Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.

Is there a right way for Google's generative AI to create fake images of Nazis? Gemini, Google's answer to ChatGPT, was shown last week to generate an absurd range of racially and gender-diverse German soldiers styled in Wehrmacht garb. It was, understandably, ridiculed for not generating any images of Nazis who were actually white. Prodded further, it seemed to actively resist generating images of white people altogether. The company ultimately apologized for "inaccuracies in some historical image generation depictions" and paused Gemini's ability to generate images featuring people.

Microsoft announced an artificial intelligence partnership Monday with the French startup Mistral AI that could lessen the software giant's reliance on ChatGPT-maker OpenAI for supplying the next wave of chatbots and other generative AI products. Mistral AI emerged less than a year ago but is already what Microsoft described Monday as an "innovator and trailblazer" at the vanguard of building more efficient and cost-effective AI systems. Microsoft and Mistral didn't disclose the financial terms of the deal, though Microsoft said it involves a small investment in the Paris-based startup. That suggests it is far smaller than Microsoft's investment of billions of dollars into OpenAI, a years-long relationship that has attracted the scrutiny of antitrust regulators in the U.S. and Europe. Mistral on Monday released a public test version of its own chatbot, called Le Chat, that apparently was flooded with so much interest that a company executive said it was temporarily unavailable for part of the day.

The brave new world of Generative AI has become the latest battleground for U.S. culture wars. Google issued an apology after anti-woke X-users, including Elon Musk, shared examples of Google's chatbot Gemini refusing to generate images of white people--including historical figures--even when specifically prompted to do so. Gemini's insistence on prioritizing diversity and inclusion over accuracy is likely a well intentioned attempt to stamp out bias in early GenAI datasets that tended to create stereotypical images of Africans and other minority groups as well women, causing outrage among progressives. But there is much more at stake than the selective outrage of U.S. conservatives and progressives. How the "guardrails" of GenAI are defined and deployed is likely to have a significant and increasing impact on shaping the ecosystem of information and ideas that most humans engage with.

After complaints that Google's image generator built into its Gemini AI was (ugh) woke, Google explained why it may have overcorrected for diversity. Prabhakar Raghavan, the company's senior vice president for knowledge and information, said Google's efforts to ensure a wide range of people generated in images "failed to account for cases that should clearly not show a range." Users criticized Google for depicting specific white figures or historically white groups of people as racially diverse individuals. In Engadget's tests, asking Gemini to create illustrations of the Founding Fathers resulted in images of white men with a single person of color or woman among them. When we asked the chatbot to generate images of popes through the ages, we got photos depicting Black women and Native Americans as the leader of the Catholic Church.

The AI election is here. Already this year, a robocall generated using artificial intelligence targeted New Hampshire voters in the January primary, purporting to be President Joe Biden and telling them to stay home in what officials said could be the first attempt at using AI to interfere with a US election. The "deepfake" calls were linked to two Texas companies, Life Corporation and Lingo Telecom. It's not clear if the deepfake calls actually prevented voters from turning out, but that doesn't really matter, said Lisa Gilbert, executive vice-president of Public Citizen, a group that's been pushing for federal and state regulation of AI's use in politics. "I don't think we need to wait to see how many people got deceived to understand that that was the point," Gilbert said.

Semantic Overlap Summarization (SOS) is a constrained multi-document summarization task, where the constraint is to capture the common/overlapping information between two alternative narratives. While recent advancements in Large Language Models (LLMs) have achieved superior performance in numerous summarization tasks, a benchmarking study of the SOS task using LLMs is yet to be performed. As LLMs' responses are sensitive to slight variations in prompt design, a major challenge in conducting such a benchmarking study is to systematically explore a variety of prompts before drawing a reliable conclusion. Fortunately, very recently, the TELeR taxonomy has been proposed which can be used to design and explore various prompts for LLMs. Using this TELeR taxonomy and 15 popular LLMs, this paper comprehensively evaluates LLMs on the SOS Task, assessing their ability to summarize overlapping information from multiple alternative narratives. For evaluation, we report well-established metrics like ROUGE, BERTscore, and SEM-F1$ on two different datasets of alternative narratives. We conclude the paper by analyzing the strengths and limitations of various LLMs in terms of their capabilities in capturing overlapping information The code and datasets used to conduct this study are available at https://anonymous.4open.science/r/llm_eval-E16D.

Caseworkers in the child welfare (CW) sector use predictive decision-making algorithms built on risk assessment (RA) data to guide and support CW decisions. Researchers have highlighted that RAs can contain biased signals which flatten CW case complexities and that the algorithms may benefit from incorporating contextually rich case narratives, i.e. - casenotes written by caseworkers. To investigate this hypothesized improvement, we quantitatively deconstructed two commonly used RAs from a United States CW agency. We trained classifier models to compare the predictive validity of RAs with and without casenote narratives and applied computational text analysis on casenotes to highlight topics uncovered in the casenotes. Our study finds that common risk metrics used to assess families and build CWS predictive risk models (PRMs) are unable to predict discharge outcomes for children who are not reunified with their birth parent(s). We also find that although casenotes cannot predict discharge outcomes, they contain contextual case signals. Given the lack of predictive validity of RA scores and casenotes, we propose moving beyond quantitative risk assessments for public sector algorithms and towards using contextual sources of information such as narratives to study public sociotechnical systems.

Open-ended question answering requires models to find appropriate evidence to form well-reasoned, comprehensive and helpful answers. In practical applications, models also need to engage in extended discussions on potential scenarios closely relevant to the question. With augmentation of retrieval module, open-source Large Language Models (LLMs) can produce coherent answers often with different focuses, but are still sub-optimal in terms of reliable evidence selection and in-depth question analysis. In this paper, we propose a novel Chain-of-Discussion framework to leverage the synergy among multiple open-source LLMs aiming to provide \textbf{more correct} and \textbf{more comprehensive} answers for open-ended QA, although they are not strong enough individually. Our experiments show that discussions among multiple LLMs play a vital role in enhancing the quality of answers. We release our data and code at \url{https://github.com/kobayashikanna01/Chain-of-Discussion}.

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs perform in specific domains (e.g, the intellectual property (IP) domain). In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain. The MoZIP benchmark includes three challenging tasks: IP multiple-choice quiz (IPQuiz), IP question answering (IPQA), and patent matching (PatentMatch). In addition, we also develop a new IP-oriented multilingual large language model (called MoZi), which is a BLOOMZ-based model that has been supervised fine-tuned with multilingual IP-related text data. We evaluate our proposed MoZi model and four well-known LLMs (i.e., BLOOMZ, BELLE, ChatGLM and ChatGPT) on the MoZIP benchmark. Experimental results demonstrate that MoZi outperforms BLOOMZ, BELLE and ChatGLM by a noticeable margin, while it had lower scores compared with ChatGPT. Notably, the performance of current LLMs on the MoZIP benchmark has much room for improvement, and even the most powerful ChatGPT does not reach the passing level.