In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

In this paper, we explore the potential of Vision-Language Models (VLMs), specifically CLIP, in predicting visual object relationships, which involves interpreting visual features from images into language-based relations. Current state-of-the-art methods use complex graphical models that utilize language cues and visual features to address this challenge. We hypothesize that the strong language priors in CLIP embeddings can simplify these graphical models paving for a simpler approach. We adopt the UVTransE relation prediction framework, which learns the relation as a translational embedding with subject, object, and union box embeddings from a scene. We systematically explore the design of CLIP-based subject, object, and union-box representations within the UVTransE framework and propose CREPE (CLIP Representation Enhanced Predicate Estimation). CREPE utilizes text-based representations for all three bounding boxes and introduces a novel contrastive training strategy to automatically infer the text prompt for union-box. Our approach achieves state-of-the-art performance in predicate estimation, mR@5 27.79, and mR@20 31.95 on the Visual Genome benchmark, achieving a 15.3\% gain in performance over recent state-of-the-art at mR@20. This work demonstrates CLIP's effectiveness in object relation prediction and encourages further research on VLMs in this challenging domain.

The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon significantly hinders information dissemination across communities and fuels societal polarization. Therefore, it is crucial to develop methods for quantifying echo chambers. In this paper, we present the Echo Chamber Score (ECS), a novel metric that assesses the cohesion and separation of user communities by measuring distances between users in the embedding space. In contrast to existing approaches, ECS is able to function without labels for user ideologies and makes no assumptions about the structure of the interaction graph. To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity. To assess the effectiveness of ECS, we use a Twitter dataset consisting of four topics - two polarizing and two non-polarizing. Our results showcase ECS's effectiveness as a tool for quantifying echo chambers and shedding light on the dynamics of online discourse.

Successful deployment of artificial intelligence (AI) in various settings has led to numerous positive outcomes for individuals and society. However, AI systems have also been shown to harm parts of the population due to biased predictions. AI fairness focuses on mitigating such biases to ensure AI decision making is not discriminatory towards certain groups. We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time and act as a social stressor. More specifically, we discuss how biased models can lead to more negative real-world outcomes for certain groups, which may then become more prevalent by deploying new AI models trained on increasingly biased data, resulting in a feedback loop. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest. We examine current strategies for improving AI fairness, assess their limitations in terms of real-world deployment, and explore potential paths forward to ensure we reap AI's benefits without causing society's collapse.

Published in the Proceedings of the National Academy of Sciences https://www.pnas.org/doi/10.1073/pnas.2305016120 Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (n = 6,183), we show that ChatGPT outperforms crowd-workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average, while ChatGPT's intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003--about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification. 1 Introduction Many NLP applications require high-quality labeled data, notably to train classifiers or evaluate the performance of unsupervised models. For example, researchers often aim to filter noisy social media data for relevance, assign texts to different topics or conceptual categories, or measure their sentiment or stance.

Tech leaders and AI experts on Tuesday warned that the U.S. military needs to move quickly to harness its military data and invest in emerging technology if it wants to compete with the Chinese in an era when artificial intelligence is upending global conflict. "The country that is able to most rapidly and effectively integrate new technology into war-fighting wins," Alexandr Wang, the CEO of Scale AI, told lawmakers on a House Armed Services subcommittee. China is spending three times more than the U.S. on developing AI tools, Wang noted. "The Chinese Communist Party deeply understands the potential for AI to disrupt warfare, and is investing heavily to capitalize," he said. "AI is China's Apollo project."

Proponents say such practices may help reduce use-of-force incidents. The United Nations Security Council held its first discussion on artificial intelligence (AI) and associated risks, with a number of leaders highlighting the dangerous potential the technology possesses in the wrong hands. "The malicious use of AI systems for terrorist, criminal or state purposes could cause horrific levels of deaths and destruction, widespread trauma and deep psychological damage on an unimaginable scale," U.N. Secretary-General Antonio Guterres said in his remarks at the meeting. "Generative AI has enormous potential for good and evil at scale." "Its creators themselves have warned that much bigger, potentially catastrophic and existential risks lie ahead," he added.

The United States is reportedly planning to send Ukraine another $1.3 billion in military aid as it continues a counteroffensive against Russia. The weapons package includes air defenses, counter-drone systems, exploding drones and ammunition, Reuters reported, citing two unnamed U.S. officials. Weapons manufacturers will provide the arms purchased by the United States to Kyiv through President Biden's Ukraine Security Assistance Initiative (USAI) program, so U.S. weapons stocks will not be depleted. Among the systems and ammunition the U.S. plans to buy for Kyiv are counter-air defenses made by L3Harris Technologies called the Vehicle-Agnostic Modular Palletized ISR Rocket Equipment or VAMPIRE, Reuters reported. Military aid, delivered as part of U.S. security assistance to Ukraine, is unloaded at the Boryspil International Airport outside Kyiv, Feb. 13, 2022.

Experts at a congressional hearing Tuesday on the future of AI downplayed Elon Musk's predictions for how quickly an artificial general intelligence, or AGI, could be developed. The exchange came during a House Armed Services subcommittee hearing featuring a trio of AI experts. Each of the three said developing an AGI would take longer than Musk had predicted. "When Chairman [Mike] Gallagher had a conversation with Elon Musk, he said that AGI was five to six years away. I was surprised by that timeline. What is your sense of how long we are from AGI?" Rep. Ro Khanna, D-Calif., asked.

Malicious use of artificial intelligence systems could cause a "horrific" amount of death and destruction, the UN secretary general has said, calling for a new UN body to tackle the threats posed by the technology. António Guterres said harmful use of AI for terrorist, criminal or state purposes could also cause "deep psychological damage", and he said AI-enabled cyber-attacks were already targeting UN peacekeeping and humanitarian operations. "The malicious use of AI systems for terrorist, criminal or state purposes could cause horrific levels of death and destruction, widespread trauma and deep psychological damage on an unimaginable scale," Guterres said. Speaking at the first UN security council session on AI, he said the advent of generative AI – the term for AI tools such as ChatGPT that produce convincing text, image and voice from human prompts – could be a defining moment for disinformation and hate speech and add a "new dimension" to the manipulation of human behaviour. Guterres called for the creation of a new UN entity along the lines of the Intergovernmental Panel on Climate Change to tackle the risks.