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Vipul Raheja

8 accepted papers

2025

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

NAACL 2025long

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advanceme…

Cited by 1SourcePDFScholar
2024

Benchmarking Cognitive Biases in Large Language Models as Evaluators

ACL 2024findings

Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-context learning. In this work, we assemble 16 LLMs encompassing four different size ranges and evaluate their output responses by preference ranking from the other LLMs as eval…

2024

ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models

NAACL 2024long

In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering. However, research on understanding their capabilities on the task of self-contradictions in long documents has been…

2024

Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs

ACL 2024long

With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that were written by humans, particularly uncovering the…

2024

mEdIT: Multilingual Text Editing via Instruction Tuning

NAACL 2024long

We introduce mEdIT, a multi-lingual extension to CoEdIT – the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lingual large, pre-trained language models (LLMs) via instruction tuning. They are designed to take instructions from the us…

2023

CoEdIT: Text Editing by Task-Specific Instruction Tuning

EMNLP 2023long findings

We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present a large langua…

Cited by 0SourcecodeScholar
2022

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks

EMNLP 2022main

Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document.Most recent research has focused on understanding and classifying different types of edits in the ite…

2022

Understanding Iterative Revision from Human-Written Text

ACL 2022long

Writing is, by nature, a strategic, adaptive, and, more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a s…