Large language models (LMs) beyond a certain scale, demonstrate the emergent capability
of generating free-text rationales for their predictions via chain-of-thought (CoT) prompting …

Explanations of neural models aim to reveal a model's decision-making process for its
predictions. However, recent work shows that current methods giving explanations such as …

Neural language models (LMs) have achieved impressive results on various language-
based reasoning tasks by utilizing latent knowledge encoded in their own pretrained …

With the proliferation of social media, accurate detection of hate speech has become critical
to ensure safety online. To combat nuanced forms of hate speech, it is important to identify …

Large language models (LMs) are capable of generating free-text rationales to aid question
answering. However, prior work 1) suggests that useful self-rationalization is emergent only …

Real-world domain experts (eg, doctors) rarely annotate only a decision label in their day-to-
day workflow without providing explanations. Yet, existing low-resource learning techniques …

Large language models (LLMs) have shown remarkable capabilities in various natural
language understanding tasks. With only a few demonstration examples, these LLMs can …

Human-annotated labels and explanations are critical for training explainable NLP models.
However, unlike human-annotated labels whose quality is easier to calibrate (eg, with a …

Generating free-text rationales is a promising step towards explainable NLP, yet evaluating
such rationales remains a challenge. Existing metrics have mostly focused on measuring the …

Data annotation is a costly task; thus, researchers have proposed low-scenario learning
techniques like Active-Learning (AL) to support human annotators; Yet, existing AL works …