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Aparna Garimella

18 accepted papers

2025

ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly

Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek +2
COLING 2025main

Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s n…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents

Akriti Jain, Pritika Ramu, Aparna Garimella, Apoorv Saxena
EMNLP 2025

Large Language Models (LLMs) have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations via instruction-tuning methods. However, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long docume

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Infogen: Generating Complex Statistical Infographics from Documents

Akash Ghosh, Aparna Garimella, Pritika Ramu, Sambaran Bandyopadhyay, Sriparna Saha
ACL 2025long

Statistical infographics are powerful tools that simplify complex data into visually engaging and easy-to-understand formats. Despite advancements in AI, particularly with LLMs, existing efforts have been limited to generating simple charts, with no prior work addressing the creation of complex info…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

LegalCore: A Dataset for Event Coreference Resolution in Legal Documents

Kangda Wei, Xi Shi, Jonathan Tong, Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain +2
ACL 2025finding

Recognizing events and their coreferential mentions in a document is essential for understanding semantic meanings of text. The existing research on event coreference resolution is mostly limited to news articles. In this paper, we present the first dataset for the legal domain, LegalCore, which has…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

DocScript: Document-level Script Event Prediction

Puneet Mathur, Vlad I. Morariu, Aparna Garimella, Franck Dernoncourt, Jiuxiang Gu, Ramit Sawhney +3
COLING 2024main

We present a novel task of document-level script event prediction, which aims to predict the next event given a candidate list of narrative events in long-form documents. To enable this, we introduce DocSEP, a challenging dataset in two new domains - contractual documents and Wikipedia articles, whe…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context

Nihar Sahoo, Pranamya Kulkarni, Arif Ahmad, Tanu Goyal, Narjis Asad, Aparna Garimella +1
NAACL 2024long

The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existing efforts predominantly focus on English language and the Western context, leaving a void for a reliable dataset that e…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2024

Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Text

Pritika Ramu, Aparna Garimella, Sambaran Bandyopadhyay
EMNLP 2024main

Understanding whether a generated table is of good quality is important to be able to use it in creating or editing documents using automatic methods. In this work, we underline that existing measures for table quality evaluation fail to capture the overall semantics of the tables, and sometimes unf…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Presentations are not always linear! GNN meets LLM for Text Document-to-Presentation Transformation with Attribution

Himanshu Maheshwari, Sambaran Bandyopadhyay, Aparna Garimella, Anandhavelu Natarajan
EMNLP 2024finding

Automatically generating a presentation from the text of a long document is a challenging and useful problem. In contrast to a flat summary, a presentation needs to have a better and non-linear narrative, i.e., the content of a slide can come from different and non-contiguous parts of the given docu…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement

Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna Garimella, Jordan Lee Boyd-Graber
EMNLP 2024finding

Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and accelerating the process. Current text-to-image models (Rombach et al., 2022a; Belouadi et al., 2023) struggle with gener…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness

Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, Dan Roth
EMNLP 2024finding

Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for th…

Cited by 4Source ↗PDF ↗Scholar ↗
2023

$k$NN-LM Does Not Improve Open-ended Text Generation

Shufan Wang, Yixiao Song, Andrew Drozdov, Aparna Garimella, Varun Manjunatha, Mohit Iyyer
EMNLP 2023long main

In this paper, we study the generation quality of interpolation-based retrieval-augmented language models (LMs). These methods, best exemplified by the $k$NN-LM, interpolate the LM's predicted distribution of the next word with a distribution formed from the most relevant retrievals for a given pref…

Cited by 0Source ↗Scholar ↗
2023

What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions

Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger
EMNLP 2023long main

Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of \textit{par…

Cited by 0Source ↗Scholar ↗
2023

“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters

Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng
EMNLP 2023long findings

Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content, including professional documents such as recommendation letters. Though bringing convenience, this application also introduces unprecedented fairness concerns. Model-gen…

Cited by 0Source ↗code ↗Scholar ↗
2022

Agent-Specific Deontic Modality Detection in Legal Language

Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger
EMNLP 2022main

Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties. While natural language understanding technologies can be valuable in supporting such understanding in the legal domain, the limited availability of d…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2022

Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models

Aniruddha Mahapatra, Sharmila Reddy Nangi, Aparna Garimella, Anandhavelu N
EMNLP 2022main

Transformer-based language models trained on large natural language corpora have been very useful in downstream entity extraction tasks. However, they often result in poor performances when applied to domains that are different from those they are pretrained on. Continued pretraining using unlabeled…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2021

AUTOSUMM: Automatic Model Creation for Text Summarization

Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya +1
EMNLP 2021main

Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets. However, obtaining the best model configuration for a given dataset requires an extensive knowledge of deep le…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2021

ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring

Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan, Anandhavelu N, Rajiv Jain
EMNLP 2021main

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses cal…

Cited by 10Source ↗PDF ↗Scholar ↗
2020

“Judge me by my size (noun), do you?” YodaLib: A Demographic-Aware Humor Generation Framework

Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
COLING 2020main

The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs® stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such sto…

Cited by 5Source ↗PDF ↗Scholar ↗