As the utilization of language models in interdisciplinary, human-centered studies grow, the expectation of model capabilities continues to evolve. Beyond excelling at conventional tasks, models are recently expected to perform well on user-centric measurements involving confidence and human (dis)agreement -- factors that reflect subjective preferences. While modeling of subjectivity plays an essential role in cognitive science and has been extensively studied, it remains under-explored within the NLP community. In light of this gap, we explore how language models can harness subjectivity by conducting comprehensive experiments and analysis across various scenarios using both fine-tuned models and prompt-based large language models (LLMs). Our quantitative and qualitative experimental results indicate that existing post-hoc calibration approaches often fail to produce satisfactory results. However, our findings reveal that personality traits and demographical information are critical for measuring subjectivity. Furthermore, our in-depth analysis offers valuable insights for future research and development in the interdisciplinary studies of NLP and cognitive science.

In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLMs). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual's unique habits and preferences. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.

The Metaverse And NFTs: If the Metaverse is the door to the unique realm of experiences, NFTs are the exclusive keys to that door. These keys are increasingly becoming inevitable for exploring the limitless territories of exciting and personalised digital experiences. With the rising popularity of NFTs, their intrinsic nature makes them the DNA certification for our society. The concept of Metaverse is one of the pillars that are leveraged by the presence of NFTs, playing a pivotal role in building the digital twin of our society. Metaverse can be considered to be an inspiration behind the architectural bedrock of decentralised and interoperable space where real, online, and every kind of experience that was once conceived within the bounds of a science fiction.

Daimler has announced its self-driving'friendly' electric Smart car, geared toward urban ride sharing, will debut at next month's Frankfurt Motor Show in Germany. The car, dubbed the Smart Vision EQ ForTwo, is a fully-autonomous vehicle with no steering wheel or pedals that riders can hail with an app, much like an Uber taxi. Passengers can then pick fellow riders based on their mutual interests, and the car will even suggest topics of conversation to stop things getting awkward. The outside of the car sports a panel on the front that displays welcome messages, destinations, who it is picking up and even warnings to other cars and pedestrians. Daimler has announced its self-driving electric Smart car (pictured), geared toward urban car sharing, will debut at next month's Frankfurt Motor Show in Germany The vehicle's side doors open upwards over the rear axle and will display news and weather updates.

Bengaluru: Last year, for the first time ever, an international beauty contest was judged by machines. Thousands of people from across the world submitted their photos to Beauty.AI, hoping that their faces would be selected by an advanced algorithm free of human biases, in the process accurately defining what constitutes human beauty. In preparation, the algorithm had studied hundreds of images of past beauty contests, training itself to recognize human beauty based on the winners. But what was supposed to be a breakthrough moment that would showcase the potential of modern self-learning, artificially intelligent algorithms rapidly turned into an embarrassment for the creators of Beauty.AI, as the algorithm picked the winners solely on the basis of skin colour. "The algorithm made a fairly non-trivial correlation between skin colour and beauty. A classic example of bias creeping into an algorithm," says Nisheeth K. Vishnoi, an associate professor at the School of Computer and Communication Sciences at Switzerland-based École Polytechnique Fédérale de Lausanne (EPFL).