20 citations · 30 across the 28 of their papers we have counts for
32 papers
A Good Talk Does not Look Like a Summary, It Teaches You! Measuring Takeaways from Paper-to-Video Talks
Ishani Mondal, Aparna Garimella, Ananya Sai +2
Automatically generated videos from scientific papers are increasingly used for education and research dissemination. However, existing evaluation metrics mainly measure visual qua…
An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA
Saransh Sharma, Pritika Ramu, Aparna Garimella +1
Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and users often refine answers…
Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents
Akriti Jain, Anish Mulay, Divyansh Verma +3
Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing factors, and incorporating su…
Modeling Contextual Passage Utility for Multihop Question Answering
Akriti Jain, Aparna Garimella
Multihop Question Answering (QA) requires systems to identify and synthesize information from multiple text passages. While most prior retrieval methods assist in identifying relev…
TabReX : Tabular Referenceless eXplainable Evaluation
Tejas Anvekar, Junha Park, Aparna Garimella +1
Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely…
Knowing What's Missing: Assessing Information Sufficiency in Question Answering
Akriti Jain, Aparna Garimella
Determining whether a provided context contains sufficient information to answer a question is a critical challenge for building reliable question-answering systems. While simple p…
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives
Ritam Upadhyay, Naman Ahuja, Rishabh Baral +2
Large language model (LLM) approaches to tabular summarization rely on extensive prompt engineering, decomposition pipelines, or entity-level intermediate representations to achiev…
MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference
Jeonghyun Park, Ingeol Baek, Seunghyun Yoon +5
Real-world multi-hop QA is naturally linked with ambiguity, where a single query can trigger multiple reasoning paths that require independent resolution. Since ambiguity can occur…
Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents
Akriti Jain, Pritika Ramu, Aparna Garimella +1
Large Language Models (LLMs) have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations via instruction-tuning methods. However, it is…
RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis
Anku Rani, Aparna Garimella, Apoorv Saxena +2
Data visualizations like charts are fundamental tools for quantitative analysis and decision-making across fields, requiring accurate interpretation and mathematical reasoning. The…
SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity
Ishani Mondal, Meera Bharadwaj, Ayush Roy +2
We present SMART-Editor, a framework for compositional layout and content editing across structured (posters, websites) and unstructured (natural images) domains. Unlike prior mode…
Infogen: Generating Complex Statistical Infographics from Documents
Akash Ghosh, Aparna Garimella, Pritika Ramu +2
Statistical infographics are powerful tools that simplify complex data into visually engaging and easy-to-understand formats. Despite advancements in AI, particularly with LLMs, ex…
LegalCore: A Dataset for Event Coreference Resolution in Legal Documents
Kangda Wei, Xi Shi, Jonathan Tong +5
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…
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement
Ishani Mondal, Zongxia Li, Yufang Hou +3
Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and a…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness
Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella +3
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 re…
Towards Region-aware Bias Evaluation Metrics
Angana Borah, Aparna Garimella, Rada Mihalcea
When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced benchmarks that can be used to assess the bias…
Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Text
Pritika Ramu, Aparna Garimella, Sambaran Bandyopadhyay
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 th…
Presentations are not always linear! GNN meets LLM for Document-to-Presentation Transformation with Attribution
Himanshu Maheshwari, Sambaran Bandyopadhyay, Aparna Garimella +1
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 a…
IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
Nihar Ranjan Sahoo, Pranamya Prashant Kulkarni, Narjis Asad +4
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). Existi…
FABLES: Evaluating faithfulness and content selection in book-length summarization
Yekyung Kim, Yapei Chang, Marzena Karpinska +5
While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited ev…
"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters
Yixin Wan, George Pu, Jiao Sun +3
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 recommen…
KNN-LM Does Not Improve Open-ended Text Generation
Shufan Wang, Yixiao Song, Andrew Drozdov +3
In this paper, we study the generation quality of interpolation-based retrieval-augmented language models (LMs). These methods, best exemplified by the KNN-LM, interpolate the LM's…
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Ishani Mondal, Michelle Yuan, Anandhavelu N +5
Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; howe…