Papers by Gagan Bansal
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations (2021.findings-acl)
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| Challenge: | despite interest in explainable AI, there is increasing skepticism as to whether explanations are useful to end-users in downstream applications. |
| Approach: | They conduct user studies to measure whether explanations help users decide when to accept or reject an ODQA system's answer. |
| Outcome: | The proposed study shows that explanations outperform baselines across modalities but the best strategy varies with the modality. |
Navigating Rifts in Human-LLM Grounding: Study and Benchmark (2025.acl-long)
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| Challenge: | Language models excel at following instructions but struggle with collaborative aspects of conversation that humans naturally employ. |
| Approach: | They analyze logs from WildChat, MultiWOZ, and Bing Chat to examine grounding challenges . they propose a benchmark to determine when LLMs fail to initiate grounding . |
| Outcome: | The proposed model predicts interactions that fail to ground with users . the proposed model is based on human-human interactions with humans . |
AUTOGEN STUDIO: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems (2024.emnlp-demo)
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Victor Dibia, Jingya Chen, Gagan Bansal, Suff Syed, Adam Fourney, Erkang Zhu, Chi Wang, Saleema Amershi
| Challenge: | Multi-agent systems are emerging as effective pattern for solving long-running, complex tasks in numerous do- mains. |
| Approach: | They propose a no-code developer tool for rapidly prototyping, debugging, and evaluating multi-agent work flows built upon the AUTOGEN framework. |
| Outcome: | The proposed tool provides an intuitive drag-and-drop UI for agent workflow specification, interactive evaluation and debugging of workflows, and a gallery of reusable agent components. |
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models (2023.findings-acl)
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| Challenge: | Large language models have shown impressive capabilities on code generation tasks. |
| Approach: | They propose a metric that combines functional correctness and syntactic similarity to measure the productivity gains generated by large language models. |
| Outcome: | The proposed model achieves a 14% stronger correlation with value and better represents real-world gains when evaluating and comparing models. |