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This AI Paper from IBM and MIT Introduces SOLOMON: A Neuro-Inspired Reasoning Network for Enhancing LLM Adaptability in Semiconductor Layout Design

Marktechpost

Fine-tuning involves training LLMs with domain-specific data, but this process is time-intensive and requires significant computational resources. Retrieval-augmented generation ( RAG ) retrieves external knowledge to guide LLM outputs, but it does not fully address challenges related to structured problem-solving.

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning Blog

The evaluation of large language model (LLM) performance, particularly in response to a variety of prompts, is crucial for organizations aiming to harness the full potential of this rapidly evolving technology. Both features use the LLM-as-a-judge technique behind the scenes but evaluate different things.

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Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

AWS Machine Learning Blog

In this blog post, we explore a real-world scenario where a fictional retail store, AnyCompany Pet Supplies, leverages LLMs to enhance their customer experience. We will provide a brief introduction to guardrails and the Nemo Guardrails framework for managing LLM interactions. What is Nemo Guardrails? Heres how we implement this.

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning Blog

Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. You can use supervised fine-tuning (SFT) and instruction tuning to train the LLM to perform better on specific tasks using human-annotated datasets and instructions.

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4 Open-Source Alternatives to OpenAI’s $200/Month Deep Research AI Agent

Marktechpost

OpenDeepResearcher Overview: OpenDeepResearcher is an asynchronous AI research agent designed to conduct comprehensive research iteratively. It utilizes multiple search engines, content extraction tools, and LLM APIs to provide detailed insights. Jina AI for Content Extraction: Extracts and summarizes webpage content.

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An In-Depth Exploration of Reasoning and Decision-Making in Agentic AI: How Reinforcement Learning RL and LLM-based Strategies Empower Autonomous Systems

Marktechpost

LLM-Based Reasoning (GPT-4 Chain-of-Thought) A recent development in AI reasoning leverages LLMs. Task Generalization: While RL agents often require domain-specific rewards, LLM-based reasoners can adapt to diverse tasks simply by providing new instructions or context in natural language. Yet, challenges remain.

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Evaluate conversational AI agents with Amazon Bedrock

AWS Machine Learning Blog

However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that interact with external knowledge sources and tools.