Page 23: Research news on Large language models

Large language models are high-capacity neural sequence models trained on massive text and multimodal corpora to perform language understanding, generation, and reasoning. Current work examines their internal representations, cognitive and social behavior analogies to humans, and limitations in mathematical, causal, and strategic reasoning. Research also addresses alignment with human values and brain activity, safety and security vulnerabilities, privacy and de-anonymization risks, cross-lingual and sociocultural biases, scaling and efficiency laws, and frameworks for tool use, multi-agent interaction, and domain-specific deployment.

Computer Sciences

Making AI-generated code more accurate in any language

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers' lives easier if that code follows the rules of the programming language and doesn't cause ...

Computer Sciences

Training LLMs to self-detoxify their language

As we mature from childhood, our vocabulary—as well as the ways we use it—grows, and our experiences become richer, allowing us to think, reason, and interact with others with specificity and intention. Accordingly, our word ...

Machine learning & AI

Small model approach could be more effective than LLMs

Small language models are more reliable and secure than their large counterparts, primarily because they draw information from a circumscribed dataset. Expect to see more chatbots running on these slimmed-down alternatives ...

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