Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods. While computational models have been very successful at generating testable hypotheses about the effects of several factors, such as population structure or transmission biases, some phenomena have so far been more complex to capture using agent-based and formal models. This is in particular the case for the effect of the transformations of social information induced by evolved cognitive mechanisms. We here propose that leveraging the capacity of Large Language Models (LLMs) to mimic human behavior may be fruitful to address this gap. On top of being an useful approximation of human cultural dynamics, multi-agents models featuring generative agents are also important to study for their own sake. Indeed, as artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed. The software we developed for conducting these simulations is open-source and features an intuitive user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.

The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for Artificial Intelligence, introducing Large Language Models (LLMs) to the mainstream and setting new records in user adoption. LLMs, particularly ChatGPT, trained on extensive internet data, demonstrate remarkable conversational capabilities across various domains, suggesting a significant impact on the workforce. However, these models are susceptible to errors - "hallucinations" and omissions, generating incorrect or incomplete information. This poses risks especially in contexts where accuracy is crucial, such as legal compliance, medicine or fine-grained process frameworks. There are both technical and human solutions to cope with this isse. This paper explores the human factors that enable users to detect errors in LLM outputs, a critical component in mitigating risks associated with their use in professional settings. Understanding these factors is essential for organizations aiming to leverage LLM technology efficiently, guiding targeted training and deployment strategies to enhance error detection by users. This approach not only aims to optimize the use of LLMs but also to prevent potential downstream issues stemming from reliance on inaccurate model responses. The research emphasizes the balance between technological advancement and human insight in maximizing the benefits of LLMs while minimizing the risks, particularly in areas where precision is paramount. This paper performs a systematic literature research on this research topic, analyses and synthesizes the findings, and outlines future research directions. Literature selection cut-off date is January 11th 2024.

Language, a prominent human ability to express through sequential symbols, has been computationally mastered by recent advances of large language models (LLMs). By predicting the next word recurrently with huge neural models, LLMs have shown unprecedented capabilities in understanding and reasoning. Circuit, as the "language" of electronic design, specifies the functionality of an electronic device by cascade connections of logic gates. Then, can circuits also be mastered by a a sufficiently large "circuit model", which can conquer electronic design tasks by simply predicting the next logic gate? In this work, we take the first step to explore such possibilities. Two primary barriers impede the straightforward application of LLMs to circuits: their complex, non-sequential structure, and the intolerance of hallucination due to strict constraints (e.g., equivalence). For the first barrier, we encode a circuit as a memory-less, depth-first traversal trajectory, which allows Transformer-based neural models to better leverage its structural information, and predict the next gate on the trajectory as a circuit model. For the second barrier, we introduce an equivalence-preserving decoding process, which ensures that every token in the generated trajectory adheres to the specified equivalence constraints. Moreover, the circuit model can also be regarded as a stochastic policy to tackle optimization-oriented circuit design tasks. Experimentally, we trained a Transformer-based model of 88M parameters, named "Circuit Transformer", which demonstrates impressive performance in end-to-end logic synthesis. With Monte-Carlo tree search, Circuit Transformer significantly improves over resyn2 while retaining strict equivalence, showcasing the potential of generative AI in conquering electronic design challenges.

With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini. However, various issues (e.g. privacy leakage and copyright violation) of the training corpus still remain underexplored. For example, the Times sued OpenAI and Microsoft for infringing on its copyrights by using millions of its articles for training. From the perspective of LLM practitioners, handling such unintended privacy violations can be challenging. Previous work addressed the ``unlearning" problem of LLMs using gradient information, while they mostly introduced significant overheads like data preprocessing or lacked robustness. In this paper, contrasting with the methods based on first-order information, we revisit the unlearning problem via the perspective of second-order information (Hessian). Our unlearning algorithms, which are inspired by classic Newton update, are not only data-agnostic/model-agnostic but also proven to be robust in terms of utility preservation or privacy guarantee. Through a comprehensive evaluation with four NLP datasets as well as a case study on real-world datasets, our methods consistently show superiority over the first-order methods.

As I scour 35 to 40 websites a day to make sure I'm up to speed on the tech world, I'm seeing a common theme: drama. While everyone from Elon Musk to The New York Times is busy suing OpenAI, I'm just focused on whether these AI chatbots actually work. So, how useful are they, really? I did the work for you. I compared the free versions of ChatGPT from OpenAI, Google Gemini and Perplexity to see how well they helped with some real-life scenarios.

OpenAI denounced Elon Musk's lawsuit against the company in a legal filing on Monday, describing the Tesla CEO's claims as "frivolous" and intended only "to advance his commercial interests". The filing, a response to Musk suing OpenAI earlier this month over allegations that it abandoned its pledge to help humanity, rejects many of the core assertions in Musk's suit. The company denies that it ever broke what Musk calls its "Founding Agreement", stating that no such contract ever existed. "Musk's claims rest on convoluted – often incoherent – factual premises," the filing states. "Musk says his Founding Agreement was'memorialized,' but any actual agreement is conspicuously missing from the pleading."

Earlier this year, sexually explicit images of Taylor Swift were shared repeatedly X. The pictures were almost certainly created with generative-AI tools, demonstrating the ease with which the technology can be put to nefarious ends. This case mirrors many other apparently similar examples, including fake images depicting the arrest of former President Donald Trump, AI-generated images of Black voters who support Trump, and fabricated images of Dr. Anthony Fauci. There is a tendency for media coverage to focus on the source of this imagery, because generative AI is a novel technology that many people are still trying to wrap their head around. But that fact obscures the reason the images are relevant: They spread on social-media networks.

"There is no Founding Agreement, or any agreement at all with Musk," OpenAI said in a court filing as a defendant in Elon Musk's lawsuit. We're, of course, talking about the lawsuit Musk filed against OpenAI, which accuses it of violating its status as a non-profit, as well as of violating a founding agreement promising the organization would never operate for profit and would release its AI publicly. The company said the billionaire's claims are based on "convoluted -- often incoherent -- factual premises." It called that founding agreement "a fiction Musk has conjured to lay unearned claim to the fruits of an enterprise he initially supported, then abandoned, then watched succeed without him." If the case goes to discovery, there's evidence that would show that Musk supported OpenAI's transition into a for-profit structure, "to be controlled by Musk himself," OpenAI continued.

Recent advancements in Natural Language Processing (NLP) with Large Language Models (LLMs) have made the sentiment analysis of financial news by machines a practical achievement and no longer just a dream. More precisely, Large Language Models (LLMs) have marked a major step forward in processing large contexts, exhibiting human-level performance on various professional and academic benchmarks, although they still have limitations such as reliability issues and limited context windows [OpenAI, 2023]. Their ability to process more context has shown particularly interesting applications in many business areas [George and George, 2023]. Hence exploring the potential to extract either weak or strong signals from financial news to enhance a risk-on risk-off investment strategy becomes highly pertinent. Indeed, extracting sentiment from financial news is not new [Tetlock, 2007, Schumaker and Chen, 2009], and finance has a longstanding tradition of exploiting textual data [Kearney and Liu, 2014].

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.