The interplay between humans and Generative AI (Gen AI) draws an insightful parallel with the dynamic relationship between giraffes and acacias on the African Savannah. Just as giraffes navigate the acacia's thorny defenses to gain nourishment, humans engage with Gen AI, maneuvering through ethical and operational challenges to harness its benefits. This paper explores how, like young giraffes that are still mastering their environment, humans are in the early stages of adapting to and shaping Gen AI. It delves into the strategies humans are developing and refining to help mitigate risks such as bias, misinformation, and privacy breaches, that influence and shape Gen AI's evolution. While the giraffe-acacia analogy aptly frames human-AI relations, it contrasts nature's evolutionary perfection with the inherent flaws of human-made technology and the tendency of humans to misuse it, giving rise to many ethical dilemmas. Through the HHH framework we identify pathways to embed values of helpfulness, honesty, and harmlessness in AI development, fostering safety-aligned agents that resonate with human values. This narrative presents a cautiously optimistic view of human resilience and adaptability, illustrating our capacity to harness technologies and implement safeguards effectively, without succumbing to their perils. It emphasises a symbiotic relationship where humans and AI continually shape each other for mutual benefit.

Large language models (LLMs) are very proficient text generators. We leverage this capability of LLMs to generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. Given task-specific data in a source language and a teacher model trained on this data, we propose using this teacher to label LLM generations and employ a set of simple data selection strategies that use the teacher's label probabilities. Our data selection strategies help us identify a representative subset of diverse generations that help boost zero-shot accuracies while being efficient, in comparison to using all the LLM generations (without any subset selection). We also highlight other important design choices that affect cross-lingual performance such as the use of translations of source data and what labels are best to use for the LLM generations. We observe significant performance gains across sentiment analysis and natural language inference tasks (of up to a maximum of 7.13 absolute points and 1.5 absolute points on average) across a number of target languages (Hindi, Marathi, Urdu, Swahili) and domains.

Codebooks -- documents that operationalize constructs and outline annotation procedures -- are used almost universally by social scientists when coding unstructured political texts. Recently, to reduce manual annotation costs, political scientists have looked to generative large language models (LLMs) to label and analyze text data. However, previous work using LLMs for classification has implicitly relied on the universal label assumption -- correct classification of documents is possible using only a class label or minimal definition and the information that the LLM inductively learns during its pre-training. In contrast, we argue that political scientists who care about valid measurement should instead make a codebook-construct label assumption -- an LLM should follow the definition and exclusion criteria of a construct/label provided in a codebook. In this work, we collect and curate three political science datasets and their original codebooks and conduct a set of experiments to understand whether LLMs comply with codebook instructions, whether rewriting codebooks improves performance, and whether instruction-tuning LLMs on codebook-document-label tuples improves performance over zero-shot classification. Using Mistral 7B Instruct as our LLM, we find re-structuring the original codebooks gives modest gains in zero-shot performance but the model still struggles to comply with the constraints of the codebooks. Optimistically, instruction-tuning Mistral on one of our datasets gives significant gains over zero-shot inference (0.76 versus 0.53 micro F1). We hope our conceptualization of the codebook-specific task, assumptions, and instruction-tuning pipeline as well our semi-structured LLM codebook format will help political scientists readily adapt to the LLM era.

Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific writing, particularly in business contexts, is relatively less explored. Our formative study with industry professionals revealed the limitations in current LLMs' understanding of the nuances in such domain-specific writing. To address this gap, we propose an approach of human-AI collaborative taxonomy development to perform as a guideline for domain-specific writing assistants. This method integrates iterative feedback from domain experts and multiple interactions between these experts and LLMs to refine the taxonomy. Through larger-scale experiments, we aim to validate this methodology and thus improve LLM-powered writing assistance, tailoring it to meet the unique requirements of different stakeholder needs.

