2025-01-31

Generative AI for Assisting Software Developers

CodeMonkeys: Scaling Test-Time Compute for Software Engineering

Relevance: This paper directly addresses using LLMs to assist software developers by focusing on solving real-world GitHub issues. It explores scaling test-time compute to improve LLM capabilities in code editing and bug fixing. The system, CodeMonkeys, iteratively generates and tests code edits, showcasing a practical application of generative AI in software development, aligning with the topic’s focus on code completion, bug detection, and code refactoring.

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AI Agents

EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents

Relevance: This paper introduces EmbodiedEval, a benchmark specifically designed to evaluate multimodal LLMs as embodied agents. The benchmark covers various tasks in 3D environments, directly addressing the challenges and capabilities of AI agents interacting with their surroundings. Its focus on interaction, navigation, and object manipulation aligns perfectly with the core concepts of AI agent research.

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Prompt Engineering Techniques

OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas

Relevance: This paper explores techniques for creating customizable role-playing LLMs, a task heavily reliant on prompt engineering. The method uses synthetic personas to create character-aligned instructional responses, directly impacting how prompts are designed to elicit specific behaviors from the model. The ‘role-playing’ aspect is a direct example of prompt engineering techniques.

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Are Vision Language Models Texture or Shape Biased and Can We Steer Them?

Relevance: This paper investigates biases in Vision Language Models (VLMs) and how these biases can be steered through prompting. It directly explores how different prompts affect the model’s output, demonstrating a key aspect of prompt engineering—controlling model behavior through careful prompt design. The ability to steer bias through prompting is a crucial element of effective prompt engineering.

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Human-in-the-loop Machine Learning

Improving Video Generation with Human Feedback

Relevance: This paper explicitly uses human feedback to improve video generation models. It builds a large-scale human preference dataset and incorporates this feedback into a reinforcement learning framework to refine the model’s performance. The use of human preferences to train a reward model is a clear example of reinforcement learning from human feedback, a core concept in human-in-the-loop ML.

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Techniques for Explaining AI Behavior

Open Problems in Mechanistic Interpretability

Relevance: This paper discusses open problems in mechanistic interpretability, a subfield of Explainable AI (XAI). It focuses on understanding the internal workings of neural networks to achieve better transparency and interpretability. Addressing these problems is crucial for developing techniques that explain AI behavior, a key goal of XAI research.

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