Despite the recent advancements in information retrieval (IR), zero-shot IR remains a significant challenge, especially when dealing with new domains, languages, and newly-released use cases that lack historical query traffic from existing users. For such cases, it is common to use query augmentations followed by fine-tuning pre-trained models on the document data paired with synthetic queries. In this work, we propose a novel Universal Document Linking (UDL) algorithm, which links similar documents to enhance synthetic query generation across multiple datasets with different characteristics. UDL leverages entropy for the choice of similarity models and named entity recognition (NER) for the link decision of documents using similarity scores. Our empirical studies demonstrate the effectiveness and universality of the UDL across diverse datasets and IR models, surpassing state-of-the-art methods in zero-shot cases. The developed code for reproducibility is included in https://github.com/eoduself/UDL

Dense retrieval systems are commonly used for information retrieval (IR). They rely on learning text representations through an encoder and usually require supervised modeling via labelled data which can be costly to obtain or simply unavailable. In this study, we introduce a novel unsupervised text representation learning technique via instruction-tuning the pre-trained encoder-decoder large language model (LLM) under the dual-encoder retrieval framework. We demonstrate on multiple languages that the corpus representation can be augmented by the representations of relevant synthetic queries generated by the instruct-tuned LLM founded on the Rao-Blackwell theorem. Furthermore, we effectively align the query and corpus text representation with self-instruct tuning. We evaluate our proposed method under low-resource settings on three English, two German and one Portuguese retrieval datasets measuring NDCG@10, MRR@100, Recall@100. We significantly improve the average zero-shot retrieval performance on all metrics, increasing out-of-box FLAN-T5 model variations by [4.73%, 6.15%] in absolute NDCG@10 and exceeding four supervised dense retrievers.

The size of embeddings generated by large language models can negatively affect system latency and model size in certain downstream practical applications (e.g. KNN search). In this work, we propose EmbedTextNet, a light add-on network that can be appended to an arbitrary language model to generate a compact embedding without requiring any changes in its architecture or training procedure. Specifically, we use a correlation penalty added to the weighted reconstruction loss that better captures the informative features in the text embeddings, which improves the efficiency of the language models. We evaluated EmbedTextNet on three different downstream tasks: text similarity, language modelling, and text retrieval. Empirical results on diverse benchmark datasets demonstrate the effectiveness and superiority of EmbedTextNet compared to state-of-art methodologies in recent works, especially in extremely low dimensional embedding sizes. The developed code for reproducibility is included in the supplementary material.

In this work, we investigate Data Augmentation methods to improve the performance of state-of-the-art models for four different downstream tasks. Specifically, we propose Generative Adversarial Network using Language Models (GAN-LM) approach that combines a deep generative model with a pre-trained language model to produce diverse augmentations. We compare the GAN-LM to various conventional methods in non-contextual- and contextual-levels on four public datasets: ZESHEL for zero-shot entity linking, TREC for question classification, STS-B for sentence pairs semantic textual similarity (STS), and mSTS for multilingual sentence pairs STS. Additionally, we subsample these datasets to study the impact of such augmentations in low-resource settings where limited amounts of training data is available. Compared to the state-of-the-art methods in downstream tasks, we mostly achieve the best performance using GAN-LM approach. Finally, we investigate the way of combining the GAN-LM with other augmentation methods to complement our proposed approach. The developed code for reproducibility is included in the supplementary material.