Fusion-in-Decoder: A New Era in Comprehensive Question Answering and Beyond

7 min readFeb 11, 2025

Introduction to Fusion-in-Decoder (FiD)

Background

In open-domain question answering (QA), the goal is to provide accurate answers to questions by leveraging a vast corpus of unstructured text, such as Wikipedia or other large datasets. Traditional QA systems often follow a two-step process: retrieving relevant documents and then extracting or generating answers from these documents. However, this approach can be limited by the quality of the retrieval step and the ability of the model to integrate information from multiple sources.

Motivation

The primary motivation behind FiD is to improve the integration of information from multiple documents. In many real-world scenarios, the answer to a question may not be contained within a single document. Instead, it may require synthesizing information from several sources. Traditional models that process each document independently may struggle to combine this information effectively, leading to incomplete or inaccurate answers.

Core Concept

FiD addresses this challenge by introducing a novel way to handle multiple documents in the context of sequence-to-sequence models. The key idea is to fuse information from all retrieved documents directly within the decoder, rather than processing each document separately and aggregating results afterward. This allows the model to consider all available evidence simultaneously when generating an answer.

How FiD Works

Let’s delve deeper into each component of the Fusion-in-Decoder (FiD) approach for open-domain question answering.

1. Retrieval of Documents

Objective: The goal is to identify and retrieve documents or passages from a large corpus that are relevant to the given question.

Information Retrieval Techniques: This step often employs traditional information retrieval methods such as TF-IDF, BM25, or more advanced neural retrieval models like Dense Passage Retrieval (DPR). These methods rank documents based on their relevance to the query.
Challenges: The retrieval process must balance precision and recall. It should retrieve enough relevant documents to cover the necessary information while avoiding too many irrelevant ones that could confuse the model.

2. Encoding

Objective: Convert the retrieved documents into a format that the model can process effectively.

Shared Encoder: A transformer-based model, such as BERT, is typically used to encode each document. The encoder transforms the text into a sequence of hidden states or embeddings, capturing semantic information.
Uniform Representation: By using a shared encoder, all documents are transformed into a uniform representation, making it easier for the decoder to process them collectively.

3. Fusion in the Decoder

Objective: Integrate information from multiple documents to generate a coherent and informed answer.

Concatenation of Representations: The encoded representations of all retrieved documents are concatenated into a single input sequence for the decoder. This allows the decoder to access all the information simultaneously.
Handling Long Sequences: Since the concatenated input can be quite long, the decoder must be capable of handling long sequences. This is where transformer-based decoders excel due to their self-attention mechanism.

4. Attention Mechanism

Objective: Allow the model to focus on the most relevant parts of the input when generating each part of the output.

Self-Attention: The decoder uses self-attention to dynamically focus on different parts of the concatenated input. This means that for each token it generates, it can weigh the importance of different pieces of evidence from the input.
Cross-Attention: In some architectures, cross-attention layers are used to specifically attend to the encoder outputs, allowing the decoder to integrate information from the entire input sequence effectively.

5. Answer Generation

Objective: Produce a coherent and accurate answer to the question by synthesizing information from multiple sources.

Sequential Generation: The decoder generates the answer token by token, using the attention mechanism to incorporate information from the entire input sequence.
Contextual Synthesis: By considering all documents simultaneously, the model can synthesize information, resolve contradictions, and fill in gaps that might exist if only a single document were considered.
Output: The final output is a sequence of tokens that form the answer to the question, ideally capturing the most relevant and accurate information from the input documents.

Advantages of FiD

Comprehensive Understanding: By processing multiple documents together, FiD can provide more comprehensive answers that consider various perspectives and pieces of evidence.
Improved Accuracy: The ability to synthesize information from multiple sources can lead to more accurate answers, especially in cases where no single document contains all the necessary information.
Scalability: FiD can be scaled to handle large numbers of documents, making it suitable for open-domain tasks where the relevant information might be spread across many sources.

Applications of FiD

Fusion-in-Decoder (FiD) is a versatile approach that can be applied to various tasks beyond open-domain question answering. Its ability to synthesize information from multiple sources makes it particularly valuable in several areas of natural language processing (NLP). Let’s explore some of these applications in more detail:

1. Open-Domain Question Answering

Objective: Provide accurate answers to questions by leveraging a large corpus of text.
FiD’s Role: In open-domain QA, FiD excels by integrating information from multiple documents to generate comprehensive answers. This is crucial when the answer is not contained within a single document but requires synthesizing data from various sources.

