Papers by Saujas Vaduguru
Identifying & Interactively Refining Ambiguous User Goals for Data Visualization Code Generation (2025.emnlp-main)
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| Challenge: | ambiguities in natural language can lead to outputs that seem correct but fail to reflect the speaker’s intent. |
| Approach: | They propose to identify and then resolve ambiguities in natural language and propose metrics to quantify them. |
| Outcome: | The proposed metrics better correlate with human annotations than uncertainty baselines. |
Success and Cost Elicit Convention Formation for Efficient Communication (2026.acl-long)
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| Challenge: | ad hoc conventions allow people to coordinate on short, less costly utterances that are understood using shared conversational context. |
| Approach: | They propose a method to train large multimodal models to form conventions . they use simulated reference games to produce training data . |
| Outcome: | The proposed method reduces message length by up to 41% while increasing success by 15% over the course of the interaction. |
Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMs (2024.acl-short)
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| Challenge: | Existing work on discriminative evaluations of large language models has focused on discrimination, but this paper examines their intention understanding by examining their responses to non-literal utterances. |
| Approach: | They propose a framework to evaluate large language models’ intention understanding by examining their responses to non-literal utterances. |
| Outcome: | The proposed framework compares large language models' responses to human-like expectations and provides nuanced evaluations of their intention understanding. |
mrCAD: Multimodal Communication to Refine Computer-aided Designs (2025.findings-emnlp)
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William P McCarthy, Saujas Vaduguru, Karl D.d. Willis, Justin Matejka, Judith E Fan, Daniel Fried, Yewen Pu
| Challenge: | generative AI excels at creating artifacts in a single turn but can struggle to make precise refinements that match our design intent. |
| Approach: | They propose to use multi-turn interactions to iterate and refine computer-aided designs (CADs) they use text and drawing to communicate with each other over multiple rounds of interaction . |
| Outcome: | mrCAD consists of 6,082 communication games, 15,163 instruction-execution rounds, played between 1,092 pairs of humans. |
Symbolic Planning and Code Generation for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Large language models excel at processing and generating text and code, but lack a grounded task-oriented dialogue system that can handle grounding. |
| Approach: | They propose a modular and interpretable grounded dialogue system that integrates a reader and planner to convert partner utterances into executable code and a symbolic planner to determine the next appropriate response. |
| Outcome: | The proposed system outperforms the existing state-of-the-art on a one-common dialogue task and improves task success in human evaluations from 56% to 69% in the most challenging setting. |