The structure of naming systems in natural languages hinges on a trade-off between high informativeness and low complexity. Prior work capitalizes on information theory to formalize these notions; however, these studies generally rely on two simplifications: (i) optimal listeners, and (ii) universal communicative need across languages. Here, we address these limitations by introducing an information-theoretic framework for discrete object naming systems, and we use it to prove that an optimal trade-off is achievable if and only if the listener's decoder is equivalent to the Bayesian decoder of the speaker. Adopting a referential game setup from emergent communication, and focusing on the semantic domain of kinship, we show that our notion of optimality is not only theoretically achievable but also emerges empirically in learned communication systems.

My dog contributes to climate change. I've been a vegetarian for over a decade. It's not because of my health, or because I dislike the taste of chicken or beef: It's a lifestyle choice I made because I wanted to reduce my impact on the planet. And yet, twice a day, every day, I lovingly scoop a cup of meat-based kibble into a bowl and set it down for my 50-pound rescue dog, a husky mix named Loki. Until recently, I hadn't devoted a huge amount of thought to that paradox.

Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, including patients' posts, comments, and questions to professionals. We investigate how current state-of-the-art large language models (LLMs) perform on this task by exploring different prompting strategies such as chain of thought reasoning, few-shot learning, and self-prompting. With a human-in-the-loop setup, we verify the extracted metaphors and compile the outputs into a corpus named HealthQuote.NL. We believe the extracted metaphors can support better patient care, for example shared decision making, improved communication between patients and clinicians, and enhanced patient health literacy. They can also inform the design of personalized care pathways. We share prompts and related resources at https://github.com/aaronlifenghan/HealthQuote.NL

We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.

Social media, such as Twitter, provides opportunities for caregivers of dementia patients to share their experiences and seek support for a variety of reasons. Availability of this information online also paves the way for the development of internet-based interventions in their support. However, for this purpose, tweets written by caregivers of dementia patients must first be identified. This paper demonstrates our system for the SMM4H 2025 shared task 3, which focuses on detecting tweets posted by individuals who have a family member with dementia. The task is outlined as a binary classification problem, differentiating between tweets that mention dementia in the context of a family member and those that do not. Our solution to this problem explores large language models (LLMs) with various prompting methods. Our results show that a simple zero-shot prompt on a fine-tuned model yielded the best results. Our final system achieved a macro F1-score of 0.95 on the validation set and the test set. Our full code is available on GitHub.

We use generative agents powered by large language models (LLMs) to simulate a social network in a shared residential building, driving the temperature decisions for a central heating system. Agents, divided into Family Members and Representatives, consider personal preferences, personal traits, connections, and weather conditions. Daily simulations involve family-level consensus followed by building-wide decisions among representatives. We tested three personality traits distributions (positive, mixed, and negative) and found that positive traits correlate with higher happiness and stronger friendships. Temperature preferences, assertiveness, and selflessness have a significant impact on happiness and decisions. This work demonstrates how LLM-driven agents can help simulate nuanced human behavior where complex real-life human simulations are di fficult to set. Introduction Social network simulations are widely utilized to model the interactions between people, often relying on agent-based modeling to represent the relationship between people and their environment. In these simulations, the actions of the agents are selected from a predefined set of rules specified by the modeler. While this rule-based approach allows for a clear definition of the decision process and provides control over the outcomes, it also introduces a limitation. The predefined rules may not be able to model various dimensions of human behavior, such as irrational decision-making, and restrict the knowledge of agents to what is encoded by the modeler. Large language models (LLMs) are trained on a vast amount of data, mostly obtained from web pages (Wang et al., 2024). Learning from this human-generated data allows the models to have a level of real-world knowledge and reduces the amount of external information that is required to be given to perform various tasks. Moreover, with their abilities such as reasoning and role-playing, LLMs have previously been shown to have significant capabilities of simulating human-like behavior. Generative agents, as introduced in Park et al. (2023), rely on LLMs to generate agent behaviors, based on agent-specific memory about the agent's identity, interactions with the other agents, and the environment.

Breakthroughs, discoveries, and DIY tips sent every weekday. Just as birth rates decline in many wealthy and developed nations, dog parenting is remaining steady and even gaining in popularity. Up to half of households in Europe and 66 percent of homes in the United States have at least one dog and these pets are often regarded as a family member or "fur baby." To dig into what this shift says about our society, researchers from Eötvös Loránd University in Budapest, Hungary conducted a literature review to analyze the data. They propose that while dogs do not replace children, they can offer a chance to fulfill an innate nurturing drive similar to parenting, but with fewer demands than raising biological children.

For millions of people, WhatsApp is a vital connection to friends and family around the world. But cybersecurity experts have issued a fresh warning over an insidious scam which has already duped users out of almost half a million pounds since the start of 2025. In the so-called'Hi Mum' scam, criminals impersonate a family member to trick their victims into sending them money. Now, fraudsters are even using AI voice impersonation technology to dupe their victims. The scam begins by sending a WhatsApp message saying'Hi Mum' or'Hi Dad' as the sender claims they have lost their phone and have been locked out of their bank account.

Large language models (LLMs) have become a disruptive force in the industry, introducing unprecedented capabilities in natural language processing, logical reasoning and so on. However, the challenges of knowledge updates and hallucination issues have limited the application of LLMs in medical scenarios, where retrieval-augmented generation (RAG) can offer significant assistance. Nevertheless, existing retrieve-then-read approaches generally digest the retrieved documents, without considering the timeliness, authoritativeness and commonality of retrieval. We argue that these approaches can be suboptimal, especially in real-world applications where information from different sources might conflict with each other and even information from the same source in different time scale might be different, and totally relying on this would deteriorate the performance of RAG approaches. We propose PolyRAG that carefully incorporate judges from different perspectives and finally integrate the polyviews for retrieval augmented generation in medical applications. Due to the scarcity of real-world benchmarks for evaluation, to bridge the gap we propose PolyEVAL, a benchmark consists of queries and documents collected from real-world medical scenarios (including medical policy, hospital & doctor inquiry and healthcare) with multiple tagging (e.g., timeliness, authoritativeness) on them. Extensive experiments and analysis on PolyEVAL have demonstrated the superiority of PolyRAG.

As a retiree, I want to stay close to my grandkids. I worry that not learning how to use AI will leave me behind. What's the easiest tool for me to learn, and should I be worried? I promise that you do not need to learn how to use a generative AI tool like ChatGPT or Claude to ensure your grandkids see you as a relevant, informed person. If anything, I would say that our culture has tipped over the past year to generally oppose the use of generative AI tools due to their outsize environmental impact, ethical concerns over their data scraping, and general sludginess of the outputs.