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7 de may. de 2024 · LaMDA (or Language Model for Dialogue Applications) is a computer program that uses artificial intelligence to generate real conversations, just as if it were a human. A bit like any modern conversational agent (chatbots). But unlike “classic” chatbots, LaMDA is based on the most advanced linguistic
18 de abr. de 2024 · From r to Q∗: Your Language Model is Secretly a Q-Function. Reinforcement Learning From Human Feedback (RLHF) has been a critical to the success of the latest generation of generative AI models. In response to the complex nature of the classical RLHF pipeline, direct alignment algorithms such as Direct Preference Optimization (DPO ...
1 de may. de 2024 · Updated on May 1, 2024. By. Alex McFarland and Antoine Tardif. In the rapidly evolving world of artificial intelligence (AI), Large Language Models (LLMs) have emerged as a cornerstone, driving innovations and reshaping the way we interact with technology.
Hace 3 días · Lamda (Language Model for Dialogue Applications) is a family of LLMs developed by Google Brain announced in 2021. Lamda used a decoder-only transformer language model and was pre-trained on a large corpus of text. In 2022, LaMDA gained widespread attention when then-Google engineer Blake Lemoine went public with claims that the ...
23 de abr. de 2024 · Bard's story began in 2021 with the unveiling of LaMDA (Language Model for Dialogue Applications) by Google AI. LaMDA focused on generating natural and engaging conversation, a...
29 de abr. de 2024 · This paper introduces a novel framework for generating intent-oriented dialogues, leveraging the commonsense knowledge of large language models (LLMs). Our proposed SalesBot 2.0 dataset encompasses thousands of human-like dialogues, demonstrating smoother transitions, improved naturalness, and enhanced consistency compared to ...
21 de abr. de 2024 · Tailored for dialogue applications, the Llama 3 Instruct model has been significantly enhanced, incorporating over 10 million human-annotated data samples and leveraging a sophisticated mix of training methods such as supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct policy optimization ...
