LBC: Language-Based-Classifier for Out-Of-Variable Generalization

Large Language Models (LLMs) have great success in natural language processing tasks such as response generation. However, their use in tabular data has been limited due to their inferior performance compared to traditional machine learning models (TMLs) such as XGBoost. We find that the pre-trained knowledge of LLMs enables them to interpret new variables that appear in a test without additional training, a capability central to the concept of Out-of-Variable (OOV). From the findings, we propose a Language-Based-Classifier (LBC), a classifier that maximizes the benefits of LLMs to outperform TMLs on OOV tasks. LBC employs three key methodological strategies: 1) Categorical changes to adjust data to better fit the model's understanding, 2) Advanced order and indicator to enhance data representation to the model, and 3) Using verbalizer to map logit scores to classes during inference to generate model predictions. These strategies, combined with the pre-trained knowledge of LBC, emphasize the model's ability to effectively handle OOV tasks. We empirically and theoretically validate the superiority of LBC. LBC is the first study to apply an LLM-based model to OOV tasks. The source code is at https://github.com/sksmssh/LBCforOOVGen

Audio Mosaicing with Simulation-based Inference

Mosaics and collages have been an integral part of art for decades. Particularly important in contemporary media art is the audio mosaic, in which an artist manually combines several audio sources in order to construct one single coherent sound, combining elements from disparate sources. Here we propose an algorithm to automatically create audio mosaics using the simulation-based inference paradigm. Our algorithm takes as input an audio file that one wishes to approximate, and a list of audio files one can use for approximation, finding a posterior distribution from which one can sample reconstructions of the original audio file, using the sources in an interpretable and disentangled manner. We validate our approach by creating an audio mosaic which reconstructs the sound of a traditional Korean funeral using 100 K-pop songs rearranged and overlapped.