Gopher vs OPT-IML

Discover the cutting-edge advancements in artificial intelligence with DeepMind's exploration of language processing capabilities in AI. At the heart of this exploration is Gopher, a 280-billion-parameter language model designed to understand and generate human-like text. Language serves as the core of human intelligence, enabling us to express thoughts, create memories, and foster understanding.

Realizing its importance, DeepMind's interdisciplinary teams have endeavored to drive the development of language models like Gopher, balancing innovation with ethical considerations and safety. Learn how these language models are advancing AI research by enhancing performance in tasks ranging from reading comprehension to fact-checking while identifying limitations such as logical reasoning challenges. Attention is also given to the potential ethical and social risks associated with large language models, including the propagation of biases and misinformation, and the steps being taken to mitigate these risks.

The paper titled "OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization" focuses on fine-tuning large pre-trained language models with a technique called instruction-tuning, which has been demonstrated to improve model performance on zero and few-shot generalization to unseen tasks. The main challenge addressed in the study is grasping the performance trade-offs due to different decisions made during instruction-tuning, such as task sampling strategies and fine-tuning objectives.

The authors introduce the OPT-IML Bench—a comprehensive benchmark comprising 2000 NLP tasks from 8 different benchmarks—and use it to evaluate the instruction tuning on OPT models of varying sizes. The resulting instruction-tuned models, OPT-IML 30B and 175B, exhibit significant improvements over vanilla OPT and are competitive with specialized models, further inspiring the release of the OPT-IML Bench framework for broader research use.