This repo contains the data and source code for baseline models in the NeurIPS 2021 benchmark paper for Constrained Language Understanding Evaluation Standard (CLUES) under MIT License.

The benchmark data is located in the data directory. We also release source codes for two fine-tuning strategies on CLUES, one with classic fine-tuning and the other with prompt-based fine-tuning.

1. > git clone [email protected]:microsoft/CLUES.git
2. > git clone [email protected]:namisan/mt-dnn.git
3. > cp -rf CLUES/classic_finetuning/ mt-dnn/
4. > cd mt-dnn/

1. Preprocess data
   > bash run_clues_data_process.sh <CLUES-DATA> <CLUES-TASK-DEF: e.g., experiments/clues/clues_ext_task_def.yml>

2. Train/test Models
   > bash run_clues_batch.sh <CLUES-DATA> <MODEL-SIZE: large or base>

1. cd prompt_finetuning
2. Run sh setup.sh to automatically fetch dependency codebase and apply our patch for CLUES

All prompt-based funetuning baselines run commands are in experiments.sh, simple run by sh experiments.sh

Here we maintain a leaderboard, allowing researchers to submit their results as entries.

• Each submission must be submitted as a pull request modifying the markdown file underlying the leaderboard.
• The submission must attach an accompanying public paper and public source code for reproducing their results on our dataset.
• A submission can be toward any subset of tasks in our benchmark, or toward the aggregate leaderboard.
• For any task targeted by the submission, we require evaluation on (1) 10, 20, and 30 shots, and (2) all 5 splits of the corresponding dataset and a report of their mean and standard deviation.
• Each leaderboard will be sorted by the 30-shot mean S1 score (where S1 score is a variant of F1 score defined in our paper).
• The submission should not use data from the 4 other splits during few-shot finetuning of any 1 split, either as extra training set or as validation set for hyperparameter tuning.
• However, we allow external data, labeled or unlabeled, to be used for such purposes. Each submission using external data must mark the corresponding columns "external labeled" and/or "external unlabeled". Note, in this context, "external data" refers to data used after pretraining (e.g., for task-specific tuning); in particular, methods using existing pretrained models only, without extra data, should not mark either column. For obvious reasons, models cannot be trained on the original labeled datasets from where we sampled the few-shot CLUES data.
• In the table entry, the submission should include a method name and a citation, hyperlinking to their publicly released source code reproducing the results. See the last entry of the table below for an example.

• FT = (classic) finetuning
• PT = prompt based tuning
• ICL = in-context learning, in the style of GPT-3
• μ±σ = mean μ and standard deviation σ across our 5 splits. Aggregate standard deviation is calculated using the sum-of-variance formula from individual tasks' standard deviations.

@article{cluesteam2021,
  title={Few-Shot Learning Evaluation in Natural Language Understanding},
  author={Mukherjee, Subhabrata and Liu, Xiaodong and Zheng, Guoqing and Hosseini, Saghar and Cheng, Hao and Yang, Greg and Meek, Christopher and Awadallah, Ahmed Hassan and Gao, Jianfeng},
  year={2021}
}

MT-DNN: https://github.com/namisan/mt-dnn
LM-BFF: https://github.com/princeton-nlp/LM-BFF

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