Despite their unprecedented success, even the largest language models make mistakes.
Similar to how humans learn and improve using feedback, previous work proposed …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Large language models (LLMs) have emerged as powerful techniques for various NLP
tasks, such as mathematical reasoning and plan generation. In this paper, we study …

Despite the recent progress in language generation models, their outputs may not always
meet user expectations. In this work, we study whether informational feedback in natural …

We present EdiT5-a novel semi-autoregressive text-editing model designed to combine the
strengths of non-autoregressive text-editing and autoregressive decoding. EdiT5 is faster …

While understanding and removing gender biases in language models has been a long-
standing problem in Natural Language Processing, prior research work has primarily been …

Conversational query rewriting (CQR) realizes conversational search by reformulating the
search dialogue into a standalone rewrite. However, existing CQR models either are not …

Text simplification research has mostly focused on sentence-level simplification, even
though many desirable edits-such as adding relevant background information or reordering …

Many NLP pipelines split text into sentences as one of the crucial preprocessing steps. Prior
sentence segmentation tools either rely on punctuation or require a considerable amount of …

Large Language Models (LLMs) often inherit biases from the web data they are trained on,
which contains stereotypes and prejudices. Current methods for evaluating and mitigating …