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Found 5 papers, 3 papers with code
Top-Down Partitioning for Efficient List-Wise Ranking
Large Language Models (LLMs) have significantly impacted many facets of natural language processing and information retrieval.
Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
In re-ranking, we investigate operating points of adaptive re-ranking with different first stages to find the point in graph traversal where the first stage no longer has an effect on the performance of the overall retrieval pipeline.
"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine-tuning the parameters of an LLM specific to a downstream task with labeled examples, a small number of such examples is appended to a prompt instruction for controlling the decoder's generation process.
Exploiting Positional Bias for Query-Agnostic Generative Content in Search
We demonstrate such defects by showing that non-relevant text--such as promotional content--can be easily injected into a document without adversely affecting its position in search results.
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding.