With rapid advances in machine learning, many people in the field have been discussing the rise of digital minds and the possibility of artificial sentience. Future developments in AI capabilities and safety will depend on public opinion and human-AI interaction. To begin to fill this research gap, we present the first nationally representative survey data on the topic of sentient AI: initial results from the Artificial Intelligence, Morality, and Sentience (AIMS) survey, a preregistered and longitudinal study of U.S. public opinion that began in 2021. Across one wave of data collection in 2021 and two in 2023 (total N = 3,500), we found mind perception and moral concern for AI well-being in 2021 were higher than predicted and significantly increased in 2023: for example, 71% agree sentient AI deserve to be treated with respect, and 38% support legal rights. People have become more threatened by AI, and there is widespread opposition to new technologies: 63% support a ban on smarter-than-human AI, and 69% support a ban on sentient AI. Expected timelines are surprisingly short and shortening with a median forecast of sentient AI in only five years and artificial general intelligence in only two years. We argue that, whether or not AIs become sentient, the discussion itself may overhaul human-computer interaction and shape the future trajectory of AI technologies, including existential risks and opportunities.

Conversational large language models are fine-tuned for both instruction-following and safety, resulting in models that obey benign requests but refuse harmful ones. While this refusal behavior is widespread across chat models, its underlying mechanisms remain poorly understood. In this work, we show that refusal is mediated by a one-dimensional subspace, across 13 popular open-source chat models up to 72B parameters in size. Specifically, for each model, we find a single direction such that erasing this direction from the model's residual stream activations prevents it from refusing harmful instructions, while adding this direction elicits refusal on even harmless instructions. Leveraging this insight, we propose a novel white-box jailbreak method that surgically disables refusal with minimal effect on other capabilities. Finally, we mechanistically analyze how adversarial suffixes suppress propagation of the refusal-mediating direction. Our findings underscore the brittleness of current safety fine-tuning methods. More broadly, our work showcases how an understanding of model internals can be leveraged to develop practical methods for controlling model behavior.

Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.

Retrieval-augmented generation supports language models to strengthen their factual groundings by providing external contexts. However, language models often face challenges when given extensive information, diminishing their effectiveness in solving questions. Context compression tackles this issue by filtering out irrelevant information, but current methods still struggle in realistic scenarios where crucial information cannot be captured with a single-step approach. To overcome this limitation, we introduce CompAct, a novel framework that employs an active strategy to condense extensive documents without losing key information. Our experiments demonstrate that CompAct brings significant improvements in both performance and compression rate on multi-hop question-answering (QA) benchmarks. CompAct flexibly operates as a cost-efficient plug-in module with various off-the-shelf retrievers or readers, achieving exceptionally high compression rates (47x).

OpenAI whistleblowers have urged the US financial watchdog to investigate non-disclosure agreements at the startup after claiming the contracts included restrictions such as requiring employees to seek permission before contacting regulators. Non-disclosure agreements (NDAs) typically bar an employee from sharing company information with outside parties but a group of whistleblowers are arguing that OpenAI's agreements could have led to workers being punished for raising concerns about the company to federal authorities. San Francisco-based OpenAI is the developer of the ChatGPT chatbot and a key player in the artificial intelligence boom, which has been accompanied by expressions of concern from experts about the potential dangerous capabilities of the technology. "Given the well-documented potential risks posed by the irresponsible deployment of AI, we urge the Commissioners to immediately approve an investigation into OpenAI's prior NDAs, and to review current efforts apparently being undertaken by the company to ensure full compliance with SEC rules," the letter to Gary Gensler, the chair of the US Securities and Exchange Commission (SEC), said. The letter from whistleblower representatives was sent on 1 July and published by the Washington Post on Saturday after the news organisation obtained it from the office of the US senator Chuck Grassley.

An entity defending itself against infringement may attempt to A key capability in managing patent applications or a patent invalidate a patent by finding novelty-destroying prior art to that portfolio is comparing claims to other text, e.g. a patent patent. In all cases, the key task is to search through a set of specification. Because the language of claims is different from documents and determine whether those documents cover all language used elsewhere in the patent application or in non-patent aspects of each claim of the subject patent application or granted text, this has been challenging for computer based natural patent. Thus, a claim of a subject patent (application) may be language processing. We test two new LLM-based approaches considered a query to an information retrieval system whose and find that both provide substantially better performance than objective is to retrieve a document or set of documents that previously published values. The ability to match dense contain all aspects of that claim.