ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned Language Models

In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as BART, ConvBERT, and GPT-2. The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.

AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes

We introduce AnyHome, a framework that translates open-vocabulary descriptions, ranging from simple labels to elaborate paragraphs, into well-structured and textured 3D indoor scenes at a house-scale. Inspired by cognition theories, AnyHome employs an amodal structured representation to capture 3D spatial cues from textual narratives and then uses egocentric inpainting to enrich these scenes. To this end, we begin by using specially designed template prompts for Large Language Models (LLMs), which enable precise control over the textual input. We then utilize intermediate representations to maintain the spatial structure's consistency, ensuring that the 3D scenes align closely with the textual description. Then, we apply a Score Distillation Sampling process to refine the placement of objects. Lastly, an egocentric inpainting process is incorporated to enhance the realism and appearance of the scenes. AnyHome stands out due to its hierarchical structured representation combined with the versatility of open-vocabulary text interpretation. This allows for extensive customization of indoor scenes at various levels of granularity. We demonstrate that AnyHome can reliably generate a range of diverse indoor scenes, characterized by their detailed spatial structures and textures, all corresponding to the free-form textual inputs.