Open-domain question answering (QA) systems are often built with retrieval
modules. However, retrieving passages from a given source is known to suffer
from insufficient knowledge coverage. Alternatively, prompting large language
models (LLMs) to generate contextual passages based on their parametric
knowledge has been shown to improve QA performance. Yet, LLMs tend to
"hallucinate" content that conflicts with the retrieved knowledge. Based on the
intuition that answers supported by both sources are more likely to be correct,
we propose COMBO, a Compatibility-Oriented knowledge Merging for Better
Open-domain QA framework, to effectively leverage the two sources of
information. Concretely, we match LLM-generated passages with retrieved
counterparts into compatible pairs, based on discriminators trained with silver
compatibility labels. Then a Fusion-in-Decoder-based reader model handles
passage pairs to arrive at the final answer. Experiments show that COMBO
outperforms competitive baselines on three out of four tested open-domain QA
benchmarks. Further analysis reveals that our proposed framework demonstrates
greater efficacy in scenarios with a higher degree of knowledge conflicts.

基于知识兼容性匹配的 COMBO 框架能够有效地利用两种信息源，改善开放领域的问答性能，并在四项测试问答基准中有三个的竞争对手表现更好，特别在存在较高程度知识冲突的场景中表现出更大的功效。

合并生成和检索知识的开放领域问答

Merging Generated and Retrieved Knowledge for Open-Domain QA

The open-ended Visual Question Answering (VQA) task requires AI models to
jointly reason over visual and natural language inputs using world knowledge.
Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to
the task and shown to be powerful world knowledge sources. However, these
methods suffer from low knowledge coverage caused by PLM bias -- the tendency
to generate certain tokens over other tokens regardless of prompt changes, and
high dependency on the PLM quality -- only models using GPT-3 can achieve the
best result.
To address the aforementioned challenges, we propose RASO: a new VQA pipeline
that deploys a generate-then-select strategy guided by world knowledge for the
first time. Rather than following the de facto standard to train a multi-modal
model that directly generates the VQA answer, RASO first adopts PLM to generate
all the possible answers, and then trains a lightweight answer selection model
for the correct answer. As proved in our analysis, RASO expands the knowledge
coverage from in-domain training data by a large margin. We provide extensive
experimentation and show the effectiveness of our pipeline by advancing the
state-of-the-art by 4.1% on OK-VQA, without additional computation cost. Code
and models are released at this http URL

本研究提出了一种新的 VQA 模型管道 RASO，利用预训练语言模型生成所有可能的答案，然后训练轻量级的答案选择模型，扩大了领域内数据的知识覆盖范围，并在不额外增加计算成本的基础上将 OK-VQA 的最新成果提高了 4.1%。