The AI Insight Forums were announced a few months ago by Senate Majority Leader Chuck Schumer as part of his "SAFE Innovation" initiative, which is really a set of principles for AI legislation in the United States. The invite list was heavily skewed toward Big Tech execs, including CEOs of AI companies, though a few civil society and AI ethics researchers were included too. Coverage of the meeting thus far has put a particular emphasis on the reportedly unanimous agreement about the need for AI regulation, and on issues raised by Elon Musk and others about the "civilizational risks" created by AI. (This tracker from Tech Policy Press is pretty handy if you want to know more.) But to really dig below the surface, I caught up with one of the other attendees, Inioluwa Deborah Raji, who gave me an inside look at how the first meeting went, the pernicious myths she needed to debunk, and where disagreements could be felt in the room. Raji is a researcher at the University of California, Berkeley, and a fellow at Mozilla.

GOP presidential candidate Vivek Ramaswamy discusses whether President Biden will be the 2024 Democrat nominee on "Hannity." FIRST ON FOX: Vivek Ramaswamy's Republican presidential campaign is explaining the former CEO's "awakening" on the threat China poses to the United States, following scrutiny for his former company's partnership with a Chinese Communist Party-backed company just a few years ago. Ramaswamy has repeatedly expressed his support for banning American companies from expanding into China. Just Thursday, he unveiled his plan to "decouple" from China in a speech in his home state of Ohio. "Unless you stop turning our companies into lobbying pawns, unless you actually play by the same set of rules abiding by the same standards we agreed to, then we're cutting the cord," he said.

This is a rush transcript of'Fox News Sunday' on September 24, 2023. This copy may not be in its final form and may be updated. The chaos at the border grows by the day, as the pressure to take greater action builds yet again on the White House. We need people from the top. HEMMER (voice-over): A border city mayor and Democrat declaring a state of emergency as thousands upon thousands of migrants flow into the country. JOE BIDEN, PRESIDENT OF THE UNITED STATES: Republicans in Congress and my predecessor spent four years gutting the immigration system -- under my predecessor. They continue to undermine our border security today. HEMMER: We'll get reaction from border state Democrat, Texas Congressman Henry Cuellar. President Biden says he'll join the picket line in Michigan on Tuesday, just a day before Donald Trump will be there, too. Meanwhile, another presidential hopeful pushes back. TIM SCOTT (R-SC), PRESIDENTIAL CANDIDATE: We need a president who says we are not going to subsidize unions, period. HEMMER: We'll discuss with a man whose eyes are on the White House, South Carolina Senator Tim Scott. We'll ask Republican National Committee chairwoman Ronna McDaniel what voters can expect to see on stage Wednesday night. JAMES LANKFORD (R-OK): It's a symbol of respect for the country when you dress respectfully when you're doing this responsibility. JOHN FETTERMAN (D-PA): I think there are more important things we should be talking about rather if -- if I dressed like a slob. The number of illegals crossing our border hit another new record. We want to show you our FOX News drone camera from Eagle Pass, Texas. We've been watching remarkable images today of a human flood that shows no sign of receding. And today, a new survey shows how displeased Americans are with the president's border policies. In a moment, we'll speak with border state Democrat, Texas Congressman Henry Cuellar, on that. But, first, to Griff Jenkins who has been in Eagle Pass for what seems like several years now. Well, there's a humanitarian crisis playing out along our southern border in places like here in Eagle Pass, Texas, where migrants have traveled thousands of miles in hopes of reaching the U.S. in numbers far greater than what border officials are able to handle. Actions include sending active duty troops to the border, increasing deportations and granting temporary protective status to nearly half a million Venezuelans, making it easier for them to find work in cities like New York, where officials are struggling to find room for them. Meanwhile, Texas Governor Greg Abbott trying to deter the migrants from entering his state, with miles of dense razor wire, Humvees manning the riverbank and guardsmen in rafts attempting to turn them back.

Large Language Models (LLMs), acting as a powerful reasoner and generator, exhibit extraordinary performance across various natural language tasks, such as question answering (QA). Among these tasks, Multi-Hop Question Answering (MHQA) stands as a widely discussed category, necessitating seamless integration between LLMs and the retrieval of external knowledge. Existing methods employ LLM to generate reasoning paths and plans, and utilize IR to iteratively retrieve related knowledge, but these approaches have inherent flaws. On one hand, Information Retriever (IR) is hindered by the low quality of generated queries by LLM. On the other hand, LLM is easily misguided by the irrelevant knowledge by IR. These inaccuracies, accumulated by the iterative interaction between IR and LLM, lead to a disaster in effectiveness at the end. To overcome above barriers, in this paper, we propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved framework (Furthest Reasoning) and an attached module (Plan Assessor). 1) Furthest reasoning operates by masking previous reasoning path and generated queries for LLM, encouraging LLM generating chain of thought from scratch in each iteration. This approach enables LLM to break the shackle built by previous misleading thoughts and queries (if any). 2) The Plan Assessor is a trained evaluator that selects an appropriate plan from a group of candidate plans proposed by LLM. Our methods are evaluated on three highly recognized public multi-hop question answering datasets and outperform state-of-the-art on most metrics (achieving a 10%-12% in answer accuracy).

