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Found 7 papers, 4 papers with code
Humans and language models diverge when predicting repeating text
In contrast with these findings, we present a scenario in which the performance of humans and LMs diverges.
Large Language Models Based Automatic Synthesis of Software Specifications
As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually.
Synthesizing Programs with Continuous Optimization
In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem.
Selecting Informative Contexts Improves Language Model Fine-tuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI.
Selecting Informative Contexts Improves Language Model Finetuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
This is, in part, due to the emergence of a wide range of novel techniques in machine learning.
