This study introduces De-DSI, a novel framework that fuses large language models (LLMs) with genuine decentralization for information retrieval, particularly employing the differentiable search index (DSI) concept in a decentralized setting. Focused on efficiently connecting novel user queries with document identifiers without direct document access, De-DSI operates solely on query-docid pairs. To enhance scalability, an ensemble of DSI models is introduced, where the dataset is partitioned into smaller shards for individual model training. This approach not only maintains accuracy by reducing the number of data each model needs to handle but also facilitates scalability by aggregating outcomes from multiple models. This aggregation uses a beam search to identify top docids and applies a softmax function for score normalization, selecting documents with the highest scores for retrieval. The decentralized implementation demonstrates that retrieval success is comparable to centralized methods, with the added benefit of the possibility of distributing computational complexity across the network. This setup also allows for the retrieval of multimedia items through magnet links, eliminating the need for platforms or intermediaries.

Extracting hyper-relations is crucial for constructing comprehensive knowledge graphs, but there are limited supervised methods available for this task. To address this gap, we introduce a zero-shot prompt-based method using OpenAI's GPT-3.5 model for extracting hyper-relational knowledge from text. Comparing our model with a baseline, we achieved promising results, with a recall of 0.77. Although our precision is currently lower, a detailed analysis of the model outputs has uncovered potential pathways for future research in this area.

The task of machine translation has seen major progress in recent times with the advent of large-scale Transformer-based models (e.g., Vaswani et al., 2017; Dehghani et al., 2019; Liu et al., 2020a). However, there has been less progress on language pairs that specifically involve morphologically rich languages. Moreover, although there has been previous work that builds linguistic structure into translation models to deal with morphological complexity (Sennrich and Haddow, 2016; Dalvi et al., 2017; Matthews et al., 2018), to the best to our knowledge there has not been work that applies such strategies to large-scale Transformer-based models. We hypothesize that providing Transformers access to structured linguistic representations can significantly boost their performance on translation into languages with complex morphology that encodes linguistic structure. In this work, we investigate two methods for introducing such structural bias into Transformer-based models. In the first method, we use the TP-Transformer (TPT) (Schlag et al., 2019), in which a traditional Transformer is augmented with Tensor Product Representations (TPRs) (Smolensky, 1990) ( 2).

Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content -- e.g., posts, news, products, comments --, but also user feedback -- e.g., ratings, views, reads, clicks --, together with context data -- user device, spatial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.