Achieving State-of-the-Art Open-Domain QA Performance through Fusion-in-Decoder Method

Abstract

Open-domain question answering (QA) has recently made significant progress, with generative models like Transformers demonstrating impressive performance. However, these models are computationally expensive to train and query, limiting their practical application. In this whitepaper, we introduce a novel approach to open-domain QA that combines the strengths of retrieval and generative models, aiming to achieve more efficient and accurate question answering. Our approach, termed Fusion-in-Decoder, retrieves informative passages and leverages them with a sequence-to-sequence model to generate answers. This method demonstrates state-of-the-art results on benchmarks like Natural Questions and TriviaQA, and offers a highly scalable framework for aggregating and combining information from multiple passages.

Full text

Achieving State-of-the-Art Open-Domain QA
Performance through Fusion-in-Decoder Method
Waseem AlShikh Melisa Russak Kiran Kamble Christopher Bryant
Manhal Daaboul Kirk Goddard Brock Imel Parikshith Kulkarni
Writer, Inc.
{waseem,melisa,kiran,...,firstname}@writer.com
Abstract
Open-domain question answering (QA) has recently made significant progress,
with generative models like Transformers demonstrating impressive performance.
However, these models are computationally expensive to train and query, limiting
their practical application. In this whitepaper, we introduce a novel approach to
open-domain QA that combines the strengths of retrieval and generative models,
aiming to achieve more efficient and accurate question answering. Our approach,
termed Fusion-in-Decoder, retrieves informative passages and leverages them with
a sequence-to-sequence model to generate answers. This method demonstrates
state-of-the-art results on benchmarks like Natural Questions and TriviaQA, and
offers a highly scalable framework for aggregating and combining information
from multiple passages.
1 Introduction
Generative models have emerged as a powerful tool for open-domain question answering, proving
their effectiveness in various benchmarks. However, these models come with the cost of high resource
consumption during training and querying. A viable solution to such practical concerns involves
leveraging external knowledge sources such as Wikipedia, which can alleviate the need for massive
computing resources.
Our research centers on harnessing the strengths of both generative models and retrieval systems for
more efficient open-domain QA. We propose the Fusion-in-Decoder approach, a two-step method that
first retrieves support passages and then processes them with a sequence-to-sequence model to gener-
ate responses. We demonstrate the efficacy and scalability of our approach through comprehensive
experiments and comparisons with existing methods.
2 Background and Related Work
Open-domain QA involves extracting factual information from large-scale language models trained
on vast amounts of data. Previously, retrieval-based approaches using extractive models, sparse
representations, and dense embeddings have been employed for this task.
Generative models have been utilized for several datasets requiring abstractive answers, such as
NarrativeQA, CoQA, and ELI5. The combination of retrieval and generative models has also been
explored, but with limited scalability when processing large numbers of support passages.
Preprint. Under review.

Our research extends these prior works by investigating Fusion-in-Decoder, a method that can
efficiently and effectively integrate passage retrieval and generative models.
3 Methodology
The Fusion-in-Decoder (FiD) approach focuses on effectively combining the advantages of retrieval
and generative models for efficient and accurate open-domain QA. The methodology consists of the
following steps:
1. Support Passage Retrieval
•
Data Preprocessing: Perform tokenization, stemming, and stop-word removal on the
text corpus to generate a properly formatted dataset suitable for the retrieval models.
•
Indexing: Index the preprocessed dataset to create an efficient, searchable structure for
the chosen retrieval model.
• Passage Retrieval Algorithms:
–
Sparse Retrieval: Implement the BM25 algorithm, an information retrieval function
that ranks matching passages from the text corpus based on their relevance to the
question.
–
Dense Retrieval: Use the Dense Retriever (DPR), an embedding-based approach
that ranks passages based on their similarity to the input question using vector
representations for both questions and passages.
•
Retrieval Evaluation: Set a predefined number, *k*, of top-ranked passages to be
passed on to the generative model.
2. Generative Encoder-Decoder Model
•
Preprocessing: Concatenate the input question and the retrieved support passages into
a single, formatted input string.
•
Encoder-Decoder Model Selection: Choose an appropriate generative model such as T5
or BART, which are pretrained sequence-to-sequence models based on the Transformer
architecture.
•
Model Fine-tuning: Fine-tune the selected encoder-decoder model on the QA dataset
by minimizing the generation loss while conditioned on the retrieved support passages.
• Generation Settings:
–
Temperature: Control the randomness of generated outputs by adjusting the tem-
perature parameter.
–
Beam Search: Explore a predefined number of alternative answers during sequence
generation to improve the generated response’s quality.
3. Evaluation Metrics
•
Exact Match (EM): Measure the percentage of generated answers that exactly match
the reference ground-truth answers.
•
F1 Score: Compute the F1 score to assess generated answers based on the occurrence
of tokens, considering both recall and precision without requiring an exact match.
4. Ablation Study
•
Impact of Sparse and Dense Retrieval: Investigate the effect of using sparse (BM25)
versus dense (DPR) retrieval methods on the final QA performance.
•
Influence of Generative Models: Analyze the impact of using different generative
models (e.g., T5 or BART) on the overall QA performance.
•
Role of Retrieved Passages: Study the effects of varying the number of retrieved
support passages on the FiD method’s accuracy.
Through this extensive methodology, the Fusion-in-Decoder approach can effectively integrate
passage retrieval and generative models while efficiently improving the performance of open-domain
question answering tasks.
2

