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Agent-As-A-Judge: Evaluate Agents With Agents
The paper titled “Agent-As-A-Judge: Evaluate Agents With Agents” addresses a critical challenge in the field of Artificial Intelligence (AI) concerning the evaluation methodologies for agentic systems.
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Agent S: An Open Agentic Framework
The rapid advancement of technology has significantly transformed human-computer interaction (HCI), leading to the development of autonomous agents capable of performing complex tasks. These agents are designed to enhance user experience by automating repetitive and intricate processes, thereby improving efficiency and accessibility.
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LightRAG: Simple And Fast Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to enhance the capabilities of large language models (LLMs) by integrating external knowledge sources. This integration allows for the generation of contextually relevant responses that are grounded in factual information.
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What Makes In-Context Learning Work?
In recent years, large language models (LMs) have demonstrated remarkable capabilities in performing various tasks through a process known as in-context learning. This approach allows models to condition their predictions on a few input-label pairs, or demonstrations, without requiring explicit training or fine-tuning.
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MLE-Bench: Evaluating ML Agents On ML Engineering
The research paper introduces MLE-bench, a novel benchmark designed to evaluate the performance of AI agents in machine learning (ML) engineering tasks. The significance of this research lies in its ability to provide a structured framework for assessing how well AI agents can perform complex tasks that are typically handled by human engineers.
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CLUs Transform LLMs Into Adaptive Reasoners
The research paper explores the limitations of traditional machine learning models, particularly Large Language Models (LLMs), which often rely on static learning paradigms that require extensive retraining to adapt to new information. The authors introduce Composite Learning Units (CLUs) as a novel framework designed to enhance the adaptability and reasoning capabilities of LLMs through continuous…
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Thinking LLMs: General IF With Thought Generation
This research introduces Thinking LLMs, which enhance traditional Large Language Models (LLMs) by incorporating a mechanism for internal thought generation prior to response generation. The proposed Thought Preference Optimization (TPO) methodology enables these models to improve their instruction-following capabilities without the need for additional human data.
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Understanding the Limitations of Reasoning in LLMs
Let’s distill and learn from: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models Abstract This document explores the GSM-Symbolic benchmark, a novel framework designed to evaluate the mathematical reasoning capabilities of Large Language Models (LLMs). By addressing the limitations of traditional benchmarks, this framework provides AI engineers with structured methodologies for enhancing…
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Hallo2: Audio-Driven Portrait Image Animation
The research paper titled “Hallo2: Long-Duration And High-Resolution Audio-Driven Portrait Image Animation” addresses the growing demand for realistic and controllable animations in multimedia applications. The significance of audio-driven portrait animation lies in its potential to enhance user engagement and interactivity in various fields, including entertainment, virtual reality, and personalized content creation.
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Understanding and Mitigating Hallucination in LLMs
This document explores the phenomenon of hallucination in Large Language Models (LLMs), a critical challenge for AI engineers aiming to deploy reliable AI systems. Hallucination refers to the generation of nonsensical or factually incorrect responses, which can undermine trust in AI applications. We present a comprehensive overview of the mechanisms behind hallucination, an experimental framework…
Category: AI Engineering Practice
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