2. Summarization

Objective: Generate concise summaries of large volumes of text.
FiD’s Role: For tasks like multi-document summarization, FiD can effectively combine information from different documents to produce a coherent and comprehensive summary. This is particularly useful for summarizing news articles, research papers, or any content where the key points are spread across multiple documents.

3. Information Retrieval

Objective: Retrieve and rank documents based on their relevance to a query.
FiD’s Role: While FiD is primarily used for generating answers, its principles can enhance information retrieval systems by providing a mechanism to better understand and integrate the content of multiple documents, potentially improving the ranking and relevance of retrieved documents.

4. Knowledge Synthesis

Objective: Integrate and synthesize information from diverse sources to form a unified understanding.
FiD’s Role: In domains like scientific research or business intelligence, FiD can be used to synthesize information from various reports, studies, or data sources, providing a comprehensive view that supports decision-making or further analysis.

5. Dialogue Systems

Objective: Engage in natural and informative conversations with users.
FiD’s Role: In dialogue systems, especially those requiring access to external knowledge bases, FiD can help generate responses that are informed by multiple sources, leading to more accurate and contextually relevant interactions.

6. Fact-Checking and Verification

Objective: Verify the accuracy of claims by cross-referencing multiple sources.
FiD’s Role: FiD can assist in fact-checking by aggregating and analyzing information from various documents to determine the veracity of a claim, providing a more robust basis for verification.

7. Cross-Document Coreference Resolution

Objective: Identify and resolve references to the same entities across multiple documents.
FiD’s Role: By processing multiple documents simultaneously, FiD can help identify and link references to the same entities, improving the coherence and accuracy of information extraction tasks.

8. Complex Query Answering

Objective: Answer complex queries that require reasoning over multiple pieces of information.
FiD’s Role: For complex queries that involve multiple steps or require understanding relationships between different pieces of information, FiD can integrate and reason over the necessary data to provide accurate answers.

Fusion-in-Decoder (FiD) represents a significant advancement in the field of natural language processing, particularly in tasks that require the integration and synthesis of information from multiple sources. Its innovative approach to handling multi-document inputs within a sequence-to-sequence framework offers several key advantages and opens up new possibilities across various applications.

Key Advantages

Comprehensive Information Synthesis: FiD’s ability to process multiple documents simultaneously allows it to synthesize information in a way that single-document models cannot. This leads to more complete and nuanced outputs, as the model can draw on a broader context and integrate diverse pieces of evidence.
Improved Accuracy and Relevance: By considering all available evidence at once, FiD can produce more accurate and contextually relevant answers or summaries. This is particularly beneficial in scenarios where information is fragmented across different sources, as FiD can resolve contradictions and fill in informational gaps.
Scalability and Flexibility: FiD is well-suited for open-domain tasks, where the relevant information may be distributed across a vast corpus. Its architecture can be scaled to handle large numbers of documents, making it adaptable to various domains and datasets.
Enhanced Contextual Understanding: The use of attention mechanisms in the decoder allows FiD to dynamically focus on the most relevant parts of the input, enhancing its ability to understand and generate contextually appropriate responses.

Conclusion

Applications and Impact

FiD’s versatility extends its applicability beyond open-domain question answering to include tasks such as summarization, information retrieval, knowledge synthesis, dialogue systems, fact-checking, and more. In each of these areas, FiD’s ability to integrate information from multiple sources enhances the quality and depth of the outputs, providing more comprehensive insights and solutions.

In Summarization, FiD can generate coherent summaries that capture the essence of multiple documents, making it valuable for news aggregation, research synthesis, and content curation.
In Dialogue Systems, FiD can improve the informativeness and relevance of responses by drawing on a wide range of knowledge sources.
In Fact-Checking, FiD can assist in verifying claims by aggregating evidence from various documents, supporting more robust and reliable verification processes.

Future Directions

The success of FiD highlights the potential for further innovations in multi-document processing and information synthesis. Future research may explore:

Enhanced Retrieval Techniques: Improving the initial document retrieval step to ensure that the most relevant and diverse documents are considered.
Efficient Scaling: Developing more efficient architectures and training methods to handle even larger datasets and more complex queries.
Domain-Specific Adaptations: Tailoring FiD to specific domains, such as legal, medical, or scientific fields, where specialized knowledge and terminology are crucial.