To further evaluate the effectiveness of the proposed approach with complex label space, we conduct experiments on the relation extraction and event extraction datasets, including SemEval Task 8. The proposed model is implemented using Pytorch. We utilize a grid search over multiple hyperparameters and select the best result as measured on. We report mean and standard deviation performance over 5 different splits.
As shown in Table 2 , we observe that our approach obtains better performance than conventional fine-tuning and achieves comparable results with LM-BFF. Note that DART does not need any prompt engineering or external model e. DART can obtain These results indicate that DART can better stimulate potential ability and makes the pre-trained language model a better few-shot learner. We also notice that DART yields better performance than P-tuning, which indicates that label optimization is beneficial.
For the classification tasks with the complex label space, as shown in Table 3 and Figure 2 a , we observe that DART outperforms the conventional fine-tuning approach as well as LM-BFF with a large margin on relation extraction and event extraction datasets in both the few-shot and fully supervised settings.
The proposed approach achieves an improvement of 2. These findings also indicate that more relevant templates and labels can be determined without expert intervention, making it possible to generalize the proposed approach to other domains. Furthermore, we notice that the improvement decays slowly when K becomes larger i.
Our approach is a simple yet effective fine-tuning paradigm that does not require prompt engineering within the complex label space, thus, making it possible to be an appropriate plug-in for some SOTA models. We conduct an ablation study to validate the effectiveness of the components in the proposed approach. We observe that DART exhibits a performance decay in the absence of any one of the modules, i. Furthermore, we notice that differentiable label optimization is more sensitive to performance and is highly beneficial for DART, especially for low-resource settings.
Since the proposed approach is the first approach that utilizes the differentiable label optimization, these findings illustrate that a suitable label token is important.
To evaluate whether the proposed approach can be applied to other LMs, we conduct experiments using GPTmedium. We conduct a nearest-neighbor vocabulary embedding search to project the Top-3 optimized pseudo-label tokens in V to a readable natural language. This finding indicates that optimized label embeddings can present better semantic representation ability.
The ability of the proposed approach to perform few-shot learning can be attributed to the label and being a true language understanding task, that once the model is capable of performing it correctly, it can easily apply this knowledge to other tasks that are framed as such.
Superficially, i DART does not optimize any new parameters; however, conventional fine-tuning should learn an explicit classifier head over [CLS] embeddings, which may fail in the low-data regime. Our work may fail when the distribution of the task corpus varies from that of the pre-training corpus.
For example, a general pre-trained language model may be fine-tuned with more training instances in a specific domain e. This issue can be addressed by intermediate training Phang et al. Besides, our work also shows an instability associated with hyper-parameters which is also observed by Dodge et al. Overall, however, we believe our work will inspire future work to few-shot settings with more practical applications to low-data settings, e.
This paper presents DART, a simple-yet-effective fine-tuning approach that improves the fast-shot learning pre-trained language model. The proposed approach can produce satisfactory improvements in the few-shot scenarios when compared to the conventional fine-tuning approaches. The proposed method is also pluggable for other language models and can be extended to other tasks, such as intent detection.
Intuitively, the results obtained in this study can be used to stimulate two future research directions in the few-shot learning for NLP: i Extending the proposed approach to a semi-supervised setting to further leverage unlabeled data; ii Extending the proposed approach to few-shot lifelong learning, whereas prompts must be optimized with adaptive tasks. The pre-train-fine-tune approach has become the standard for natural language processing NLP.
However, supervised fine-tuning is still practically affected by labeled data. This study proposes a novel pluggable, extensible, and efficient approach named DifferntiAble pRompT DART , which can convert small language models into better few-shot learners without any prompt engineering.
We believe that our study makes a significant contribution to the literature because determining the appropriate prompts requires domain expertise, and handcrafting a high-performing prompt often requires impractically large validation sets, and these issues have been overcome with the use of the proposed method, which is model-agnostic, parameter-efficient, and independent of prompt engineering.
We experimentally verified our proposed approach on 13 standard NLP tasks, and it was seen to outperform several standard NLP platforms. PyTorch: an imperative style, high-performance deep learning library. Exploring the limits of transfer learning with a unified text-to-text transformer. Already have an account? Login here. Don't have an account? Signup here. There are no comments yet. Ningyu Zhang 36 publications. Luoqiu Li 6 publications. Xiang Chen 84 publications. Shumin Deng 26 publications.
