2024-08-09

Generative AI for Assisting Software Developers

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases

Relevance: This paper proposes a system that integrates LLMs with graph database interfaces extracted from code repositories. This allows the LLM to perform code navigation, context retrieval, and other tasks that require understanding the structure of entire codebases, which is directly relevant to using generative AI to support software developers.

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Prompt Engineering Techniques

WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models

Relevance: This paper introduces WalledEval, a toolkit for evaluating the safety of LLMs, including their ability to resist various forms of prompt injection attacks. This is directly relevant to prompt engineering, as understanding the vulnerabilities of LLMs to prompt manipulation is crucial for developing safe and reliable prompts.

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StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation

Relevance: This paper proposes StructEval, a framework for evaluating LLM capabilities by conducting structured assessments across multiple cognitive levels and critical concepts. This aligns with prompt engineering research by highlighting the importance of understanding how different prompts and prompt formats affect an LLM’s reasoning capabilities.

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Human-in-the-loop Machine Learning

Self-Taught Evaluators

Relevance: This paper presents an approach to improving LLM evaluators without human annotations, using synthetic training data generated through an iterative process. This is highly relevant to human-in-the-loop machine learning, as it explores methods for training AI systems to evaluate their own performance, reducing the reliance on human input.

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Generative AI for UI Design and Engineering

Fast Sprite Decomposition from Animated Graphics

Relevance: This paper presents an approach for decomposing animated graphics into sprites, which can be used for creating interactive UI elements. This is relevant to generative AI for UI design, as it demonstrates how AI can be used to automate tasks like asset creation for UI development.

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MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization

Relevance: This paper introduces MeshAnything V2, a model that generates artist-created meshes aligned to given shapes, which can be used for creating 3D UI elements. This is relevant to generative AI for UI design as it explores how AI can be used for generating high-quality 3D assets for UI development.

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Techniques for Explaining AI behavior

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models

Relevance: This paper proposes using board game models to evaluate the quality of sparse autoencoders (SAEs) trained to disentangle interpretable features in LLM representations. This is relevant to explainable AI, as it explores methods for understanding the latent features learned by LLMs.

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