9 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…