Challenge: Existing search methods for COVID-19 are not based on scientific data, but use a neural re-ranking model pre-trained on scientific text.
Approach: They propose a zero-shot ranking algorithm that adapts to COVID-related scientific literature . they use a neural re-ranking model pre-trained on scientific text and filters the target document .
Outcome: The proposed algorithm outperforms models on the TREC COVID Round 1 leaderboard . it outperformed models that do not rely on TREC-COVID data .

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GENRA: Enhancing Zero-shot Retrieval with Rank Aggregation (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have been shown to perform zero-shot document retrieval, a process that typically consists of two steps: retrieving relevant documents, and re-ranking them based on their relevance to the query.
Approach: They propose a new approach to zero-shot document retrieval that incorporates rank aggregation to improve retrieval effectiveness.
Outcome: The proposed approach improves existing methods on benchmark datasets and shows that it can perform zero-shot retrieval.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)

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Challenge: COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge .
Approach: They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available .
Outcome: The proposed schema outperforms PubMed search in clinical trials.
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)

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Challenge: Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting.
Approach: They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset.
Outcome: The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting.
Document Classification for COVID-19 Literature (2020.findings-emnlp)

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Challenge: a global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide variety of fields.
Approach: They analyze a LitCovid dataset to find out how classification models can help organize COVID-19 research papers.
Outcome: The proposed model outperforms all baseline models on the LitCovid dataset . it also outperformed BioBERT and other models with micro-F1 and accuracy scores of 86% and 75% .
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (D19-1)

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Challenge: 0Shot-TC is a challenging NLU problem to which little attention has been paid by the research community.
Approach: They propose to use a standardized evaluation system to classify text snippets without seeing task specific training data.
Outcome: The proposed model is based on a set of standardized evaluations and state-of-the-art baselines.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Challenge: a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach: a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome: a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)

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Challenge: a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms.
Approach: They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia .
Outcome: The proposed dataset shows that it is useful in monolingual vs. multilingual settings.
LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews (2025.findings-acl)

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Challenge: Existing methods for abstract screening focus on binary classification settings; existing question answering (QA) based ranking approaches suffer from error propagation.
Approach: They propose a systematic literature review (SLR) method that uses large language models to evaluate the SLR's inclusion and exclusion criteria.
Outcome: The proposed method outperforms existing question answering (QA) based methods by 5-10 pp. in mean precision.
Transitioning from benchmarks to a real-world case of information-seeking in Scientific Publications (2023.findings-acl)

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Challenge: Scientific publications are one of the primary means by which researchers disseminate their findings and discoveries to the community, but the amount of information to go through can easily become daunting and challenging.
Approach: They propose a zero-shot text search case for information seeking in scientific papers that uses a biomedical IR benchmark and an industrial case of vitamin B's impact on health.
Outcome: The proposed model is based on a zero-shot text search case in scientific publications . it shows that benchmarks are not faithfully representative of the task at hand .
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization (2021.naacl-srw)

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Challenge: Existing semantic visualization methods are limited in finding connections between corpora targeting a specific topic.
Approach: They propose to use semantic visualization to explore large datasets of complex networks by exploiting the semantics of the relations in them.
Outcome: The proposed method can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them.

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