41 citations · 53 across the 9 of their papers we have counts for
9 papers
On Improving Faithfulness of Podcasts from Documents
Soumya Dutta, Tejas Indulal Dhamecha, Pannaga Shivaswamy
Large language models (LLMs) are increasingly used to generate long-form conversational content such as podcasts from textual sources. While these systems produce fluent and engagi…
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…
CATTO: Balancing Preferences and Confidence in Language Models
Nisarg Parikh, Ananya Sai, Pannaga Shivaswamy +2
Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequent…
What is in a name? Mitigating Name Bias in Text Embeddings via Anonymization
Sahil Manchanda, Pannaga Shivaswamy
Text-embedding models often exhibit biases arising from the data on which they are trained. In this paper, we examine a hitherto unexplored bias in text-embeddings: bias arising fr…
POSIT: Promotion of Semantic Item Tail via Adversarial Learning
Qiuling Xu, Pannaga Shivaswamy, Xiangyu Zhang
In many recommendations, a handful of popular items (e.g., movies / television shows, news, etc.) can be dominant in recommendations for many users. However, we know that in a larg…
ExCalibR: Expected Calibration of Recommendations
Pannagadatta Shivaswamy
In many recommender systems and search problems, presenting a well balanced set of results can be an important goal in addition to serving highly relevant content. For example, in…
Online Structured Prediction via Coactive Learning
Pannaga Shivaswamy, Thorsten Joachims
We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the us…
Online Learning with Preference Feedback
Pannagadatta K. Shivaswamy, Thorsten Joachims
We propose a new online learning model for learning with preference feedback. The model is especially suited for applications like web search and recommender systems, where prefere…
Large-Margin Learning of Submodular Summarization Methods
Ruben Sipos, Pannaga Shivaswamy, Thorsten Joachims
In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multi-document summarization. By taking a structured predicition ap…