Zhen Bi 7 publications. Chuanqi Tan 28 publications. Fei Huang 50 publications. Huajun Chen 51 publications. Related Research. Sinong Wang , et al. Tianyu Gao , et al. Liyuan Liu , et al. Belinda Z. Li , et al. Kang Min Yoo , et al. Zhiyu Chen , et al. Pengfei Liu , et al. Scao and Rush observe that prompting can often compensate for hundreds of data points on average across multiple classification tasks.
However, determining the appropriate prompts requires domain expertise, and handcrafting a high-performing prompt often requires impractically large validation sets Perez et al.
Recent studies Lu et al. Therefore, previous approaches have attempted to search for discrete prompt tokens automatically. However, it is non-trivial for widespread classification tasks to obtain an optimized prompt template and target label token. Gao et al. However, these approaches still have to optimize the external parameters e. The proposed approach focuses on a more realistic few-shot setting the number of labeled instances per class can be any variable.
To further evaluate the effectiveness of the proposed approach with complex label space, we conduct experiments on the relation extraction and event extraction datasets, including SemEval Task 8 Hendrickx et al.
The proposed model is implemented using Pytorch Paszke et al. We employ AdamW as the optimizer. We follow Soares et al. External Parameter. External Architecture. Prefix-Tuning Li and Liang WARP Hambardzumyan et al. P-tuning Liu et al. DART Ours. SST-2 acc. MR acc. CR acc. Subj acc. TREC acc. MNLI acc. SNLI acc. QNLI acc. MRPC F1. QQP F1. He thought that Emmanuel had the perfect face and personality for commercials. The friend proved right. Emmanuel was signed by the agency the minute the agents saw him and before long he was appearing in commercials for such products as fruit juice, cars, stereos, glue, soup, toys, coffee, pudding, pizza, and, of course, Burger King.
His Colgate commercial can be seen in the movie 'Splash' when Madison the mermaid goes to the mall it can be seen on the TVs in the background. Emmanuel also became one of the biggest stars in Japan as well as America. He made three personal-appearance tours in that country, and a record he made there shot to the top of the charts.
He even made a television movie for Japanese television called Samurai in New York. He has maintained a friendship with music superstar Michael Jackson for many years. It all began when they met at an awards ceremony. The two young men spent many happy hours together discussing performing and show business. The 3' 6" tall he says there is no known medical reason for his still-short stature and has grown 6" since the age of 12 Lewis graduated from Clark Atlanta University with a theater arts degree in He is said to be looking for another TV series and to possibly go into directing.
He is good friends with Marc Price 'Skippy' on Family Ties and now a stand-up comedian and has gone on tour with him and done various promotional appearances with him. He founded his own musical label called Emmanuel Lewis Entertainment. He is said to have a black belt in karate and is a martial arts expert. He also is a student of Billy Blanks' Tae-Bo.
In the August 1st episode, he played an In February , he appeared in a commercial for Denny's. Sign In. Edit Emmanuel Lewis. Showing all 47 items. Graduated from Clark Atlanta University in with a theatre arts degree. Was a contestant on "The Weakest Link". His first question was "What month has the least amount of days? His college years stretched from until He took time off for occasional acting gigs.
Returned to show business after graduating college in , but his options were limited. He was just over 4 feet tall and still looked like the little boy from Webster Has one sister and two brothers: Lizziebeth b.
Was often mistaken for Gary Coleman because of the 3 things he had in common with the Diff'rent Strokes child star: he's African-American, short, and has starred in a sitcom about trans-racial adoptions. Was 12 years old when Webster first premiered. The series finale premiered just one day after his 18th birthday.
George Burns died on his 25 birthday, and The Notorious B. His contract was renegotiated by his agent and attorney after the first season of Webster to commit him to five more seasons and give him a share of the profits, including syndication money.
His tight-knit family kept him off the destructive path traveled by so many child stars. Chooses his projects very carefully. He turned down an offer to play an urban gang-banger. Became a budding entrepreneur, dabbling with both a limousine company and a car wash service. Remained friends with Michael Jackson up until his death, though they rarely saw each other. According to endocrinologists, he has all the potential for normal growth. Had no entourage or limo during his Webster fame.
His mother drove him to work and home. His physical size combined with his age made his Hollywood clout all that more incongruous. Founded his own music label, Emmanuel Lewis Entertainment June I love the concept that if I ever wanted to walk away from show biz without having to depend on any past money, I can have current money being taken care of all the time.
I would never really expose any of my girlfriends to the Press. Everything that I would have with them would be very private. I didn't want to put them through what I had to go through.
My mom is extremely protective of me. Wherever I went, my mom and my family went. It was never a shock to me. It was never a thing where I'd say, 'Oh, my God! What's going on here?
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