This paper explores the application of Large Language Models (LLMs) like ChatGPT in psychological research. It highlights LLMs’ potential to simulate aspects of human cognition and behavior across various branches…
Continue readingThis academic paper delves into the advancements made by Large Language Models (LLMs) in natural language processing and their extension to other modalities such as speech and vision. It carries…
Continue readingThis research introduces the Emotional chat Model (E-chat), a new dialogue system that comprehends and reacts to emotions carried in speech. Using an emotion embedding extracted by a speech encoder…
Continue readingThe article discusses Auffusion, a Text-to-Audio (TTA) system that leverages the power of diffusion models and large language models. Auffusion adapts Text-to-Image (T2I) diffusion models to the TTA task, improving…
Continue readingThe article discusses the limitations of current vulnerability detection methods in Android applications, like the high number of false positives and limited analysis scope. It suggests that machine learning approaches…
Continue readingThe paper presents ProLab, a unique approach that uses property-level label space to generate robust and interpretable segmentation models. Rather than solely relying on category-specific annotations, ProLab uses descriptive properties…
Continue readingThe paper proposes a language-based programming assistant for industrial robots. The assistant provides interactive dialogues about skills, example use-cases and expected robot behavior. The authors present three alternative model families,…
Continue readingThis paper introduces LLM-ST, a model for speech translation developed from a pre-trained large language model (LLM). The model integrates a speech encoder with the LLM and uses multi-task instruction…
Continue readingThe article presents the Text2Analysis benchmark, a tool developed to fill the gap in advanced analysis like forecasting and chart generation in tabular data analysis. The existing research has primarily…
Continue readingThe article provides an in-depth analysis of In-Context Learning (ICL) and Supervised Fine-Tuning (SFT), two predominant methodologies in machine learning. The authors highlight a common problem with these methods: overconfidence…
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