2024-08-30

Generative AI for Assisting Software Developers

LLM-3D Print: Large Language Models To Monitor and Control 3D Printing

Relevance: This paper proposes a framework using LLMs for monitoring and controlling 3D printing processes, which is directly applicable to software development. LLMs can be used to analyze data from 3D printers and identify potential issues in the printing process, thus assisting developers in identifying and resolving bugs in their code.

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SWE-bench-java: A GitHub Issue Resolving Benchmark for Java

Relevance: This paper introduces SWE-bench-java, a benchmark for evaluating the ability of LLMs to resolve GitHub issues in Java code. This aligns with the topic of Generative AI for assisting software developers as LLMs can be used to help developers understand and resolve issues in their code.

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Prompt Engineering Techniques

Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature

Relevance: This paper explores the use of LLMs to create a knowledge navigation framework for scientific literature. Prompt engineering plays a key role in designing effective prompts to guide the LLM in organizing and structuring scientific information. The paper suggests using prompt engineering techniques to enhance the search experience for users.

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Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models

Relevance: This paper proposes a framework for enhancing the task expertise of LLMs using open knowledge. It specifically focuses on the role of prompt engineering in selecting relevant models and instructions for specific tasks. The paper highlights the importance of crafting effective prompts to guide the LLM towards achieving desired outcomes.

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Human-in-the-loop Machine Learning

K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences

Relevance: This paper presents K-Sort Arena, a platform for benchmarking generative models using human preferences. This aligns with human-in-the-loop machine learning as it involves incorporating human feedback into the evaluation process. The platform uses K-wise comparisons, where human users compare multiple model outputs to provide more informative feedback.

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Generative AI for UI Design and Engineering

Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation

Relevance: This paper proposes an approach for interactive 3D layout control in diffusion-based image generation. It leverages 3D boxes for object placement and allows users to interactively modify the scene layout. This method has potential implications for UI design and engineering as it could be used to generate 3D prototypes or interactive mockups of user interfaces.

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Techniques for Explaining AI behavior

LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation

Relevance: This paper introduces a framework for distilling knowledge from a large multimodal language model (LLaVA) into a smaller one. While the paper doesn’t directly focus on explainability, the use of Mixture of Experts (MoE) architecture in the smaller model makes it more transparent by revealing the contributions of different experts to the model’s outputs. This approach can facilitate understanding of the model’s decision-making process.

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MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement

Relevance: This paper proposes a method for generating novel views of human images using a 3D-aware diffusion model. While not directly addressing explainability, the paper highlights the use of the SMPL-X model for 3D body representation, which offers a more interpretable and explicit representation of the human body compared to black-box approaches. This can aid in understanding the model’s reasoning.

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MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

Relevance: This paper introduces a benchmark for evaluating multimodal LLMs in challenging real-world scenarios. By providing a dataset with annotations from human experts, this benchmark facilitates a better understanding of the model’s strengths and weaknesses in specific tasks. This can be valuable for identifying biases and limitations of the model and improving its explainability.

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