4 Experiments and Results
We perform extensive experiments to assess the Fusion-in-Decoder (FiD) method on various datasets,
comparing its performance against other commonly used approaches. Our experiments focus on
several aspects, including the choice of different retrieval models, generative models, and varying the
number of retrieved passages.
Datasets: We evaluate our method on three widely used QA benchmarks - Natural Questions (NQ),
TriviaQA, and SQuAD. Each dataset has distinct content and structure, posing diverse challenges for
the FiD method.
4.1 Comparison with Existing Methods
In this experiment, we compare the FiD method’s performance against other popular approaches,
including extractive models (EM), sparse retrieval (BM25), dense retrieval (DPR), and standalone
generative models such as T5 and BART.
A. Exact Match (EM) We use the EM score to evaluate the percentage of generated answers that
exactly match the reference answers. Table 1 presents the EM scores for each method on the three
datasets.
Table 1: Add caption
Method Natural Questions TriviaQA SQuAD
Extractive Model (EM) 37.5 59.5 52.8
BM25-based Retrieval 39.8 61.2 54.2
DPR-based Retrieval 42.4 63.1 55.4
T5 Generative Model 46.3 64.9 57.3
BART Generative Model 45.8 65.3 56.7
Fusion-in-Decoder (FiD) 50.7 66.1 60.2
The results show that the FiD method surpasses all other methods in terms of EM scores across all
three datasets.
B. F1 Score We also use F1 scores to assess the generated responses’ quality based on token
occurrence, considering both precision and recall without requiring a precise match. Table 2 presents
the F1 scores for each method on the three datasets.
Table 2: Add caption
Method Natural Questions TriviaQA SQuAD
Extractive Model (EM) 43.9 64.8 56.7
BM25-based Retrieval 46.3 66.4 58.2
DPR-based Retrieval 48.7 68.4 59.9
T5 Generative Model 52.6 70.2 62.1
BART Generative Model 52.1 70.6 61.5
Fusion-in-Decoder (FiD) 57.0 71.4 65.0
As observed, the FiD method consistently performs better across all datasets, achieving higher F1
scores than other methods.
4.2 Impact of Different Retrieval Models
In this experiment, we analyze the FiD method’s performance when utilizing different retrieval
methods, namely sparse (BM25) and dense (DPR) retrieval, for retrieving support passages. Our
results demonstrate that the FiD method achieves improved EM scores when employing dense
retrieval techniques.
3

Table 3: Add caption
Retrieval Model Natural Questions TriviaQA SQuAD
FiD with BM25 49.2 65 59.1
FiD with DPR 50.7 66.1 60.2
4.3 Influence of Generative Models
We investigate the FiD method’s performance when employing different generative models, specifi-
cally T5 and BART. The results show that while both models produce competitive EM scores, T5
slightly surpasses the performance of BART in our experiments.
Table 4: Add caption
Generative Model Natural Questions TriviaQA SQuAD
FiD with T5 50.7 66.1 60.2
FiD with BART 49.3 65.6 59.7
4.4 Role of Retrieved Passages
To study the effect of the number of retrieved passages on the FiD method’s performance, we conduct
experiments with varying numbers of support passages ranging from 1 to 100. Table 3 presents the
EM scores for different numbers of retrieved passages.
Table 5: Add caption
Number of Passages Natural Questions TriviaQA SQuAD
1 38.3 56.8 53.6
5 47.2 62.6 58.8
10 48.6 64.1 59.5
25 49.9 65.2 60
50 50.5 65.8 60.4
100 50.7 66.1 60.2
The results demonstrate that increasing the number of retrieved passages typically leads to improved
QA performance. However, the improvements begin to plateau after a certain number of passages. In
our experiments, retrieving 50-100 passages provided optimal results.
4.5 Analysis of Response Length
As generative models can create answers of varying lengths, we analyze the performance of FiD
based on the length of generated answers. Our findings indicate that the FiD method performs equally
well on both short and long answers, maintaining a consistently high EM and F1 score throughout
the varying response lengths.
These comprehensive experiments, spanning multiple dimensions of analysis, reveal that the Fusion-
in-Decoder method excels in open-domain question answering tasks. The method effectively leverages
the strengths of retrieval and generative models, achieving state-of-the-art results and significantly
outperforming existing methods. By doing so, it demonstrates its potential for practical applications
in large-scale question-answering systems.
5 Future Work
We plan to optimize the Fusion-in-Decoder method to handle even larger numbers of support passages,
as well as explore more efficient methods for scaling. Furthermore, we are interested in incorporating
retrieval directly into the model and learning the entire system end-to-end.
4

Table 6: Add caption
Length Natural Questions TriviaQA SQuAD
Short 51.6 66.7 60.6
Medium 50.2 65.9 60.1
Long 50.4 65.8 60.0
6 Conclusion
In this whitepaper, we introduced the Fusion-in-Decoder approach to open-domain question answer-
ing, showcasing its effectiveness and scalability compared to existing methods. By combining the
power of generative models with passage retrieval, this method achieves state-of-the-art results in
benchmarks while demonstrating its potential for practical applications.
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