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Found 10 papers, 4 papers with code
Social Orientation: A New Feature for Dialogue Analysis
Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.
Parallel Structures in Pre-training Data Yield In-Context Learning
Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update.
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs to generate more consistent natural-language explanations on related examples.
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enable humans to precisely infer the model's outputs on diverse counterfactuals of the explained input.
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models
We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models.
On the Relation between Sensitivity and Accuracy in In-context Learning
In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios.
Meta-learning via Language Model In-context Tuning
The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples.
Cross-language Sentence Selection via Data Augmentation and Rationale Training
This paper proposes an approach to cross-language sentence selection in a low-resource setting.
Improved Synthetic Training for Reading Comprehension
Automatically generated synthetic training examples have been shown to improve performance in machine reading comprehension (MRC).
Detecting and Reducing Bias in a High Stakes Domain
Gang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep learning approach can predict aggression and loss in posts.
