Is ChatGPT good at search? investigating large language models as re-ranking agents
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization
across various language-related tasks, including search engines. However, existing work …
across various language-related tasks, including search engines. However, existing work …
Dense text retrieval based on pretrained language models: A survey
Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …
required to return relevant information resources to user's queries in natural language. From …
T2ranking: A large-scale chinese benchmark for passage ranking
Passage ranking involves two stages: passage retrieval and passage re-ranking, which are
important and challenging topics for both academics and industries in the area of …
important and challenging topics for both academics and industries in the area of …
One-shot labeling for automatic relevance estimation
S MacAvaney, L Soldaini - Proceedings of the 46th International ACM …, 2023 - dl.acm.org
Dealing with unjudged documents (" holes") in relevance assessments is a perennial
problem when evaluating search systems with offline experiments. Holes can reduce the …
problem when evaluating search systems with offline experiments. Holes can reduce the …
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Many text generation systems benefit from using a retriever to retrieve passages from a
textual knowledge corpus (eg, Wikipedia) which are then provided as additional context to …
textual knowledge corpus (eg, Wikipedia) which are then provided as additional context to …
Leveraging llms for unsupervised dense retriever ranking
In this paper we present Large Language Model Assisted Retrieval Model Ranking
(LARMOR), an effective unsupervised approach that leverages LLMs for selecting which …
(LARMOR), an effective unsupervised approach that leverages LLMs for selecting which …
Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction
Recent progress in neural information retrieval has demonstrated large gains in quality,
while often sacrificing efficiency and interpretability compared to classical approaches. We …
while often sacrificing efficiency and interpretability compared to classical approaches. We …
Systematic evaluation of neural retrieval models on the touché 2020 argument retrieval subset of BEIR
The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR
benchmark---a combination of different IR evaluation datasets. Interestingly, previous …
benchmark---a combination of different IR evaluation datasets. Interestingly, previous …
Rethinking the role of token retrieval in multi-vector retrieval
Multi-vector retrieval models such as ColBERT [Khattab et al., 2020] allow token-level
interactions between queries and documents, and hence achieve state of the art on many …
interactions between queries and documents, and hence achieve state of the art on many …
Noisy perturbations for estimating query difficulty in dense retrievers
Estimating query difficulty, also known as Query Performance Prediction (QPP), is
concerned with assessing the retrieval quality of a ranking method for an input query. Most …
concerned with assessing the retrieval quality of a ranking method for an input query. Most …