The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information. Due to the isolated architecture and the limited interaction, existing IR systems are unable to fully accommodate the shift from directly providing information to humans to indirectly serving large language models. In this paper, we propose Self-Retrieval, an end-to-end, LLM-driven information retrieval architecture that can fully internalize the required abilities of IR systems into a single LLM and deeply leverage the capabilities of LLMs during IR process. Specifically, Self-retrieval internalizes the corpus to retrieve into a LLM via a natural language indexing architecture. Then the entire retrieval process is redefined as a procedure of document generation and self-assessment, which can be end-to-end executed using a single large language model. Experimental results demonstrate that Self-Retrieval not only significantly outperforms previous retrieval approaches by a large margin, but also can significantly boost the performance of LLM-driven downstream applications like retrieval augumented generation.

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why this two-step strategy leads to biased inference in empirically plausible settings. More constructively, we propose a one-step strategy for valid inference that uses the upstream and downstream models jointly. The one-step strategy (i) substantially reduces bias in simulations; (ii) has quantitatively important effects in a leading application using CEO time-use data; and (iii) can be readily adapted by applied researchers.

Despite the critical need to align search targets with users' intention, retrievers often only prioritize query information without delving into the users' intended search context. Enhancing the capability of retrievers to understand intentions and preferences of users, akin to language model instructions, has the potential to yield more aligned search targets. Prior studies restrict the application of instructions in information retrieval to a task description format, neglecting the broader context of diverse and evolving search scenarios. Furthermore, the prevailing benchmarks utilized for evaluation lack explicit tailoring to assess instruction-following ability, thereby hindering progress in this field. In response to these limitations, we propose a novel benchmark,INSTRUCTIR, specifically designed to evaluate instruction-following ability in information retrieval tasks. Our approach focuses on user-aligned instructions tailored to each query instance, reflecting the diverse characteristics inherent in real-world search scenarios. Through experimental analysis, we observe that retrievers fine-tuned to follow task-style instructions, such as INSTRUCTOR, can underperform compared to their non-instruction-tuned counterparts. This underscores potential overfitting issues inherent in constructing retrievers trained on existing instruction-aware retrieval datasets.

This short paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is exemplified through a proof of concept centered on the ``reproductive number estimate of infectious diseases'' research theme, using a fine-tuned large language model (LLM) to automate the creation of structured records to populate a backend database that now goes beyond keywords. The result is a next-generation IR method accessible at https://orkg.org/usecases/r0-estimates.

The online diffusion of information related to Europe and migration has been little investigated from an external point of view. However, this is a very relevant topic, especially if users have had no direct contact with Europe and its perception depends solely on information retrieved online. In this work we analyse the information circulating online about Europe and migration after retrieving a large amount of data from social media (Twitter), to gain new insights into topics, magnitude, and dynamics of their diffusion. We combine retweets and hashtags network analysis with geolocation of users, linking thus data to geography and allowing analysis from an "outside Europe" perspective, with a special focus on Africa. We also introduce a novel approach based on cross-lingual quotes, i.e. when content in a language is commented and retweeted in another language, assuming these interactions are a proxy for connections between very distant communities. Results show how the majority of online discussions occurs at a national level, especially when discussing migration. Language (English) is pivotal for information to become transnational and reach far. Transnational information flow is strongly unbalanced, with content mainly produced in Europe and amplified outside. Conversely Europe-based accounts tend to be self-referential when they discuss migration-related topics. Football is the most exported topic from Europe worldwide. Moreover, important nodes in the communities discussing migration-related topics include accounts of official institutions and international agencies, together with journalists, news, commentators and activists.

