Enhancing Open-Domain Question Answering with Vectorized Context Encoding

Core Concepts

Incorporating a small encoder model to effectively encode longer contexts and leverage cross-attention mechanism to improve the performance of open-domain question answering.

Abstract

The paper proposes a method to enhance open-domain question answering (ODQA) by leveraging a small encoder model to effectively encode longer contexts. The key highlights are:

The method utilizes a small encoder model to encode additional contexts beyond the maximum length that the original task model can handle. This is achieved by applying a cross-attention mechanism between the encoded contexts and the original task model's inputs.

Experiments are conducted on two ODQA datasets (TriviaQA and Natural Questions) and evaluated on held-in, held-out, and in-context learning (ICL) settings. The results show that the proposed method consistently outperforms the baseline, which is fine-tuned on data with limited context length.

The computing resource requirements of the proposed method are close to the baseline, and the runtime remains competitive. This is achieved by using a 10x smaller encoder model compared to the original task model, avoiding the need for complex techniques to reduce the computation graph during backpropagation.

The paper also analyzes the impact of training strategies for the encoder model, demonstrating the importance of carefully optimizing the encoder parameters to maintain its encoding capability.

Further experiments show the effectiveness of the proposed method in a more challenging setting where the original task model is provided with only the question-answer pairs without any context information.

Overall, the paper presents a simple yet effective method to enhance ODQA by leveraging a small encoder model to encode longer contexts, which leads to improved performance across various evaluation settings.

Stats

The length of context that the model can cover increases from 2k (in text form) to a maximum of 10k (in dense form, which is condensed by the encoder).
The run time of the proposed method remains competitive compared to the baseline, as shown in Figure 2.

Quotes

None

Key Insights Distilled From

Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts

by Zhuo Chen,Xi... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02022.pdf

Deeper Inquiries

How can the proposed method be extended to handle even longer contexts beyond 10k tokens?

To handle contexts longer than 10k tokens, the proposed method can be extended by implementing a hierarchical approach. This approach involves dividing the lengthy context into smaller segments and processing them sequentially. Each segment can be encoded separately by the encoder, and the cross-attention mechanism can be utilized to maintain coherence and relevance across the segments. By aggregating the information from each segment, the model can effectively cover longer contexts while maintaining performance and efficiency.

What are the potential limitations of the cross-attention mechanism in modeling the relationship between context and in-context learning samples without contexts?

One potential limitation of the cross-attention mechanism in modeling the relationship between context and in-context learning samples without contexts is the lack of direct interaction between the context and the in-context learning samples. Since the cross-attention mechanism primarily focuses on aligning the input embeddings with the encoded context information, it may not effectively capture the nuanced relationships between the context and the in-context learning samples. This limitation could lead to suboptimal performance in scenarios where the context plays a crucial role in understanding the learning samples.

How can the proposed method be adapted to other language understanding tasks beyond open-domain question answering?

The proposed method can be adapted to other language understanding tasks by customizing the input format and prompts based on the specific requirements of the task. For tasks like sentiment analysis, text classification, or machine translation, the input format can be modified to include relevant context information and prompts tailored to the task. Additionally, the encoder and cross-attention mechanism can be fine-tuned on task-specific data to optimize performance. By adjusting the architecture and training process to suit the characteristics of different tasks, the proposed method can be effectively applied to a wide range of language understanding tasks.