Multi-Granularity Guided Fusion-in-Decoder for Robust Open-Domain Question Answering

מושגי ליבה

The core message of this paper is that incorporating multi-granularity evidence, including both passage-level and sentence-level information, can significantly improve the performance and efficiency of open-domain question answering systems.

תקציר

The paper proposes a novel model called Multi-Granularity Guided Fusion-in-Decoder (MGFiD) that addresses two key challenges in open-domain question answering (ODQA):

Effectively using evidence from multiple retrieved passages:

MGFiD employs multi-task learning to jointly optimize answer generation and passage re-ranking.
The passage re-ranking task helps the model discern relevant passages from spurious ones.

Identifying supportive sentences within passages:

In addition to passage-level evidence, MGFiD also learns to classify sentences as supportive or not, capturing fine-grained evidence.
The sentence-level predictions are used to construct an "anchor vector" that is injected into the decoder to guide the answer generation process.

Furthermore, MGFiD leverages the outcomes of the multi-task learning to improve efficiency. It uses the passage re-ranking scores to prune less supportive passages, reducing the computational cost in the decoding phase.
The experiments on the Natural Questions and TriviaQA datasets show that MGFiD outperforms existing models, highlighting the benefits of its multi-granularity approach to evidence identification and utilization.

סטטיסטיקה

The Natural Questions (NQ) dataset contains 79,168 training, 8,757 development, and 3,610 test examples.
The TriviaQA (TQA) dataset contains 78,785 training, 8,837 development, and 11,313 test examples.
The average number of passages that contain the answer span per question is 4.5 for NQ and 8.9 for TQA.
The retriever (Karpukhin et al., 2020) trained by Izacard and Grave (2021a) achieves a Recall@20 of 0.87 for NQ and 0.86 for TQA.

ציטוטים

"To address this problem, we propose the Multi-Granularity guided Fusion-in-Decoder (MGFiD), discerning evidence across multiple levels of granularity."
"Based on multi-task learning, MGFiD harmonizes passage re-ranking with sentence classification. It aggregates evident sentences into an anchor vector that instructs the decoder."

תובנות מפתח מזוקקות מ:

Multi-Granularity Guided Fusion-in-Decoder

by Eunseong Cho... ב- arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02581.pdf

שאלות מעמיקות

How can the proposed multi-granularity approach be extended to other knowledge-intensive NLP tasks beyond open-domain question answering

The multi-granularity approach proposed in the context can be extended to other knowledge-intensive NLP tasks beyond open-domain question answering by adapting the concept of discerning evidence across multiple levels of granularity. For tasks like document summarization, sentiment analysis, or information retrieval, the model can be trained to identify relevant information at both the passage and sentence levels. By incorporating multi-task learning to distinguish evidentiality and leveraging LLMs for generating pseudo-labels, the model can effectively aggregate evidence from various sources. This approach can enhance the model's ability to extract key information and generate accurate responses in a wide range of NLP tasks.

What are the potential limitations of using large language models (LLMs) for generating pseudo-labels, and how can these limitations be addressed

Using large language models (LLMs) for generating pseudo-labels may have limitations such as bias in the generated labels, reliance on the model's pre-trained knowledge, and potential errors in labeling. To address these limitations, several strategies can be implemented:

Diverse Labeling Sources: Incorporate multiple LLMs or different labeling methods to reduce bias and increase the robustness of the generated labels.
Human Validation: Introduce human validation to verify the accuracy of the pseudo-labels generated by LLMs and correct any errors or biases.
Fine-tuning LLMs: Fine-tune the LLMs on specific tasks or domains to improve the quality of the generated labels and reduce reliance on pre-trained knowledge.
Regular Evaluation: Regularly evaluate the performance of the labeling process and adjust the methodology based on feedback to enhance the accuracy of the generated labels.

How can the insights from this work on leveraging multi-level evidence be applied to improve the interpretability and explainability of question answering systems

The insights from leveraging multi-level evidence can be applied to improve the interpretability and explainability of question answering systems by:

Evidence Highlighting: Implement a system that highlights the key passages and sentences that influenced the model's answer generation, providing transparency on the reasoning process.
Explanation Generation: Develop a feature that generates explanations for the model's predictions by showcasing the evidence used and the reasoning behind selecting specific passages or sentences.
Confidence Calibration: Integrate a confidence calibration mechanism that indicates the model's certainty in the selected evidence, helping users understand the reliability of the generated answers.
Interactive Interfaces: Create interactive interfaces that allow users to explore the evidence hierarchy and understand how different levels of evidence contribute to the final answer, enhancing the interpretability of the system.