Backstage at one of the largest artificial intelligence conferences in the world, Gov. Gavin Newsom listened to two leaders in the field debate opposite views of a high-profile bill on his desk to protect Californians from the technology. "Honestly, I take advantage of opportunities like this," Newsom said recounting the exchange later during an interview at the Salesforce conference in San Francisco in mid-September. "I just watched them, and I was like, 'Here we go. Should I sign it, or should I not?' Then'absolutely,' 'absolutely not' and back and forth." The scene offered a peek into Newsom's deliberations on regulating the tech industry, including an explosion of AI companies, and the forces seeking to influence him during bill-signing season at the state Capitol.

Despite demonstrating superior performance across a variety of linguistic tasks, pre-trained large language models (LMs) often require fine-tuning on specific datasets to effectively address different downstream tasks. However, fine-tuning these LMs for downstream tasks necessitates collecting data from individuals, which raises significant privacy concerns. Federated learning (FL) has emerged as the de facto solution, enabling collaborative model training without sharing raw data. While promising, federated fine-tuning of large LMs faces significant challenges, including restricted access to model parameters and high computation, communication, and memory overhead. To address these challenges, this paper introduces \textbf{Fed}erated \textbf{P}roxy-\textbf{T}uning (FedPT), a novel framework for federated fine-tuning of black-box large LMs, requiring access only to their predictions over the output vocabulary instead of their parameters. Specifically, devices in FedPT first collaboratively tune a smaller LM, and then the server combines the knowledge learned by the tuned small LM with the knowledge learned by the larger pre-trained LM to construct a large proxy-tuned LM that can reach the performance of directly tuned large LMs. The experimental results demonstrate that FedPT can significantly reduce computation, communication, and memory overhead while maintaining competitive performance compared to directly federated fine-tuning of large LMs. FedPT offers a promising solution for efficient, privacy-preserving fine-tuning of large LMs on resource-constrained devices, broadening the accessibility and applicability of state-of-the-art large LMs.

The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern transformer-based architecture with a novel tokenized representation for symbolic music data. Framing the task as sequence-to-sequence translation rather than note-wise classification reduces alignment requirements and annotation costs, while allowing the prediction of more concise and accurate notation. To serialize symbolic music data, we design a custom tokenization stage based on compound tokens that carefully quantizes continuous values. This technique preserves more score information while reducing sequence lengths by $3.5\times$ compared to prior approaches. Using the transformer backbone, our method demonstrates better understanding of note values, rhythmic structure, and details such as staff assignment. When evaluated end-to-end using transcription metrics such as MUSTER, we achieve significant improvements over previous deep learning approaches and complex HMM-based state-of-the-art pipelines. Our method is also the first to directly predict notational details like trill marks or stem direction from performance data. Code and models are available at https://github.com/TimFelixBeyer/MIDI2ScoreTransformer

Probing learned concepts in large language models (LLMs) is crucial for understanding how semantic knowledge is encoded internally. Training linear classifiers on probing tasks is a principle approach to denote the vector of a certain concept in the representation space. However, the single vector identified for a concept varies with both data and training, making it less robust and weakening its effectiveness in real-world applications. To address this challenge, we propose an approach to approximate the subspace representing a specific concept. Built on linear probing classifiers, we extend the concept vectors into Gaussian Concept Subspace (GCS). We demonstrate GCS's effectiveness through measuring its faithfulness and plausibility across multiple LLMs with different sizes and architectures. Additionally, we use representation intervention tasks to showcase its efficacy in real-world applications such as emotion steering. Experimental results indicate that GCS concept vectors have the potential to balance steering performance and maintaining the fluency in natural language generation tasks.

The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Bladet"). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk.

AI-based systems such as language models can replicate and amplify social biases reflected in their training data. Among other questionable behavior, this can lead to LM-generated text--and text suggestions--that contain normatively inappropriate stereotypical associations. In this paper, we consider the question of how "debiasing" a language model impacts stories that people write using that language model in a predictive text scenario. We find that (n=414), in certain scenarios, language model suggestions that align with common social stereotypes are more likely to be accepted by human authors. Conversely, although anti-stereotypical language model suggestions sometimes lead to an increased rate of anti-stereotypical stories, this influence is far from sufficient to lead to "fully debiased" stories.

Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinformation that impacts human well-being. Objectives/Methods: We analyzed compliance to requests to generate misleading content about medications in settings where models know the request is illogical. We investigated whether in-context directions and instruction-tuning of LLMs to prioritize logical reasoning over compliance reduced misinformation risk. Results: While all frontier LLMs complied with misinformation requests, both prompt-based and parameter-based approaches can improve the detection of logic flaws in requests and prevent the dissemination of medical misinformation. Conclusion: Shifting LLMs to prioritize logic over compliance could reduce risks of exploitation for medical misinformation.

Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of randomly sampled negative entities. Due to the limited size of the set of negatives, a simple rule-based baseline can achieve state-of-the-art results, which simply ranks entities higher based on the validity of their type. As a consequence of these insights, we reevaluate current approaches for inductive link prediction on several benchmarks using the link prediction protocol usually applied to the transductive setting. As some inductive methods suffer from scalability issues when evaluated in this setting, we propose and apply additionally an improved sampling protocol, which does not suffer from the problem mentioned above. The results of our evaluation differ drastically from the results reported in so far.

We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important.

Gov. Gavin Newsom on Sunday vetoed SB 1047, an artificial intelligence safety bill that would have established requirements for developers of advanced AI models to create protocols aimed at preventing catastrophes. The bill, introduced by Sen. Scott Wiener (D-San Francisco), would have required developers to submit their safety plans to the state attorney general, who could hold them liable if AI models they directly control were to cause harm or imminent threats to public safety. Additionally, the legislation would have required tech firms to be able to turn off the AI models they directly control if things went awry. In his veto message, Newsom said the legislation could give the public a "false sense of security about controlling this fast-moving technology" because it targeted only large-scale and expensive AI models and not smaller, specialized systems. "While well-intentioned, SB 1047 does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data," Newsom's veto message stated.