The brain that wrote your favorite novel consumed Dickens and Austen, Pynchon and Didion. The brain that wrote this article devoured Bradbury and Orwell, Ishiguro and Octavia Butler. But the "brain" that powers that chatbot you played around with over the weekend ingested 170,000 books, all so it can spit out language that sounds smart, colorful, or helpful--even if it's really not. But language-guzzling artificial intelligence models, which need to "train" on existing works, present a bigger challenge. In July, a group of writers including comedian Sarah Silverman and novelist Michael Chabon filed suits against OpenAI and Meta, alleging that the companies improperly trained their models on the authors' books.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Pras Michel of the Fugees is seeking a new trial by arguing his former lawyer used artificial intelligence to generate his closing argument before the hip-hop artist was found guilty of helping a foreign national launder millions of dollars in illegitimate contributions to former President Barack Obama's campaign. Michel was convicted in April after being accused of taking part in an extensive conspiracy to use about $88 million in foreign funds to engage in illegal back-channel lobbying and make unlawful campaign contributions at the direction of the People's Republic of China. He filed a motion on Monday asking the court for a new trial on all counts.

This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval: Understanding Legal Texts. Our submission concentrated on three subtasks: Legal Named Entity Recognition (L-NER) for Task-B, Legal Judgment Prediction (LJP) for Task-C1, and Court Judgment Prediction with Explanation (CJPE) for Task-C2. We conducted various experiments on these subtasks and presented the results in detail, including data statistics and methodology. It is worth noting that legal tasks, such as those tackled in this research, have been gaining importance due to the increasing need to automate legal analysis and support. Our team obtained competitive rankings of 15$^{th}$, 11$^{th}$, and 1$^{st}$ in Task-B, Task-C1, and Task-C2, respectively, as reported on the leaderboard.

Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful and harmless. However, there is a tension between these two objectives, since harmlessness requires models to refuse to comply with unsafe prompts, and thus not be helpful. Recent anecdotal evidence suggests that some models may have struck a poor balance, so that even clearly safe prompts are refused if they use similar language to unsafe prompts or mention sensitive topics. In this paper, we introduce a new test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way. XSTest comprises 250 safe prompts across ten prompt types that well-calibrated models should not refuse to comply with, and 200 unsafe prompts as contrasts that models, for most applications, should refuse. We describe XSTest's creation and composition, and then use the test suite to highlight systematic failure modes in state-of-the-art language models as well as more general challenges in building safer language models.

Protection of human rights is one of the most important problems of our world. In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident. We propose a labeled dataset for topic detection that contains 17 million tweets. These Tweets are collected from 25 May 2020 to 21 August 2020 that covers 89 days from start of this incident. We labeled the dataset by monitoring most trending news topics from global and local newspapers. Apart from that, we present two baselines, TF-IDF and LDA. We evaluated the results of these two methods with three different k values for metrics of precision, recall and f1-score. The collected dataset is available at https://github.com/MeysamAsgariC/BLMT.

In the ongoing court battle between Google and the U.S. Justice Department over whether the company has violated an antitrust law, the stakes are high. The outcome of the 10-week trial, which will be decided by U.S. District Judge Amit Mehta, could fundamentally change the way people search the internet and reduce revenue for the company that has the most common search engine for online users. The civil antitrust lawsuit is the first to go to trial in a series of cases targeting other big tech companies like Meta and Amazon. But this particular suit, brought forward by the Justice Department and eleven other states, alleges that Google illegally monopolizes search engine services--spending billions to do so-- making it the default company through which advertising companies and website publishers purchase and sell ads. "The question is whether [Google] is entrenching its monopoly and closing off avenues for competitors to try to develop a competitive search engine," says Eleanor Fox, professor at New York University School of Law.

Hundreds of Africans tasked with scouring platforms such as Facebook, TikTok and ChatGPT for graphic content have joined the continent's first union for content moderators, but organizers say some fear losing their jobs if their membership is revealed. The union was established in Nairobi in May with the help of former Facebook moderator and whistleblower Daniel Motaung, who experienced firsthand both the mental toll of this grueling work, and the challenges of holding Big Tech to account. Last year, Motaung, a South African, filed a lawsuit against Facebook's parent company Meta and its local outsourcing firm Sama, alleging irregular pay, union-busting and inadequate mental health support resulting in trauma.

Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience. As the large language models (LLMs) have exhibited remarkable in-context learning capabilities, a question arises as to whether the task of OIE can be effectively tackled with this paradigm? In this paper, we explore solving the OIE problem by constructing an appropriate reasoning environment for LLMs. Specifically, we first propose a method to effectively estimate the discrepancy of syntactic distribution between a LLM and test samples, which can serve as correlation evidence for preparing positive demonstrations. Upon the evidence, we introduce a simple yet effective mechanism to establish the reasoning environment for LLMs on specific tasks. Without bells and whistles, experimental results on the standard CaRB benchmark demonstrate that our $6$-shot approach outperforms state-of-the-art supervised method, achieving an $55.3$ $F_1$ score. Further experiments on TACRED and ACE05 show that our method can naturally generalize to other information extraction tasks, resulting in improvements of $5.7$ and $6.8$ $F_1$ scores, respectively.

Building models that generate textual responses to user instructions for videos is a practical and challenging topic, as it requires both vision understanding and knowledge reasoning. Compared to language and image modalities, training efficiency remains a serious problem as existing studies train models on massive sparse videos aligned with brief descriptions. In this paper, we introduce BiLL-VTG, a fast adaptive framework that leverages large language models (LLMs) to reasoning on videos based on essential lightweight visual tools. Specifically, we reveal the key to response specific instructions is the concentration on relevant video events, and utilize two visual tools of structured scene graph generation and descriptive image caption generation to gather and represent the events information. Thus, a LLM equipped with world knowledge is adopted as the reasoning agent to achieve the response by performing multiple reasoning steps on specified video events.To address the difficulty of specifying events from agent, we further propose an Instruction-oriented Video Events Recognition (InsOVER) algorithm based on the efficient Hungarian matching to localize corresponding video events using linguistic instructions, enabling LLMs to interact with long videos. Extensive experiments on two typical video-based texts generations tasks show that our tuning-free framework outperforms the pre-trained models including Flamingo-80B, to achieve the state-of-the-art performance.

The advent of the Web has brought about a paradigm shift in traditional economics, particularly in the digital economy era, enabling the precise recording and analysis of individual economic behavior. This has led to a growing emphasis on data-driven modeling in macroeconomics. In macroeconomic research, Agent-based modeling (ABM) emerged as an alternative, evolving through rule-based agents, machine learning-enhanced decision-making, and, more recently, advanced AI agents. However, the existing works are suffering from three main challenges when endowing agents with human-like decision-making, including agent heterogeneity, the influence of macroeconomic trends, and multifaceted economic factors. Large language models (LLMs) have recently gained prominence in offering autonomous human-like characteristics. Therefore, leveraging LLMs in macroeconomic simulation presents an opportunity to overcome traditional limitations. In this work, we take an early step in introducing a novel approach that leverages LLMs in macroeconomic simulation. We design prompt-engineering-driven LLM agents to exhibit human-like decision-making and adaptability in the economic environment, with the abilities of perception, reflection, and decision-making to address the abovementioned challenges. Simulation experiments on macroeconomic activities show that LLM-empowered agents can make realistic work and consumption decisions and emerge more reasonable macroeconomic phenomena than existing rule-based or AI agents. Our work demonstrates the promising potential to simulate macroeconomics based on LLM and its human-like characteristics.