Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who will ultimately apply AI models. In this paper, we seek to understand if and how AI benchmarks are used to inform decision-making. Based on the analyses of interviews with 19 individuals who have used, or decided against using, benchmarks in their day-to-day work, we find that across these settings, participants use benchmarks as a signal of relative performance difference between models. However, whether this signal was considered a definitive sign of model superiority, sufficient for downstream decisions, varied. In academia, public benchmarks were generally viewed as suitable measures for capturing research progress. By contrast, in both product and policy, benchmarks -- even those developed internally for specific tasks -- were often found to be inadequate for informing substantive decisions. Of the benchmarks deemed unsatisfactory, respondents reported that their goals were neither well-defined nor reflective of real-world use. Based on the study results, we conclude that effective benchmarks should provide meaningful, real-world evaluations, incorporate domain expertise, and maintain transparency in scope and goals. They must capture diverse, task-relevant capabilities, be challenging enough to avoid quick saturation, and account for trade-offs in model performance rather than relying on a single score. Additionally, proprietary data collection and contamination prevention are critical for producing reliable and actionable results. By adhering to these criteria, benchmarks can move beyond mere marketing tricks into robust evaluative frameworks.

Much has been made of the abilities of the new developments in machine intelligence and in particular of what chatbots such as ChatGPT that are based on large language models (LLMs) are capable of. While these new pieces of software are impressive when it comes to generating text, some people in the computing community take this observation much further and, in my opinion, much too far. They claim programming will be a thing of the past. In a January 2023 Communications column, Matt Welsh put forward this opinion: "Programming will be obsolete. I believe the conventional idea of'writing a program' is headed for extinction, and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed. In situations where one needs a'simple' program (after all, not everything should require a model of hundreds of billions of parameters running on a cluster of GPUs), those programs will, themselves, be generated by an AI rather than coded by hand."14

It can now take in information from experiments and partial data as well as predict very large and complex protein structures. In all living organisms, there is a huge variety of proteins that regulate cell functions. Basically, everything that happens in the body, from controlling muscles and forming hair to transporting oxygen into the blood and digesting food, involves proteins. But proteins are also found outside the body in, for example, detergents and medical drugs. Proteins are large molecules consisting of 20 different amino acids that stick together in long rows, much like beads in a necklace.

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that \emph{BGTplanner} achieves an average improvement of 6.76\% in training performance compared to state-of-the-art baselines.

Recent advancements in large language models (LLMs) have led to significant improvements in various natural language processing tasks, but it is still challenging for LLMs to perform knowledge-intensive complex question answering due to LLMs' inefficacy in reasoning planning and the hallucination problem. A typical solution is to employ retrieval-augmented generation (RAG) coupled with chain-of-thought (CoT) reasoning, which decomposes complex questions into chain-like sub-questions and applies iterative RAG at each sub-question. However, prior works exhibit sub-optimal reasoning planning and overlook dynamic knowledge retrieval from heterogeneous sources. In this paper, we propose AtomR, a novel heterogeneous knowledge reasoning framework that conducts multi-source reasoning at the atomic level. Drawing inspiration from the graph modeling of knowledge, AtomR leverages large language models (LLMs) to decompose complex questions into combinations of three atomic knowledge operators, significantly enhancing the reasoning process at both the planning and execution stages. We also introduce BlendQA, a novel evaluation benchmark tailored to assess complex heterogeneous knowledge reasoning. Experiments show that AtomR significantly outperforms state-of-the-art baselines across three single-source and two multi-source reasoning benchmarks, with notable performance gains of 9.4% on 2WikiMultihop and 9.5% on BlendQA.

Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we introduce PRISM, a dataset that maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and fine-grained feedback in 8,011 live conversations with 21 LLMs. With PRISM, we contribute (i) wider geographic and demographic participation in feedback; (ii) census-representative samples for two countries (UK, US); and (iii) individualised ratings that link to detailed participant profiles, permitting personalisation and attribution of sample artefacts. We target subjective and multicultural perspectives on value-laden and controversial issues, where we expect interpersonal and cross-cultural disagreement. We use PRISM in three case studies to demonstrate the need for careful consideration of which humans provide what alignment data.

This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond.

Marietje Schaake is a former Dutch member of the European parliament. She is now the international policy director at Stanford University Cyber Policy Center and international policy fellow at Stanford's Institute for Human-Centred Artificial Intelligence. Her new book is entitled The Tech Coup: How to Save Democracy from Silicon Valley. In terms of power and political influence, what are the main differences between big tech and previous incarnations of big business? The difference is the role that these tech companies play in so many aspects of people's lives: in the state, the economy, geopolitics.

This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we hear from AfriClimate AI co-founder Amal Nammouchi, learn about the kidney exchange problem, and find out how to improve the interpretability of logistic regression models. This month, we had the pleasure of chatting to Amal Nammouchi, co-founder of AfriClimate AI, a grassroots community focused on using artificial intelligence to tackle climate challenges in Africa. Amal told us about the inspiration behind the initiative, some of their activities and projects, and plans for the future. In this blog post, Danial Dervovic writes about work presented at IJCAI 2024 on improving the interpretability of logistic regression models.