Papers by Victor Dibia
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. |
LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models (2023.acl-demo)
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| Challenge: | Existing research has sought to address these challenges by automating the visualization creation process, given a dataset. |
| Approach: | They propose a tool for generating grammar-agnostic visualizations and infographics using large language models and image generation models that address multiple tasks. |
| Outcome: | The proposed tool can generate grammar-agnostic visualizations and infographics from a dataset and python api. |
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting (2025.naacl-industry)
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Emmanuel Aboah Boateng, Cassiano O Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Soundararajan Srinivasan, Victor Dibia
| Challenge: | Concept Distillation (CD) is an automated prompt optimization technique for enhancing weaker models on complex tasks. |
| Approach: | They propose an automatic prompt optimization technique for enhancing weaker models on complex tasks using a base prompt and a strong model to generate reasons for these mistakes. |
| Outcome: | The proposed technique improves weaker models on NL2Code and mathematical reasoning tasks, while preserving performance. |
Axiomatic Preference Modeling for Longform Question Answering (2023.emnlp-main)
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| Challenge: | Recent advances in large language models have helped bridge the "alignment gap" between the responses of raw pretrained language models and responses that resonate more closely with human preferences. |
| Approach: | They propose to use a axiomatic framework to generate a rich variety of preference signals to uphold these signals. |
| Outcome: | The proposed model outperforms GPT-4 and ChatGPT in preference scoring. |
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
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| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
| Approach: | They propose a library that integrates with existing infrastructure and offers helpful defaults for QA subtasks. |
| Outcome: | NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks. |