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Found 14 papers, 10 papers with code
Prompt Engineering a Prompt Engineer
While recent works indicate that large language models can be meta-prompted to perform automatic prompt engineering, we argue that their potential is limited due to insufficient guidance for complex reasoning in the meta-prompt.
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
We investigate the predictability of large language model (LLM) capabilities: given records of past experiments using different model families, numbers of parameters, tasks, and numbers of in-context examples, can we accurately predict LLM performance on new experiment configurations?
Estimating Large Language Model Capabilities without Labeled Test Data
In this paper, we propose the task of ICL accuracy estimation, in which we predict the accuracy of an LLM when doing in-context learning on a new task given only unlabeled test data for that task.
Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts
Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapting to new tasks efficiently.
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models
Distilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time.
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks.
On the Influence of Masking Policies in Intermediate Pre-training
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline.
Refining Language Models with Compositional Explanations
However, such a regularization technique lacks flexibility and coverage, since only importance scores towards a pre-defined list of features are adjusted, while more complex human knowledge such as feature interaction and pattern generalization can hardly be incorporated.
Learning to Generate Task-Specific Adapters from Task Description
Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples.
Studying Strategically: Learning to Mask for Closed-book QA
Thus, our policy packs task-relevant knowledge into the parameters of a language model.
Teaching Machine Comprehension with Compositional Explanations
Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation
Successfully training a deep neural network demands a huge corpus of labeled data.
Learning from Explanations with Neural Execution Tree
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive.
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction
In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution.
