Psychology research has long explored aspects of human personality such as extroversion, agreeableness and emotional stability. Categorizations like the `Big Five' personality traits are commonly used to assess and diagnose personality types. In this work, we explore the question of whether the perceived personality in language models is exhibited consistently in their language generation. For example, is a language model such as GPT2 likely to respond in a consistent way if asked to go out to a party? We also investigate whether such personality traits can be controlled. We show that when provided different types of contexts (such as personality descriptions, or answers to diagnostic questions about personality traits), language models such as BERT and GPT2 can consistently identify and reflect personality markers in those contexts. This behavior illustrates an ability to be manipulated in a highly predictable way, and frames them as tools for identifying personality traits and controlling personas in applications such as dialog systems. We also contribute a crowd-sourced data-set of personality descriptions of human subjects paired with their `Big Five' personality assessment data, and a data-set of personality descriptions collated from Reddit.

When working with the dataset, we noticed an imbalance across the classes, in the train-We apply the vision transformer, a deep machine learning model (Figure 1a) as well as in the provided devel-dataset. The counts build around the attention mechanism, on mel-spectrogram per sample and class are similar in train & devel (c.p. Figure 1a), representations of raw audio recordings. When adding melbased but we noticed slight variations in audio-sample length (number data augmentation techniques and sample-weighting, we of frames, c.p. Figure 1b & 1c). Distributions for train and devel achieve comparable performance on both (PRS and CCS challenge) are comparable while the test dataset has variations in sample tasks of ComParE21, outperforming most single model length regarding the number of very small (<= 0.3 seconds).

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.

The phenomenon of image quality degradation in hazy weather has a negative impact on photography work. The contrast of the image will decrease and the color will shift. Meantime, the texture and edge of objects in the scene will become blurred. As shown in Figure 1, there is an obvious difference between the pixel histograms of hazy and haze-free images. For computer vision tasks such as object detection and image segmentation, low-quality inputs can degrade the performance of the models trained on haze-free images. Therefore, many researchers try to recover high-quality clear scenes from hazy images. Before deep learning was widely used in computer vision tasks, image dehazing algorithms had mainly relied on various prior assumptions [51] Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

ChatGPT, a new artificial intelligence (AI) tool from OpenAI, has caused much amazement and apprehension. An exemplar of generative AI, ChatGPT combines powerful text generation capabilities and state-of-art conversational AI, to startling effect. This is made possible by advances in AI such as language processing, transformer neural networks, and reinforcement learning. ChatGPT has been trained on datasets that have about 500 billion words of text. For comparison, the English language Wikipedia, one of the sources on which it has been trained, totals about 4 billion words.

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Scott Mann had a problem: too many f-bombs. The writer-director had spent production on "Fall," his vertigo-inducing thriller about rock climbers stuck atop a remote TV tower, encouraging the two leads to have fun with their dialogue. That improv landed a whopping 35 "f-cks" in the film, placing it firmly in R-rated territory. But when Lionsgate signed on to distribute "Fall," the studio wanted a PG-13 edit. Sanitizing the film would mean scrubbing all but one of the obscenities. "How do you solve that?" Mann recalled from the glass-lined conference room of his Santa Monica office this October, two months after the film's debut.