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Large Language Model

This AI study from China introduces CREAM (Continuity-Relativity indExing with gAussian Middle), a streamlined but potent AI approach designed to broaden the context of extensive language models.

Pre-trained Large language models (LLMs), such as transformers, typically have a fixed context window size, most commonly around 4K tokens. Nevertheless, numerous applications require processing significantly longer contexts, going all the way up to 256K tokens. The challenge that arises in elongating the context length of these models lies primarily in the efficient use of…

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What does the future hold for Artificial Intelligence (AI), given the existence of 700,000 advanced language models on Hugging Face?

The proliferation of Large Language Models (LLMs) in the field of Artificial Intelligence (AI) has been a topic of much debate on Reddit. In a post, a user highlighted the existence of over 700,000 LLMs, raising questions about the usefulness and potential of these models. This has sparked a broad debate about the consequences of…

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Galileo Unveils Luna: A Comprehensive Evaluation Framework for Detecting Language Model Inconsistencies with Outstanding Precision and Economy

The Galileo Luna is a transformative tool in the evaluation of language model processes, specifically addressing the prevalence of hallucinations in large language models (LLMs). Hallucinations refer to situations where models generate information that isn’t specific to a retrieved context, a significant challenge when deploying language models in industry applications. Galileo Luna combats this issue…

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ObjectiveBot: An AI Structure Aiming to Improve Skills of an LLM-based Agent for Accomplishing Superior Objectives.

Large language models (LLMs), such as those used in AI, can creatively solve complex tasks in ever-changing environments without the need for task-specific training. However, achieving broad, high-level goals with these models remain a challenge due to the objectives' ambiguous nature and delayed rewards. Frequently retraining models to fit new goals and tasks is also…

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Scientists from Stanford university and Duolingo have illustrated effective methods for achieving a specified proficiency level using proprietary models like GPT4 and open-source methods.

A team from Stanford and Duolingo has proposed a new way to manage the proficiency level in texts generated by large language models (LLMs), overcoming limitations in current methods. The Common European Framework of Reference for Languages (CEFR)-aligned language model (CALM) combines techniques of finetuning and proximal policy optimization (PPO) for aligning the proficiency levels…

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Gretel AI has launched a fresh Synthetic Financial Dataset on HuggingFace 🤗 that caters to AI developers. It is multilingual and designed to aid in detecting personally identifiable information (PII).

Detecting personally identifiable information (PII) in documents can be a complex task due to numerous regulations like the EU's GDPR and multiple U.S. data protection laws. A flexible approach is needed given the variations in data formats and domain-specific requirements. In response, Gretel has developed a synthetic dataset to help with PII detection. Gretel's Navigator tool…

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Enhancing Safety Measures in Extensive Language Models (LLMs)

Artificial intelligence (AI) alignment strategies, such as Direct Preference Optimization (DPO) and Reinforcement Learning with Human Feedback (RLHF) combined with supervised fine-tuning (SFT), are essential for the safety of Large Language Models (LLMs). They work to modify these AI models to reduce the chance of hazardous interactions. However, recent research has uncovered significant weaknesses in…

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