Papers by Saleema Amershi
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. |
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions (2023.acl-long)
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| Challenge: | Large language models (LLMs) can be used to generate text data for training and evaluating other models. |
| Approach: | They propose to use logit suppression and temperature sampling to diversify text generation but at the cost of data accuracy. |
| Outcome: | The proposed approach can increase diversity but at the cost of data accuracy. |