Retrieval-augmented large language models (LLMs) have demonstrated efficacy
in knowledge-intensive tasks such as open-domain QA, addressing inherent
challenges in knowledge update and factual inadequacy. However, inconsistencies
between retrieval knowledge and the necessary knowledge for LLMs, leading to a
decline in LLM's answer quality. This paper introduces BIDER, an approach that
refines retrieval documents into Key Supporting Evidence (KSE) through
knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We
train BIDER by learning from crafting KSE, while maximizing its output to align
with LLM's information acquisition preferences through reinforcement learning.
Evaluations across five datasets show BIDER boosts LLMs' answer quality by 7%
while reducing input content length in retrieval documents by 80%,
outperforming existing methods. The proposed KSE simulation effectively equips
LLMs with essential information for accurate question answering.

介绍了 BIDER，通过知识合成、监督微调和偏好对齐将检索文档转化为关键支持证据（KSE），通过强化学习从 LLM 的信息获取偏好中获得最大化输出，评估结果表明 BIDER 提高了 LLM 的答案质量 7%，同时减少了检索文档中输入内容长度 80%，优于现有方法。该方法的 KSE 模拟有效地装备了 LLM 以获得准确的问题回答。

BIDER：基于关键支持证据的高效检索增强型 LLMs 中知识不一致性的桥接

BIDER: Bridging Knowledge Inconsistency for Efficient  Retrieval-Augmented LLMs via Key Supporting Evidence

Recent advances in the healthcare industry have led to an abundance of
unstructured data, making it challenging to perform tasks such as efficient and
accurate information retrieval at scale. Our work offers an all-in-one scalable
solution for extracting and exploring complex information from large-scale
research documents, which would otherwise be tedious. First, we briefly explain
our knowledge synthesis process to extract helpful information from
unstructured text data of research documents. Then, on top of the knowledge
extracted from the documents, we perform complex information retrieval using
three major components- Paragraph Retrieval, Triplet Retrieval from Knowledge
Graphs, and Complex Question Answering (QA). These components combine lexical
and semantic-based methods to retrieve paragraphs and triplets and perform
faceted refinement for filtering these search results. The complexity of
biomedical queries and documents necessitates using a QA system capable of
handling queries more complex than factoid queries, which we evaluate
qualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate
the effectiveness and value-add.

本研究采用知识综合、段落检索、三元组检索和复杂问答等方法，构建一个可扩展的解决方案，以有效地从大规模研究文档中提取和探索复杂信息，以解决医疗保健行业面临的大量非结构化数据的问题，并在 COVID-19 数据集上进行了定性评估以证明其有效性。

从生物医学文献中进行大规模知识综合和复杂信息检索

Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents

Identifying relationships between concepts is a key aspect of scientific
knowledge synthesis. Finding these links often requires a researcher to
laboriously search through scien- tific papers and databases, as the size of
these resources grows ever larger. In this paper we describe how distributional
semantics can be used to unify structured knowledge graphs with unstructured
text to predict new relationships between medical concepts, using a
probabilistic generative model. Our approach is also designed to ameliorate
data sparsity and scarcity issues in the medical domain, which make language
modelling more challenging. Specifically, we integrate the medical relational
database (SemMedDB) with text from electronic health records (EHRs) to perform
knowledge graph completion. We further demonstrate the ability of our model to
predict relationships between tokens not appearing in the relational database.

该研究使用分布语义学将结构化知识图谱与非结构化文本相结合，利用概率生成模型预测医学概念之间的新关系，进一步证明该方法可用于医学领域的数据稀缺性问题。