1 citations · 1 across the 10 of their papers we have counts for
10 papers
Language Models Encode the Contextual Truth of Propositions
Rupak Sarkar, Pritika Ramu, Rachel Rudinger
Prior work has shown that LLMs encode the truth of factual propositions along linear directions in activation space. It's unclear how these representations extend to contextual tru…
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…
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…
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…
StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer
Pritika Ramu, Apoorv Saxena, Meghanath M Y +2
Adapting LLMs to specific stylistic characteristics, like brand voice or authorial tones, is crucial for enterprise communication but challenging to achieve from corpora which lack…
Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
Pritika Ramu, Koustava Goswami, Apoorv Saxena +1
Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely un…
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…
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…
RE: Region-Aware Relation Extraction from Visually Rich Documents
Pritika Ramu, Sijia Wang, Lalla Mouatadid +2
Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout s…