Gopher: Ensuring Ethical Considerations and Efficient Retrieval
Introduction:
Language plays a vital role in human intelligence and communication. At DeepMind, we recognize the importance of language processing and communication, both in artificial agents and humans. We believe that powerful language models have the potential to advance AI systems by summarizing information, providing expert advice, and following instructions using natural language. Our research on language models includes the development of Gopher, a 280 billion parameter transformer language model. Gopher demonstrates superior performance in tasks such as reading comprehension and fact-checking. However, we also address the risks associated with large language models, such as biases and misinformation. To mitigate these risks, we propose an improved architecture called RETRO that reduces energy costs and improves interpretability. Our commitment to responsible and transparent research ensures that our language models serve society and contribute to the advancement of science and humanity.
Full Article: Gopher: Ensuring Ethical Considerations and Efficient Retrieval
The Importance of Language in Demonstrating Intelligence
Language plays a vital role in human communication and comprehension, showcasing our intelligence and facilitating mutual understanding. DeepMind, an artificial intelligence (AI) research company, recognizes the significance of language processing and communication both in humans and in AI systems. They believe that developing powerful language models can greatly contribute to the advancement of AI systems.
DeepMind’s Research on Language Models
DeepMind has recently released three papers on language models as part of their interdisciplinary approach to AI research. These papers focus on a 280 billion parameter transformer language model called Gopher, the ethical and social risks associated with large language models, and a new architecture with improved training efficiency.
Gopher – A 280 Billion Parameter Language Model
DeepMind conducted research on transformer language models of various sizes, ranging from 44 million parameters to the largest model, Gopher, with 280 billion parameters. By exploring the strengths and weaknesses of these models, they discovered areas where increasing the scale of a model improves performance, such as reading comprehension and identifying toxic language. However, they also found tasks where model scale does not significantly enhance results, such as logical reasoning and common-sense tasks.
Gopher’s Superior Capabilities
DeepMind’s research revealed that Gopher surpasses existing language models in several key tasks. The model’s performance on the Massive Multitask Language Understanding (MMLU) benchmark demonstrated a significant advancement towards human expert performance compared to previous models. Additionally, when prompted in a dialogue interaction, Gopher exhibited surprising coherence and the ability to discuss complex topics like cell biology. However, the research also identified failure modes, including repetitive responses, biased tendencies, and the propagation of incorrect information.
Ethical and Social Risks from Large Language Models
DeepMind’s second paper addresses the ethical and social risks associated with large language models. They present a comprehensive classification of these risks, encompassing 21 in-depth analyses across six thematic areas. DeepMind emphasizes the importance of considering a broad range of risks, as focusing solely on one risk may exacerbate other problems. The classification framework serves as a foundation for experts and the public to engage in responsible decision-making and develop approaches to mitigate risks.
Efficient Training with Internet-Scale Retrieval
The final paper proposes an improved language model architecture called RETRO (Retrieval-Enhanced Transformer). RETRO utilizes an Internet-scale retrieval mechanism during pre-training, allowing efficient querying for relevant text passages to enhance predictions. This architecture reduces the energy cost of training and enables traceability of model outputs to the sources within the training corpus. RETRO achieves state-of-the-art performance on language modeling benchmarks with significantly fewer parameters than traditional transformers.
Future Directions for DeepMind’s Language Research
These papers lay the groundwork for DeepMind’s future language research, particularly in evaluating and deploying language models in a safe and effective manner. DeepMind recognizes the importance of safe interactions between AI agents and humans, which include natural language explanations, communication for uncertainty reduction, and unpacking complex decisions. They are committed to transparency, acknowledging the limitations of their models, and mitigating identified risks. DeepMind’s multidisciplinary teams, including experts from Language, Deep Learning, Ethics, and Safety, collaborate to develop large language models that benefit society and advance scientific understanding.
Summary: Gopher: Ensuring Ethical Considerations and Efficient Retrieval
Language plays a crucial role in human intelligence and comprehension, enabling communication, expression of ideas, memory creation, and mutual understanding. DeepMind recognizes the significance of language processing and communication in both artificial agents and humans. They believe that developing powerful language models has immense potential in advancing AI systems for summarizing information, providing expert advice, and following instructions using natural language. Three papers released by DeepMind focus on language models, including a study on a 280 billion parameter transformer model called Gopher, an examination of ethical and social risks associated with large language models, and an investigation into a more efficient training architecture called RETRO. DeepMind remains committed to responsible research and addressing potential risks to ensure safe interactions with AI agents.
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