collaborators

6 papers

cs.AI2026

Engineering Systems for Data Analysis Using Interactive Structured Inductive Programming

Shraddha Surana, Ashwin Srinivasan, Michael Bain

Engineering information systems for scientific data analysis presents significant challenges: complex workflows requiring exploration of large solution spaces, close collaboration…

cs.AI2025

Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol

Harshvardhan Mestha, Karan Bania, Shreyas V Sathyanarayana +2

Our interest is in the design of software systems involving a human-expert interacting -- using natural language -- with a large language model (LLM) on data analysis tasks. For co…

cs.AI2025

Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models

Riya Naik, Ashwin Srinivasan, Swati Agarwal +1

Many of us now treat LLMs as modern-day oracles asking it almost any kind of question. However, consulting an LLM does not have to be a single turn activity. But long multi-turn in…

cs.SE2025

Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge

Shraddha Surana, Ashwin Srinivasan, Michael Bain

The IPARC Challenge, inspired by ARC, provides controlled program synthesis tasks over synthetic images to evaluate automatic program construction, focusing on sequence, selection,…

cs.CL2025

An Empirical Study of the Role of Incompleteness and Ambiguity in Interactions with Large Language Models

Riya Naik, Ashwin Srinivasan, Estrid He +1

Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startli…

cs.AI2025

Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting

Emmanuel Aboah Boateng, Cassiano O. Becker, Nabiha Asghar +5

Hand-crafting high quality prompts to optimize the performance of language models is a complicated and labor-intensive process. Furthermore, when migrating to newer, smaller, or we…