Query augmentation is a crucial technique for refining semantically imprecise
queries. Traditionally, query augmentation relies on extracting information
from initially retrieved, potentially relevant documents. If the quality of the
initially retrieved documents is low, then the effectiveness of query
augmentation would be limited as well. We propose Brain-Aug, which enhances a
query by incorporating semantic information decoded from brain signals.
BrainAug generates the continuation of the original query with a prompt
constructed with brain signal information and a ranking-oriented inference
approach. Experimental results on fMRI (functional magnetic resonance imaging)
datasets show that Brain-Aug produces semantically more accurate queries,
leading to improved document ranking performance. Such improvement brought by
brain signals is particularly notable for ambiguous queries.

利用从脑信号解码出的语义信息增强查询，Brain-Aug 生成原始查询的继续部分，通过排序导向的推理方法提高查询准确性，实验证明 Brain-Aug 使查询更准确，进而提高文档排序性能，对于歧义查询尤为显著。

通过解读脑信号进行查询扩充

Query Augmentation by Decoding Semantics from Brain Signals

In this work, we propose a simple method that applies a large language model
(LLM) to large-scale retrieval in zero-shot scenarios. Our method, Language
language model as Retriever (LameR) is built upon no other neural models but an
LLM, while breaking up brute-force combinations of retrievers with LLMs and
lifting the performance of zero-shot retrieval to be very competitive on
benchmark datasets. Essentially, we propose to augment a query with its
potential answers by prompting LLMs with a composition of the query and the
query's in-domain candidates. The candidates, regardless of correct or wrong,
are obtained by a vanilla retrieval procedure on the target collection. Such
candidates, as a part of prompts, are likely to help LLM generate more precise
answers by pattern imitation or candidate summarization. Even if all the
candidates are wrong, the prompts at least make LLM aware of in-collection
patterns and genres. Moreover, due to the low performance of a self-supervised
retriever, the LLM-based query augmentation becomes less effective as the
retriever bottlenecks the whole pipeline. So, we propose to leverage a
non-parametric lexicon-based method (e.g., BM25) as the retrieval module to
capture query-document overlap in a literal fashion. As such, LameR makes the
retrieval procedure transparent to the LLM, so it circumvents the performance
bottleneck.

该论文提出了一种简单的方法，使用大规模语言模型作为信息检索的检索器，从而在零 - shot 场景下提高检索效率。该方法通过在检索词和其他相关域内候选之间进行组合，来辅助语言模型生成更精确的答案，使得检索更加透明，从而实现竞争力更强的检索结果。

大型语言模型是强零 - shot 召回器

Large Language Models are Strong Zero-Shot Retriever

Recent work in open-domain question answering (ODQA) has shown that
adversarial poisoning of the input contexts can cause large drops in accuracy
for production systems. However, little to no work has proposed methods to
defend against these attacks. To do so, we introduce a new method that uses
query augmentation to search for a diverse set of retrieved passages that could
answer the original question. We integrate these new passages into the model
through the design of a novel confidence method, comparing the predicted answer
to its appearance in the retrieved contexts (what we call Confidence from
Answer Redundancy, e.g. CAR). Together these methods allow for a simple but
effective way to defend against poisoning attacks and provide gains of 5-20%
exact match across varying levels of data poisoning.

本研究提出了一种新的方法 —— 查询增强方法，结合一种新的置信度方法 CAR 来寻找多个可以回答原始问题的检索段落，并将它们整合到模型中，以对抗数据污染并提高 5%-20% 精确匹配度。