Education
Understanding Emergent Abilities of Language Models from the Loss Perspective
In this paper, we propose to study emergent abilities in the lens of pre-training loss, instead of model size or training compute.
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
We give a model of how to infer natural language rules by doing experiments.
Appendix
Moreover, there remains a considerable gap between the ability to answer exam questions and the application of this knowledge in real-world situations. To bridge the gap and thoroughly assess LLMs in supporting the crop science field, we introduce CROP.
Empowering and Assessing the Utility of Large Language Models in Crop Science
The CROP benchmark includes 5,045 multiple-choice questions covering three difficulty levels.
Predictive Attractor Models
The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best
