Uttaran Bhattacharya


2024

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Evaluation and Continual Improvement for an Enterprise AI Assistant
Akash Maharaj | Kun Qian | Uttaran Bhattacharya | Sally Fang | Horia Galatanu | Manas Garg | Rachel Hanessian | Nishant Kapoor | Ken Russell | Shivakumar Vaithyanathan | Yunyao Li
Proceedings of the Fifth Workshop on Data Science with Human-in-the-Loop (DaSH 2024)

The development of conversational AI assistants is an iterative process with many components involved. As such, the evaluation and continual improvement of these assistants is a complex and multifaceted problem. This paper introduces the challenges in evaluating and improving a generative AI assistant for enterprise that is under active development and how we address these challenges. We also share preliminary results and discuss lessons learned.

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TAME-RD: Text Assisted Replication of Image Multi-Adjustments for Reverse Designing
Pooja Guhan | Uttaran Bhattacharya | Somdeb Sarkhel | Vahid Azizi | Xiang Chen | Saayan Mitra | Aniket Bera | Dinesh Manocha
Findings of the Association for Computational Linguistics ACL 2024

Given a source and its edited version performed based on human instructions in natural language, how do we extract the underlying edit operations, to automatically replicate similar edits on other images? This is the problem of reverse designing, and we present TAME-RD, a model to solve this problem. TAME-RD automatically learns from the complex interplay of image editing operations and the natural language instructions to learn fully specified edit operations. It predicts both the underlying image edit operations as discrete categories and their corresponding parameter values in the continuous space.We accomplish this by mapping together the contextual information from the natural language text and the structural differences between the corresponding source and edited images using the concept of pre-post effect. We demonstrate the efficiency of our network through quantitative evaluations on multiple datasets. We observe improvements of 6-10% on various accuracy metrics and 1.01X-4X on the RMSE score and the concordance correlation coefficient for the corresponding parameter values on the benchmark GIER dataset. We also introduce I-MAD, a new two-part dataset: I-MAD-Dense, a collection of approximately 100K source and edited images, together with automatically generated text instructions and annotated edit operations, and I-MAD-Pro, consisting of about 1.6K source and edited images, together with text instructions and annotated edit operations provided by professional editors. On our dataset, we observe absolute improvements of 1-10% on the accuracy metrics and 1.14X–5X on the RMSE score.