Very good work. I was working on this before, but I was not successful. I used Visualglm to do lora training. The dataset also used pictures and bboxes of various types of objects in the pictures, and then gpt to generate descriptive statements, but instead of having coordinate information in the statements, I had gpt generate orientation space nouns instead. The results were mediocre, and even though I used comparison samples for training, there was still a serious illusion. It is good to see that your work has been successful so far, can you share examples of the sample data used for training for each task? I only see the questioning interrogatives and instructions in the paper, not the format of the responses generated by gpt. Thanks!
非常好的工作。
之前我也在做这一方面的工作，但是我没有成功。
我使用了Visualglm做lora训练。
数据集也是用了图片和图片中各类物体的bbox，再用gpt生成描述性语句，但是语句里不包含有坐标信息，而是让gpt生成了方位空间名词来代替。
效果很一般，即使我使用了对比样本进行训练，依旧有很严重的幻觉。
很高兴看到你们的工作取得了现在的成功，可以分享一下各个任务训练时使用的样本数据的示例吗？我在论文中只看到了提问的问句和指令，没有看到gpt生成的回答的格式，谢谢！
Very good work. I was working on this before, but I was not successful. I used Visualglm to do lora training. The dataset also used pictures and bboxes of various types of objects in the pictures, and then gpt to generate descriptive statements, but instead of having coordinate information in the statements, I had gpt generate orientation space nouns instead. The results were mediocre, and even though I used comparison samples for training, there was still a serious illusion. It is good to see that your work has been successful so far, can you share examples of the sample data used for training for each task? I only see the questioning interrogatives and instructions in the paper, not the format of the responses generated by gpt. Thanks! 非常好的工作。 之前我也在做这一方面的工作，但是我没有成功。 我使用了Visualglm做lora训练。 数据集也是用了图片和图片中各类物体的bbox，再用gpt生成描述性语句，但是语句里不包含有坐标信息，而是让gpt生成了方位空间名词来代替。 效果很一般，即使我使用了对比样本进行训练，依旧有很严重的幻觉。 很高兴看到你们的工作取得了现在的成功，可以分享一下各个任务训练时使用的样本数据的示例吗？我在论文中只看到了提问的问句和指令，没有看到gpt生成的回答的格式，谢谢！