Spectrum Projection Score: Aligning Retrieved Summaries with Reader
  Models in Retrieval-Augmented Generation
Large Language Models (LLMs) have shown improved generation performance
through retrieval-augmented generation (RAG) following the retriever-reader
paradigm, which supplements model inputs with externally retrieved knowledge.
However, prior work often evaluates RAG holistically, assessing the retriever
and reader jointly, making it difficult to isolate the true contribution of
retrieval, particularly given the prompt sensitivity of LLMs used as readers.
We introduce Spectrum Projection Score (SPS), a lightweight, supervision-free
metric that allows the reader to gauge the semantic alignment of a retrieved
summary with its hidden representation by comparing the area formed by
generated tokens from the summary, and the principal directions of subspace in
the reader and to measure the relevance. Building on SPS we present xCompress,
an inference time controller framework that dynamically samples, ranks, and
compresses retrieval summary candidates. Extensive experiments on five QA
benchmarks with four open source LLMs show that SPS not only enhances
performance across a range of tasks but also provides a principled perspective
on the interaction between retrieval and generation.
689a1c8afab6fdd2e52ac3b2
Zhanghao Hu
689a1c8afab6fdd2e52ac3b3
Qinglin Zhu
claimed_verified
2025-08-13T07:21:42.431Z
65313271e5b85656bd480095
/avatars/121d67608aad0d31d4875eaf5cdd88a0.svg
Qinglin Zhu
user
ALICKZHU
689a1c8afab6fdd2e52ac3b4
Siya Qi
689a1c8afab6fdd2e52ac3b5
Yulan He
689a1c8afab6fdd2e52ac3b6
Hanqi Yan
689a1c8afab6fdd2e52ac3b7
Lin Gui
claimed_verified
2025-08-13T07:21:44.951Z
687facb9b27afe94a71b7455
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/hqrApQdsEufqXpXoKu_mn.jpeg
Lin Gui
user
Monta3Pt
Spectrum Projection Score: Aligning Retrieved Summaries with Reader
  Models in Retrieval-Augmented Generation
Large Language Models (LLMs) have shown improved generation performance
through retrieval-augmented generation (RAG) following the retriever-reader
paradigm, which supplements model inputs with externally retrieved knowledge.
However, prior work often evaluates RAG holistically, assessing the retriever
and reader jointly, making it difficult to isolate the true contribution of
retrieval, particularly given the prompt sensitivity of LLMs used as readers.
We introduce Spectrum Projection Score (SPS), a lightweight, supervision-free
metric that allows the reader to gauge the semantic alignment of a retrieved
summary with its hidden representation by comparing the area formed by
generated tokens from the summary, and the principal directions of subspace in
the reader and to measure the relevance. Building on SPS we present xCompress,
an inference time controller framework that dynamically samples, ranks, and
compresses retrieval summary candidates. Extensive experiments on five QA
benchmarks with four open source LLMs show that SPS not only enhances
performance across a range of tasks but also provides a principled perspective
on the interaction between retrieval and generation.