This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-only models compared to encoder-decoder models. We investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG, (2) optimal architectural decisions for employing encoder-only PLMs in KPG, and (3) a performance comparison between in-domain encoder-only and encoder-decoder PLMs across varied resource settings. Our findings, derived from extensive experimentation in two domains reveal that with encoder-only PLMs, although KPE with Conditional Random Fields slightly excels in identifying present keyphrases, the KPG formulation renders a broader spectrum of keyphrase predictions. Additionally, prefix-LM fine-tuning of encoder-only PLMs emerges as a strong and data-efficient strategy for KPG, outperforming general-domain seq2seq PLMs. We also identify a favorable parameter allocation towards model depth rather than width when employing encoder-decoder architectures initialized with encoder-only PLMs. The study sheds light on the potential of utilizing encoder-only PLMs for advancing KPG systems and provides a groundwork for future KPG methods.

Web-scale search systems typically tackle the scalability challenge As one of the largest professional networking platforms globally, with a two-step paradigm: retrieval and ranking. The retrieval step, LinkedIn is a hub for job seekers and recruiters, with 65M+ job also known as candidate selection, often involves extracting standardized seekers utilizing the search and recommendation services weekly entities, creating an inverted index, and performing term to discover millions of open job listings. To enable realtime personalization matching for retrieval. Such traditional methods require manual for job seekers, we adopted the classic two-stage paradigm and time-consuming development of query models. In this paper, of retrieval and ranking to tackle the scalability challenge. The retrieval we discuss applying learning-to-retrieve technology to enhance layer, also known as candidate selection, chooses a small set LinkedIn's job search and recommendation systems. In the realm of of relevant jobs from the set of all jobs, after which the ranking layer promoted jobs, the key objective is to improve the quality of applicants, performs a more computationally expensive second-pass scoring thereby delivering value to recruiter customers. To achieve and sorting of the resulting candidate set. This paper focuses on this, we leverage confirmed hire data to construct a graph that improving the methodology and systems for retrieval.

Large Language Models are becoming the go-to solution for many natural language processing tasks, including in specialized domains where their few-shot capacities are expected to yield high performance in low-resource settings. Herein, we aim to assess the performance of Large Language Models for few shot clinical entity recognition in multiple languages. We evaluate named entity recognition in English, French and Spanish using 8 in-domain (clinical) and 6 out-domain gold standard corpora. We assess the performance of 10 auto-regressive language models using prompting and 16 masked language models used for text encoding in a biLSTM-CRF supervised tagger. We create a few-shot set-up by limiting the amount of annotated data available to 100 sentences. Our experiments show that although larger prompt-based models tend to achieve competitive F-measure for named entity recognition outside the clinical domain, this level of performance does not carry over to the clinical domain where lighter supervised taggers relying on masked language models perform better, even with the performance drop incurred from the few-shot set-up. In all experiments, the CO2 impact of masked language models is inferior to that of auto-regressive models. Results are consistent over the three languages and suggest that few-shot learning using Large language models is not production ready for named entity recognition in the clinical domain. Instead, models could be used for speeding-up the production of gold standard annotated data.

This paper explores a novel technique for improving recall in cross-language information retrieval (CLIR) systems using iterative query refinement grounded in the user's lexical-semantic space. The proposed methodology combines multi-level translation, semantic embedding-based expansion, and user profile-centered augmentation to address the challenge of matching variance between user queries and relevant documents. Through an initial BM25 retrieval, translation into intermediate languages, embedding lookup of similar terms, and iterative re-ranking, the technique aims to expand the scope of potentially relevant results personalized to the individual user. Comparative experiments on news and Twitter datasets demonstrate superior performance over baseline BM25 ranking for the proposed approach across ROUGE metrics. The translation methodology also showed maintained semantic accuracy through the multi-step process. This personalized CLIR framework paves the path for improved context-aware retrieval attentive to the nuances of user language.

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. A key highlight of our survey is the comparison between BERT's encoder-based models and the latest generative Large Language Models (LLMs), such as ChatGPT, which rely on decoders. Despite the popularity of LLMs, we find that for specific tasks, finely tuned BERT encoders still outperform, and at a lower deployment cost. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.