Papers with customization
LAVIS: A One-stop Library for Language-Vision Intelligence (2023.acl-demo)
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| Challenge: | a new open-source library for language-vision research and applications is available for free. |
| Approach: | They introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. |
| Outcome: | The proposed library is open-source and highly extensible and configurable. |
GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization (2025.acl-demo)
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| Challenge: | Recent advances in prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. |
| Approach: | They propose a framework that unifies different methods under a unified, customizable API while delivering highly effective prompts for different tasks. |
| Outcome: | The proposed framework unifies multiple methods under a unified, customizable API while delivering highly effective prompts for different tasks. |
Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)
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| Challenge: | Existing prompt-based methods for event argument extraction rely on discrete and manually-designed prompts that cannot exploit specific context for each example. |
| Approach: | They propose a prompt-based method that introduces soft prompts to facilitate encoding of individual example context and multiple relevant documents to boost EAE. |
| Outcome: | The proposed method extensively evaluates on benchmark datasets to demonstrate its benefits with state-of-the-art performance. |
Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization (2024.findings-acl)
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| Challenge: | Using a set of over 200 WikiHow procedures, we test several simple multi-LLM-agent architectures for customization. |
| Approach: | They propose to use a set of WikiHow procedures to test how-to procedures can be customized by multiple LLMs. |
| Outcome: | The proposed architecture outperforms an end-to-end LLM in the evaluation set of over 200 WikiHow procedures. |