Query Understanding in the Age of Large Language Models

Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM). In this position paper, we describe a generic framework for interactive query-rewriting using LLMs. Our proposal aims to unfold new opportunities for improved and transparent intent understanding while building high-performance retrieval systems using LLMs. A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language that can be further refined, controlled, and edited before the final retrieval phase. The ability to present, interact, and reason over the underlying machine intent in natural language has profound implications on transparency, ranking performance, and a departure from the traditional way in which supervised signals were collected for understanding intents. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.Comment: Accepted to GENIR(SIGIR'23

Context Aware Query Rewriting for Text Rankers using LLM

Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranking. Queries are typically rewritten during query processing time for better query modelling for the downstream ranker. With the advent of large-language models (LLMs), there have been initial investigations into using generative approaches to generate pseudo documents to tackle this inherent vocabulary gap. In this work, we analyze the utility of LLMs for improved query rewriting for text ranking tasks. We find that there are two inherent limitations of using LLMs as query re-writers -- concept drift when using only queries as prompts and large inference costs during query processing. We adopt a simple, yet surprisingly effective, approach called context aware query rewriting (CAR) to leverage the benefits of LLMs for query understanding. Firstly, we rewrite ambiguous training queries by context-aware prompting of LLMs, where we use only relevant documents as context.Unlike existing approaches, we use LLM-based query rewriting only during the training phase. Eventually, a ranker is fine-tuned on the rewritten queries instead of the original queries during training. In our extensive experiments, we find that fine-tuning a ranker using re-written queries offers a significant improvement of up to 33% on the passage ranking task and up to 28% on the document ranking task when compared to the baseline performance of using original queries

Efficient Neural Ranking using Forward Indexes and Lightweight Encoders

Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency. We propose Fast-Forward indexes -- vector forward indexes which exploit the semantic matching capabilities of dual-encoder models for efficient and effective re-ranking. Our framework enables re-ranking at very high retrieval depths and combines the merits of both lexical and semantic matching via score interpolation. Furthermore, in order to mitigate the limitations of dual-encoders, we tackle two main challenges: Firstly, we improve computational efficiency by either pre-computing representations, avoiding unnecessary computations altogether, or reducing the complexity of encoders. This allows us to considerably improve ranking efficiency and latency. Secondly, we optimize the memory footprint and maintenance cost of indexes; we propose two complementary techniques to reduce the index size and show that, by dynamically dropping irrelevant document tokens, the index maintenance efficiency can be improved substantially. We perform evaluation to show the effectiveness and efficiency of Fast-Forward indexes -- our method has low latency and achieves competitive results without the need for hardware acceleration, such as GPUs.Comment: Accepted at ACM TOIS. arXiv admin note: text overlap with arXiv:2110.0605

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that number of hops in the path from each sensor to its BS is bounded by hmax, and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios

Hgt1p, a high affinity glutathione transporter from the yeast Saccharomyces cerevisiae

A high affinity glutathione transporter has been identified, cloned, and characterized from the yeast Saccharomyces cerevisiae. This transporter, Hgt1p, represents the first high affinity glutathione transporter to be described from any system so far. The strategy for the identification involved investigating candidate glutathione transporters from the yeast genome sequence project followed by genetic and physiological investigations. This approach revealed HGT1 (open reading frame YJL212c) as encoding a high affinity glutathione transporter. Yeast strains deleted in HGT1 did not show any detectable plasma membrane glutathione transport, and hgt1Δ disruptants were non-viable in a glutathione biosynthetic mutant (gsh1Δ) background. The glutathione repressible transport activity observed in wild type cells was also absent in the hgt1Δ strains. The transporter was cloned and kinetic studies indicated that Hgt1p had a high affinity for glutathione (Km = 54 μM)) and was not sensitive to competition by amino acids, dipeptides, or other tripeptides. Significant inhibition was observed, however, with oxidized glutathione and glutathione conjugates. The transporter reveals a novel class of transporters that has homologues in other yeasts and plants but with no apparent homologues in either Escherichia coli or in higher eukaryotes other than plants

Assessment of Environmental Factors in Occurrence of Uterine Fibroids Among North Indian Women Aged between 35- 49yrs.

Uterine fibroids have always been the prime medical issue for females, especially the ones travelling in their reproductive age ( 20-35), with some studies reporting 20-80% of women developing fibroids by th

Safer plant-based nanoparticles for combating antibiotic resistance in bacteria: a comprehensive review on its potential applications, recent advances, and future perspective

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGBackground: Antibiotic resistance is one of the current threats to human health, forcing the use of drugs that are more noxious, costlier, and with low efficiency. There are several causes behind antibiotic resistance, including over-prescription of antibiotics in both humans and livestock. In this scenario, researchers are shifting to new alternatives to fight back this concerning situation. Scope and approach: Nanoparticles have emerged as new tools that can be used to combat deadly bacterial infections directly or indirectly to overcome antibiotic resistance. Although nanoparticles are being used in the pharmaceutical industry, there is a constant concern about their toxicity toward human health because of the involvement of well-known toxic chemicals (i.e., sodium/potassium borohydride) making their use very risky for eukaryotic cells. Key findings and conclusions: Multiple nanoparticle-based approaches to counter bacterial infections, providing crucial insight into the design of elements that play critical roles in the creation of antimicrobial nanotherapeutic drugs, are currently underway. In this context, plant-based nanoparticles will be less toxic than many other forms, which constitute promising candidates to avoid widespread damage to the microbiome associated with current practices. This article aims to review the actual knowledge on plant-based nanoparticle products for antibiotic resistance and the possible replacement of antibiotics to treat multidrug-resistant bacterial infections.Xunta de Galicia | Ref. ED431F 2020/12Xunta de Galicia | Ref. ED481A 2021/313Ministerio de Ciencia, Innovación y Universidades | Ref. RYC-2017-2289

Respostas ecofisiológicas e morfológicas do pau-rosa (Aniba rosaeodora Ducke) aos diferentes níveis de sombreamento, em condição de viveiro.

Publicado também em: Boletim da Faculdade de Ciências Agrárias do Pará, Belém, PA, n. 30, p. 119-132, jul./dez. 1998