用Conditional training代表在PHF(橙色实线),SFT+HF(橙色虚线)和MLE的Toxicity的分数,可以看到随tokens个数增加,PHF有明显优势 最后还有一个结论,在红队训练过程中发现的LM对池中adversarial prompt的平均misalignment score(越低越好),对于经过conditional training预处理(实线),只用conditional training finetune的模型(-...
After a human assessment of their sentiment, the three datasets we gathered are: Since previous works have highlighted the benefits of fine-tuning after pre-training with noisy data (Sinha et al., 2021, Krishna et al., 2021), and even no-human languages (Chiang and yi Lee, 2020), such...
{lidong1,nanya,wenwan,fuwei}@microsoft.com{xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.comAbstractThis paper presents a new U NI f i ed pre-trained L anguage M odel (U NI LM) thatcan be f i ne-tuned for both natural language understanding and generation tasks.The model is pre-trained...
35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65)....
A Dive into Vision-Language Models Human learning is inherently multi-modal as jointly leveraging multiple senses helps us understand and analyze new information better. Unsurprisingly, recent advances in multi-modal learning take inspiration from the effectiveness of this process to create...
GLM also significantly outperforms T5 on NLU and generation tasks with fewer parameters and data. Inspired by Pattern-Exploiting Training (PET) Schick and Schütze (2020a), we reformulate NLU tasks as manually-crafted cloze questions that mimic human language. Different from the BERT-based models ...
Some common practices in named entity recognition and relation extraction may no longer be necessarily with the use of neural language models. Specifically, with the use of self-attention mechanism, the utility in explicit sequential modeling becomes questionable. In ablation studies,...
As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. ...
y1gao@stanford.edu {kunyus, zhpengka, belvae, onuriel, srikara, shabnam, ztu, vmahad, soattos}@amazon.com Abstract We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scal...
11.预训练评估metric,以及这些metric与下游任务的关系。尽管现在预训练模型有很多维度的metric,比如math的GSM8k,Code的human eval,推理的MMLU数据集 [1]。但是这些数据集效果的提升和下游任务的关系是怎么样的?以及是否有更合理更高效的预训练评估的metric是值得深入研究的方向。