title: Know When to Fuse: Investigating Non-English Hybrid Retrieval in the
    Legal Domain
summary: Hybrid search has emerged as an effective strategy to offset the limitations
  of different matching paradigms, especially in out-of-domain contexts where
  notable improvements in retrieval quality have been observed. However, existing
  research predominantly focuses on a limited set of retrieval methods, evaluated
  in pairs on domain-general datasets exclusively in English. In this work, we
  study the efficacy of hybrid search across a variety of prominent retrieval
  models within the unexplored field of law in the French language, assessing
  both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot
  context, fusing different domain-general models consistently enhances
  performance compared to using a standalone model, regardless of the fusion
  method. Surprisingly, when models are trained in-domain, we find that fusion
  generally diminishes performance relative to using the best single system,
  unless fusing scores with carefully tuned weights. These novel insights, among
  others, expand the applicability of prior findings across a new field and
  language, and contribute to a deeper understanding of hybrid search in
  non-English specialized domains.
paper_authors._id: 66d8517dabb339ab61725c6a
paper_authors.name: Antoine Louis
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T15:25:00.048Z
paper_authors._id: 5f298d07e923d665e6162802
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1600354981837-5f298d07e923d665e6162802.jpeg
paper_authors.fullname: Antoine Louis
paper_authors.type: user
paper_authors.user: antoinelouis
paper_authors._id: 66d8517dabb339ab61725c6b
paper_authors.name: Gijs van Dijck
paper_authors._id: 66d8517dabb339ab61725c6c
paper_authors.name: Gerasimos Spanakis
paper_title: Know When to Fuse: Investigating Non-English Hybrid Retrieval in the
    Legal Domain
paper_summary: Hybrid search has emerged as an effective strategy to offset the limitations
  of different matching paradigms, especially in out-of-domain contexts where
  notable improvements in retrieval quality have been observed. However, existing
  research predominantly focuses on a limited set of retrieval methods, evaluated
  in pairs on domain-general datasets exclusively in English. In this work, we
  study the efficacy of hybrid search across a variety of prominent retrieval
  models within the unexplored field of law in the French language, assessing
  both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot
  context, fusing different domain-general models consistently enhances
  performance compared to using a standalone model, regardless of the fusion
  method. Surprisingly, when models are trained in-domain, we find that fusion
  generally diminishes performance relative to using the best single system,
  unless fusing scores with carefully tuned weights. These novel insights, among
  others, expand the applicability of prior findings across a new field and
  language, and contribute to a deeper understanding of hybrid search in
  non-English specialized domains.
paper_ai_keywords: hybrid search retrieval quality matching paradigms out-of-domain contexts domain-general datasets retrieval methods zero-shot scenarios in-domain scenarios fusion domain-general models French language supervised insights non-English specialized domains