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…
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…
Large Language Models (LLMs) like Mistral, Gemma, and Llama have significantly contributed to advancements in Natural Language Processing (NLP), but their dense models make them computationally heavy and expensive. As they utilize every parameter during inference, this intensity makes creating affordable, widespread AI challenging.
Conditional computation is seen as an efficiency-enhancing solution, activating specific model parameters…
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…
Stanford University is renowned for its contributions to artificial intelligence research and advancements, offering numerous courses equipped with practical knowledge for its students. Various AI aspects are covered, including machine learning, deep learning, natural language processing, and other crucial AI technologies. The courses are revered for their depth, relevance, and rigor making them paramount for…
In recent years, comparisons have been made between protein sequences and natural language due to their sequential structures, facilitating notable progress in deep learning models in both areas. Large language models (LLMs), for example, have seen significant success in natural language processing (NLP) tasks, prompting attempts to adapt them to interpret protein sequences.
However, these efforts…
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…
Transformer-based generative Large Language Models (LLMs) are showing significant strength in various Natural Language Processing (NLP) tasks. Among those benefiting are application developers, who interact with LLMs through APIs supplied by AI firms such as Google, OpenAI, and Baidu, who provide language model-as-a-service (LMaaS) platforms.
In the LMaaS scenario, developers send the LLM service user input…
Luma Labs recently unveiled Dream Machine, an advanced AI model built to generate high-quality, realistic, and fantasy videos from text and images. This AI device, which is built on a scalable, multimodal transformer architecture, is a significant step forward in AI technology. It has been specifically designed for video creation and is trained directly on…