When Reddit said last month it would block unauthorized data scraping from its site, most of us assumed it was to tackle chatbot training. It turns out the site/service/fandom battleground also appears to be blocking search engines other than Brave and Google, the latter of which reportedly inked a deal earlier this year with Reddit worth 60 million annually. A Reddit spokesperson told Engadget the empty search results are because these engines won't agree to the company's requirements for AI training. The company says it's in discussions with several of them. Bing and DuckDuckGo both appear to be affected.

Point-based image editing enables accurate and flexible control through content dragging. However, the role of text embedding in the editing process has not been thoroughly investigated. A significant aspect that remains unexplored is the interaction between text and image embeddings. In this study, we show that during the progressive editing of an input image in a diffusion model, the text embedding remains constant. As the image embedding increasingly diverges from its initial state, the discrepancy between the image and text embeddings presents a significant challenge. Moreover, we found that the text prompt significantly influences the dragging process, particularly in maintaining content integrity and achieving the desired manipulation. To utilize these insights, we propose DragText, which optimizes text embedding in conjunction with the dragging process to pair with the modified image embedding. Simultaneously, we regularize the text optimization process to preserve the integrity of the original text prompt. Our approach can be seamlessly integrated with existing diffusion-based drag methods with only a few lines of code.

Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given session (e.g. do they want to watch short videos or a movie or play games; are they shopping for a camping trip), it becomes easier to provide high-quality recommendations. In this paper, we introduce IntentRec, a novel recommendation framework based on hierarchical multi-task neural network architecture that tries to estimate a user's latent intent using their short- and long-term implicit signals as proxies and uses the intent prediction to predict the next item user is likely to engage with. By directly leveraging the intent prediction, we can offer accurate and personalized recommendations to users. Our comprehensive experiments on Netflix user engagement data show that IntentRec outperforms the state-of-the-art next-item and next-intent predictors. We also share several findings and downstream applications of IntentRec.

In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area.

Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However, the lack of clear agreement on which fairness definition to apply in specific contexts (\textit{e.g.,} medium-sized LMs versus large-sized LMs) and the complexity of understanding the distinctions between these definitions can create confusion and impede further progress. To this end, this paper proposes a systematic survey that clarifies the definitions of fairness as they apply to LMs. Specifically, we begin with a brief introduction to LMs and fairness in LMs, followed by a comprehensive, up-to-date overview of existing fairness notions in LMs and the introduction of a novel taxonomy that categorizes these concepts based on their foundational principles and operational distinctions. We further illustrate each definition through experiments, showcasing their practical implications and outcomes. Finally, we discuss current research challenges and open questions, aiming to foster innovative ideas and advance the field. The implementation and additional resources are publicly available at https://github.com/LavinWong/Fairness-in-Large-Language-Models/tree/main/definitions.

Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily detected upon manual or automated review (e.g., via byte entropy). However, the exploration of innocuous human-understandable malicious prompts augmented with adversarial injections remains limited. In this research, we explore converting a nonsensical suffix attack into a sensible prompt via a situation-driven contextual re-writing. This allows us to show suffix conversion without any gradients, using only LLMs to perform the attacks, and thus better understand the scope of possible risks. We combine an independent, meaningful adversarial insertion and situations derived from movies to check if this can trick an LLM. The situations are extracted from the IMDB dataset, and prompts are defined following a few-shot chain-of-thought prompting. Our approach demonstrates that a successful situation-driven attack can be executed on both open-source and proprietary LLMs. We find that across many LLMs, as few as 1 attempt produces an attack and that these attacks transfer between LLMs.

Music is a complex auditory stimulus capable of eliciting significant changes in brain activity, influencing cognitive processes such as memory, attention, and emotional regulation. However, the underlying mechanisms of music-induced cognitive processes remain largely unknown. Organoid intelligence and deep learning models show promise for simulating and analyzing these neural responses to classical music, an area significantly unexplored in computational neuroscience. Hence, we present the PyOrganoid library, an innovative tool that facilitates the simulation of organoid learning models, integrating sophisticated machine learning techniques with biologically inspired organoid simulations. Our study features the development of the Pianoid model, a "deep organoid learning" model that utilizes a Bidirectional LSTM network to predict EEG responses based on audio features from classical music recordings. This model demonstrates the feasibility of using computational methods to replicate complex neural processes, providing valuable insights into music perception and cognition. Likewise, our findings emphasize the utility of synthetic models in neuroscience research and highlight the PyOrganoid library's potential as a versatile tool for advancing studies in neuroscience and artificial intelligence.

We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annotation in multilingual news posts. We focus on the early days of the Israel War on Gaza as a case study. The task aims to foster collaboration in developing annotation guidelines for subjective tasks by creating frameworks for analyzing diverse narratives highlighting potential bias and propaganda. In a spirit of fostering and encouraging diversity, we address the problem from a multilingual perspective, namely within five languages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams participated in two annotation subtasks: bias (16 teams) and propaganda (6 teams). The teams competed in four evaluation tracks: guidelines development, annotation quality, annotation quantity, and consistency. Collectively, the teams produced 129,800 data points. Key findings and implications for the field are discussed.

Music two-tower multimodal systems integrate audio and text modalities into a joint audio-text space, enabling direct comparison between songs and their corresponding labels. These systems enable new approaches for classification and retrieval, leveraging both modalities. Despite the promising results they have shown for zero-shot classification and retrieval tasks, closer inspection of the embeddings is needed. This paper evaluates the inherent zero-shot properties of joint audio-text spaces for the case-study of instrument recognition. We present an evaluation and analysis of two-tower systems for zero-shot instrument recognition and a detailed analysis of the properties of the pre-joint and joint embeddings spaces. Our findings suggest that audio encoders alone demonstrate good quality, while challenges remain within the text encoder or joint space projection. Specifically, two-tower systems exhibit sensitivity towards specific words, favoring generic prompts over musically informed ones. Despite the large size of textual encoders, they do not yet leverage additional textual context or infer instruments accurately from their descriptions. Lastly, a novel approach for quantifying the semantic meaningfulness of the textual space leveraging an instrument ontology is proposed. This method reveals deficiencies in the systems' understanding of instruments and provides evidence of the need for fine-tuning text encoders on musical data.

As the reality TV sector increasingly has to deal with the good and bad impacts of AI, lawyer John Delaney says there are growing legal and regulatory issues. "For example, AI could be used to suggest scenarios or storylines, to edit episodes and to anticipate and assess audience reactions to in-show developments," says Mr Delaney, who is a partner at commercial law firm Perkins Coie, and who advises companies on AI and other technology issues. "However, production companies will need to consider to what extent the new Writers Guild of America agreement [to strictly restrict the use of AI] might limit their ability to use AI in connection with their reality TV programs." He adds that away from making the shows a growing issue that reality TV producers and contestants are facing is a proliferation of unauthorized, AI-generated images and videos. Mr Delaney points to generative AI tools such as chatbot ChatGPT being used to create new content from reality TV footage.