CITATION.cff

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title: >-
  Incorporating Relevance Feedback for
  Information-Seeking Retrieval using Few-Shot
  Document Re-Ranking
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Tim
    family-names: Baumgärtner
    email: baumgaertner.t@gmail.com
    affiliation: TU Darmstadt
  - given-names: 'Leonardo F. R. '
    family-names: Ribeiro
    affiliation: Amazon Alexa AI
  - given-names: Nils
    family-names: Reimers
    affiliation: cohere.ai
  - given-names: Iryna
    family-names: Gurevych
    affiliation: TU Darmstadt
abstract: >-
  Pairing a lexical retriever with a neural
  re-ranking model has set state-of-the-art
  performance on large-scale information retrieval
  datasets. This pipeline covers scenarios like
  question answering or navigational queries,
  however, for information-seeking scenarios, users
  often provide information on whether a document is
  relevant to their query in form of clicks or
  explicit feedback. Therefore, in this work, we
  explore how relevance feedback can be directly
  integrated into neural re-ranking models by
  adopting few-shot and parameter-efficient learning
  techniques. Specifically, we introduce a kNN
  approach that re-ranks documents based on their
  similarity with the query and the documents the
  user considers relevant. Further, we explore
  Cross-Encoder models that we pre-train using
  meta-learning and subsequently fine-tune for each
  query, training only on the feedback documents. To
  evaluate our different integration strategies, we
  transform four existing information retrieval
  datasets into the relevance feedback scenario.
  Extensive experiments demonstrate that integrating
  relevance feedback directly in neural re-ranking
  models improves their performance, and fusing
  lexical ranking with our best performing neural
  re-ranker outperforms all other methods by 5.2
  nDCG@20.
repository-code: https://github.com/UKPLab/incorporating-relevance
preferred-citation:
  type: proceedings
  authors:
  - given-names: Tim
    family-names: Baumgärtner
  - given-names: 'Leonardo F. R. '
    family-names: Ribeiro
  - given-names: Nils
    family-names: Reimers
  - given-names: Iryna
    family-names: Gurevych
  title: >-
    Incorporating Relevance Feedback for
    Information-Seeking Retrieval using Few-Shot
    Document Re-Ranking
  year: 2022
  url: https://arxiv.org/abs/2210.10695