SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic
  Association and Long Story Comprehension
Retrieval-augmented generation (RAG) over long documents typically involves
splitting the text into smaller chunks, which serve as the basic units for
retrieval. However, due to dependencies across the original document,
contextual information is often essential for accurately interpreting each
chunk. To address this, prior work has explored encoding longer context windows
to produce embeddings for longer chunks. Despite these efforts, gains in
retrieval and downstream tasks remain limited. This is because (1) longer
chunks strain the capacity of embedding models due to the increased amount of
information they must encode, and (2) many real-world applications still
require returning localized evidence due to constraints on model or human
bandwidth.
  We propose an alternative approach to this challenge by representing short
chunks in a way that is conditioned on a broader context window to enhance
retrieval performance -- i.e., situating a chunk's meaning within its context.
We further show that existing embedding models are not well-equipped to encode
such situated context effectively, and thus introduce a new training paradigm
and develop the situated embedding models (SitEmb). To evaluate our method, we
curate a book-plot retrieval dataset specifically designed to assess situated
retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3
substantially outperforms state-of-the-art embedding models, including several
with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model
further improves performance by over 10% and shows strong results across
different languages and several downstream applications.
68919172f01a094725f8355d
Junjie Wu
claimed_verified
2025-08-06T19:21:26.812Z
64d36ca3036ae0b3756588e3
/avatars/0d7dfbd681b1157a38d0f0a86f19b702.svg
Junjie Wu
user
junjiewu
68919172f01a094725f8355e
Jiangnan Li
68919172f01a094725f8355f
Yuqing Li
68919172f01a094725f83560
Lemao Liu
68919172f01a094725f83561
Liyan Xu
claimed_verified
2025-08-19T10:50:24.142Z
650f0fac11f3210cf7a8a849
/avatars/687d56c3a6d4f5cdb34e424cdcff954d.svg
Liyan Xu
user
lxucs
68919172f01a094725f83562
Jiwei Li
68919172f01a094725f83563
Dit-Yan Yeung
68919172f01a094725f83564
Jie Zhou
68919172f01a094725f83565
Mo Yu
claimed_verified
2025-08-11T06:49:18.431Z
67af92045a86287292026808
/avatars/8bad9272fe73ba04e077b5484837c8d3.svg
Mo
user
BishopGorov
SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic
  Association and Long Story Comprehension
Retrieval-augmented generation (RAG) over long documents typically involves
splitting the text into smaller chunks, which serve as the basic units for
retrieval. However, due to dependencies across the original document,
contextual information is often essential for accurately interpreting each
chunk. To address this, prior work has explored encoding longer context windows
to produce embeddings for longer chunks. Despite these efforts, gains in
retrieval and downstream tasks remain limited. This is because (1) longer
chunks strain the capacity of embedding models due to the increased amount of
information they must encode, and (2) many real-world applications still
require returning localized evidence due to constraints on model or human
bandwidth.
  We propose an alternative approach to this challenge by representing short
chunks in a way that is conditioned on a broader context window to enhance
retrieval performance -- i.e., situating a chunk's meaning within its context.
We further show that existing embedding models are not well-equipped to encode
such situated context effectively, and thus introduce a new training paradigm
and develop the situated embedding models (SitEmb). To evaluate our method, we
curate a book-plot retrieval dataset specifically designed to assess situated
retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3
substantially outperforms state-of-the-art embedding models, including several
with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model
further improves performance by over 10% and shows strong results across
different languages and several downstream applications.
A new training paradigm and situated embedding models (SitEmb) enhance retrieval performance by conditioning short text chunks on broader context windows, outperforming state-of-the-art models with fewer parameters.
Retrieval-augmented generation (RAG) context window embedding models situated context situated embedding models (SitEmb) BGE-M3 downstream applications