N-ary facts composed of a primary triple (head entity, relation, tail entity) and an arbitrary number of auxiliary attribute-value pairs, are prevalent in real-world knowledge graphs (KGs). Link prediction on n-ary facts is to predict a missing element in an n-ary fact. This helps populate and enrich KGs and further promotes numerous downstream applications. Previous studies usually require a substantial amount of high-quality data to understand the elements in n-ary facts. However, these studies overlook few-shot relations, which have limited labeled instances, yet are common in real-world scenarios. Thus, this paper introduces a new task, few-shot link prediction on n-ary facts. It aims to predict a missing entity in an n-ary fact with limited labeled instances. We further propose a model for Few-shot Link prEdict on N-ary facts, thus called FLEN, which consists of three modules: the relation learning, support-specific adjusting, and query inference modules. FLEN captures relation meta information from limited instances to predict a missing entity in a query instance. To validate the effectiveness of FLEN, we construct three datasets based on existing benchmark data. Our experimental results show that FLEN significantly outperforms existing related models in both few-shot link prediction on n-ary facts and binary facts.

Making the contents generated by Large Language Model (LLM) such as ChatGPT, accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each of which needs knowledge to solve. Introducing Information Retrieval (IR) to provide LLM with external knowledge is good potential to solve this problem. However, where and how to introduce IR into LLM is a big challenge. Previous work has the disadvantage that the wrong knowledge retrieved by IR misleads the LLM or breaks the reasoning chain of LLM. In this paper, we propose a novel framework called Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. First, LLM generates the global reasoning chain called Chain-of-Query (CoQ) where each node consists of an IR-oriented query and the answer to the query. Second, IR verifies the answer of each node of CoQ, it corrects the answer that is not consistent with the retrieved information when IR gives high confidence, which improves the credibility. Third, LLM can mark its missing knowledge in CoQ and IR can provide this knowledge to LLM. These three operations improve the accuracy of LLM for complex knowledge-intensive tasks in terms of reasoning ability and knowledge. Finally, SearChain generates the reasoning process and marks references to supporting documents for each reasoning step, which improves traceability. SearChain transforms the topology of reasoning from chain to tree, which can modify the reasoning direction. Experiment shows that SearChain outperforms baselines on complex knowledge-intensive tasks including multi-hop question-answering, slot filling, fact checking, and long-form question-answering.

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.

We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years. We probe the LLMs using zero- and k-shot unconstrained probes, and compare with traditional methods for negation generation, i.e., pattern-based textual extractions and knowledge-graph-based inferences, as well as crowdsourced gold statements. We measure the correctness and salience of the generated lists about subjects from different domains. Our evaluation shows that guided probes do in fact improve the quality of generated negatives, compared to the zero-shot variant. Nevertheless, using both prompts, LLMs still struggle with the notion of factuality of negatives, frequently generating many ambiguous statements, or statements with negative keywords but a positive meaning.

The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigmatic shifts to protect the privacy of LLM-based CA users.

Privacy attack is a popular and well-developed topic in various fields such as social network analysis, healthcare, finance, system, etc. [88], [89], [90]. During recent years, the surge of machine learning has provided powerful tools to solve many practical problems. However, data-driven approaches also threaten users' privacy due to the associated risks of data leakage and inference [85]. Consequently, a substantial amount of work has been devoted to investigate the vulnerabilities of ML models and the risks of privacy leakage [47]. A branch of privacy research is to develop privacy attack models, which has received much attention during the past few years. However, attack models with respect to GNNs have only been explored very recently because GNN techniques are relatively new compared with CNN/transformers in image/natural language processing(NLP) domains, and the irregular graph structure poses unique challenges to transfer existing attack techniques that are well-established in other domains. In this section, we summarize papers that have developed attack models specifically targeting GNNs. Figure 1: Illustrations of the four categories of privacy attack We classify the privacy attack models on GNN into models on graphs: a) Model extraction attacks (MEA); b) four categories (which are visualized in Figure 4): a) model Graph structure reconstruction (GSR); c) Attribute inference extraction attack (MEA), b) graph structure reconstruction attacks (AIA); and d) Membership inference attacks (MIA).