Africa
D: Supplementary Materials Table of Contents
Scores are calculated by giving a weight of 1 for applicable, 0.5 for conditionally applicable, and 0 for incorrect responses.
D: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
D comprises 52.6k question-answer pairs from 16 countries/regions,
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
Transformers can efficiently learn in-context from example demonstrations.
Many-Shot In-Context Learning Rishabh Agarwal, A vi Singh
LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.
Efficient Leverage Score Sampling for Tensor Train Decomposition
In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling.