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Found 5 papers, 1 papers with code
tinyBenchmarks: evaluating LLMs with fewer examples
The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities.
The ICL Consistency Test
Just like the previous generation of task-tuned models, large language models (LLMs) that are adapted to tasks via prompt-based methods like in-context-learning (ICL) perform well in some setups but not in others.
Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning
Finding the best way of adapting pre-trained language models to a task is a big challenge in current NLP.
Curriculum Learning with Adam: The Devil Is in the Wrong Details
We present a number of different case studies with different common hand-crafted and automated CL approaches to illustrate this phenomenon, and we find that none of them outperforms optimisation with only Adam with well-chosen hyperparameters.
Language Modelling as a Multi-Task Problem
In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability.
