2024-11-01

Generative AI for Assisting Software Developers

Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists

Relevance: This paper directly explores the use of LLMs for assisting software developers, specifically focusing on the task of feature engineering. It proposes a benchmark for evaluating LLMs in this domain, demonstrating their potential for automating tasks that traditionally require human expertise.

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REPOCOD Says ‘Not Yet’

Relevance: This paper investigates the limitations of current LLMs in real-world software development scenarios. It proposes a new benchmark called REPOCOD, which is designed to evaluate LLMs’ ability to generate code that requires file-level or repository-level context information, revealing their current limitations in tackling complex development tasks.

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AI Agents

OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization

Relevance: This paper introduces an open-source framework for developing multimodal web agents that can autonomously explore the real world, collect feedback, and improve their performance over time. This aligns with the goals of AI Agent research by creating agents capable of perceiving their environment, learning, and adapting to achieve user-defined goals.

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AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant

Relevance: This paper presents AgentStore, a platform for dynamically integrating heterogeneous agents for automating computer tasks. This platform addresses the challenge of creating AI Agents that can handle diverse and complex tasks by combining the capabilities of various specialized agents, potentially leading to more robust and adaptable agents.

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VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks

Relevance: This paper introduces VideoWebArena, a benchmark designed to evaluate the capabilities of long-context multimodal agents for video understanding. This benchmark contributes to the AI Agent research by providing a realistic and challenging environment for testing the ability of agents to understand and act upon video information, which is crucial for interacting with the real world.

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Prompt Engineering Techniques

SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization

Relevance: This paper explores prompt engineering techniques for improving the reasoning capabilities of LLMs in the context of social relation reasoning. It introduces a novel approach called Greedy Segment Prompt Optimization (GSPO), which aims to automatically optimize prompts for LLMs by performing a greedy search at the segment level.

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Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

Relevance: This paper investigates the effectiveness of chain-of-thought prompting across various tasks, particularly those where explicit reasoning can hinder performance. It provides insights into the nuanced relationship between prompt engineering techniques and the task domain, highlighting the need for careful consideration when employing these techniques.

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Human-in-the-loop Machine Learning

Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Relevance: This paper presents a human-in-the-loop reinforcement learning system for training robotic manipulation skills. The system incorporates human demonstrations and corrections to improve the robot’s performance. This work exemplifies the use of human feedback to enhance AI capabilities and demonstrates the potential for human-in-the-loop learning in robotics.

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Accelerating Direct Preference Optimization with Prefix Sharing

Relevance: This paper introduces a novel technique called prefix sharing for accelerating Direct Preference Optimization (DPO), a human-in-the-loop learning method that leverages human preferences to fine-tune models. By optimizing the computational efficiency of DPO, this work makes it more accessible and practical for various applications.

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Techniques for Explaining AI behavior

Analysing the Residual Stream of Language Models Under Knowledge Conflicts

Relevance: This paper explores the residual stream of language models, which provides insights into their internal workings and decision-making processes. By analyzing this stream, the authors demonstrate the ability to identify knowledge conflicts and predict the model’s reliance on different sources of information, contributing to the development of more transparent and interpretable AI.

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