum data generally have longer prompts, while dialogue-style prompts are usually shorter. Compared to existing open-source datasets, our preference data features more conversation turns, and are longer, on average. 3.2.2 Reward Modeling The reward model takes a model ...