As prediction market platforms like Polymarket and Kalshi battle regulators in court, Senate Democrats are urging the CFTC to avoid weighing in, escalating a broader fight over the burgeoning industry. Senator Adam Schiff, a Democrat from California, is leading the group of lawmakers urging the CFTC to stay out of state prediction market lawsuits. A group of 23 Democratic US senators sent a letter Friday to the top federal regulator overseeing prediction markets, urging the agency to avoid weighing in on pending court cases over the legality of offerings on the platforms tied to "sports, war, and other prohibited events." Prediction markets, which sell contracts tied to the outcome of real-world developments, have exploded in popularity over the past year, attracting an increasingly mainstream fanbase eager to wager on everything from geopolitical conflicts to fashion choices to the Super Bowl. As they expanded, the platforms have become a magnet for ethical and legal controversies.

We participate in CheckThat! Task 2 English and explore various methods of prompting and in-context learning, including few-shot prompting and fine-tuning with different LLM families, with the goal of extracting check-worthy claims from social media passages. Our best METEOR score is achieved by fine-tuning a FLAN-T5 model. However, we observe that higher-quality claims can sometimes be extracted using other methods, even when their METEOR scores are lower.

On May 16, 2023, Sam Altman appeared before a subcommittee of the Senate Judiciary. The title of the hearing was "Oversight of AI." The session was a lovefest, with both Altman and the senators celebrating what Altman called AI's "printing press moment"--and acknowledging that the US needed strong laws to avoid its pitfalls. "We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models," he said. The legislators hung on Altman's every word as he gushed about how smart laws could allow AI to flourish--but only within firm guidelines that both lawmakers and AI builders deemed vital at that moment.

Breakthroughs, discoveries, and DIY tips sent every weekday. If you recently received a voice message from an unusual number claiming to be your local congressperson, it's probably a scam. The FBI's crime division issued a warning this week about a new scheme in which bad actors use text messages and AI-generated voice clones to impersonate government officials. The scammers try to build a sense of connection with their target and eventually convince them to click on a malicious link that steals valuable login credentials. This scam is just the latest in a series of evolving attacks using convincing generative AI technology to trick people.

Over the course of an eight-minute interview, Elon Musk touted his numerous businesses and vision of a "Star Trek future" while telling the crowd that his Tesla Optimus robots had performed a dance for Donald Trump and the crown prince of Saudi Arabia, Mohammed bin Salman, to the tune of YMCA. He also announced that Starlink, his satellite internet company, had struck a deal for use in Saudi Arabia for maritime and aviation usage; looking to the near future, he expressed his desire to bring Tesla's self-driving robotaxis to the country. "We could not be more appreciative of having a lifetime partner and a friend like you, Elon, to the Kingdom," Saudi Arabia's minister of communications and IT, Abdullah Alswaha, told Musk. Although Musk has pivoted away from his role as de facto leader of the so-called "department of government efficiency" and moved out of the White House, the Saudi summit showed how he is still retaining his proximity to the US president and international influence. As Musk returns to his businesses as his primary focus, he is still primed to reap the rewards of his connections and political sway over Trump.

Sen. John Fetterman, D-Pa., receives praise for his less-than-formal attire from Sam Altman during a Commerce Committee hearing. Sen. John Fetterman, D-Pa., was one of the final senators to question OpenAI chief Sam Altman during Thursday's Senate Commerce Committee hearing, and the subject of both Three Mile Island and the Democrat's penchant for Carhartt outerwear came up. Fetterman said that as a senator he has been able to meet people with "much more impressive jobs and careers" and that due to Altman's technology, "humans will have a wonderful ability to adapt." He told Altman that some Americans are worried about AI on various levels, and he asked the executive to address it. In response, Altman said he appreciated Fetterman's praise.

In this paper we provide evidence that a virtual model of U.S. congresspersons based on a collection of language models satisfies the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data are capable of producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis.

We introduce a new method to identify emerging concepts in large text corpora. By analyzing changes in the heatmaps of the underlying embedding space, we are able to detect these concepts with high accuracy shortly after they originate, in turn outperforming common alternatives. We further demonstrate the utility of our approach by analyzing speeches in the U.S. Senate from 1941 to 2015. Our results suggest that the minority party is more active in introducing new concepts into the Senate discourse. We also identify specific concepts that closely correlate with the Senators' racial, ethnic, and gender identities. An implementation of our method is publicly available.

Catastrophe brings a search for accountability. As fires wreak havoc in California, Sen. Ed Markey, D-Mass., claimed in a post on X the catastrophe is "what a climate emergency looks like." He took aim at President-elect Trump, asserting the incoming president has been bought off by the oil industry. "Trump has been bought for 1 billion by Big Oil. Just a payoff to kill the IRA and the Green New Deal. We know what will happen. The LA fires are preview of coming atrocities," Markey declared in a post on X. Markey, who claims there is a "climate crisis," has also warned about the potential effects of artificial intelligence (AI).

Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided service. We treat this type of auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors. For instance, we may seek a non-toxic input that the target LLM responds to with a toxic output or an input that induces the hallucinative response from the target LLM containing politically sensitive individuals. This black-box optimization is challenging due to the scarcity of feasible points, the discrete nature of the prompt space, and the large search space. To address these challenges, we propose Curiosity-Driven Auditing for Large Language Models (CALM), which uses intrinsically motivated reinforcement learning to finetune an LLM as the auditor agent to uncover potential harmful and biased input-output pairs of the target LLM. CALM successfully identifies derogatory completions involving celebrities and uncovers inputs that elicit specific names under the black-box setting. This work offers a promising direction for auditing black-box LLMs. Our code is available at https://github.com/x-zheng16/CALM.git.