Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering (2023)

Abstract: Retrieval-augmented generation models augment knowledge encoded in a language model by providing additional relevant external knowledge (context) during generation. Although it has been shown that the quantity and quality of context impact the performance of retrieval-augmented generation models during inference, limited research explores how these characteristics affect model training. This paper explores how context quantity and quality during model training affect the performance of Fusion-in-Decoder (FiD), the state-of-the-art retrieval-augmented generation model, in extractive open-domain question answering tasks. Experimental results suggest that FiD models overfit to context quality during training and show suboptimal performance when evaluated on different context quality. Through the experimental results, we also reveal FiD models trained with different context quality have different cross-attention distribution patterns. Specifically, as context quality during training increases, FiD models tend to attend more uniformly to each passage in context. Finally, based on these observations, we propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution, which we demonstrate to be effective in improving the performance of FiD models on different context quality.

## introduction recently, large-scale pre-trained language models have achieved impressive performance in the field of natural language generation, which includes tasks that require real-world knowledge, e.g., closed-book question answering and common sense reasoning @xcite . however, these models are still prone to generate factually incorrect outputs known as hallucinations @xcite , particularly when dealing with rare entities @xcite . also, they cannot handle new information that arises after their training phase @xcite . in order to address these challenges, retrievalaugmented generation models have been recently proposed @xcite . these models draw inspiration from retrieval-based extractive open-domain question answering methods @xcite and utilize additional relevant external knowledge (e.g., a wikipedia article about an entity in a given question) during generation to augment knowledge encoded in a language model. retrieval-augmented generation models have demonstrated effectiveness in knowledge-intensive tasks @xcite such as question answering and fact checking @xcite , and have been reported to reduce hallucinations in dialogue tasks @xcite . the external knowledge given to the models is called a context, and it is usually obtained through information retrieval systems @xcite . multiple passages, typically up to 100, are often used collectively as a single context to ensure the high recall of relevant information. this strategy addresses the limitations of retrieval systems, which may return irrelevant passages and fail to capture relevant information in the top results. when dealing with contexts composed of multiple passages, we can define their quantity (the number of passages in the context) and quality (the proportion of relevant passages in the context). since the context quantity and quality vary depending on model configuration or application, e.g., the performance of the retrieval system and the computational resources available, understanding how these characteristics impact the model performance becomes an important research question. indeed, during the inference phase, it has been shown that the quantity and quality of contexts impact the performance of retrieval-augmented generation models. for example, @xcite showed that increasing the number of topranked retrieved passages used as a context during inference improves the performance of their model in the question answering task, and @xcite found that the model prediction is distracted more strongly as the proportion of conflicting misinformation in the context increases. however, regarding the training phase, it is not yet fully understood how these context characteristics impact the performance of the trained models. limited research suggests that increasing the number of retrieved passages used as a context during training improves question answering performance @xcite and reduces memorization @xcite . still, the impact of quantity and quality of contexts is mixed in these studies, as relevant passages are typically biased towards higher rank in the retrieval result, and simply increasing the number of top-ranked passages changes both the quantity and quality of the context. in this paper, we focused on extractive opendomain question answering tasks and investigated the impact of context quantity and quality on the training of fusion-in-decoder (fid) (izacard and grave, 2021b), a state-of-the-art retrievalaugmented generation model. we demonstrate that context quality during training affects the performance of the trained model. as far as our knowledge, this work is the first attempt to explicitly control context quality and investigate its effect on training of retrieval-augmented generation models. key insights obtained through our experiments are as follows: based on these observations, we propose a method to mitigate the overfitting of a trained fid model to specific context quality without additional training by controlling the selectivity of its crossattention distribution. we present an empirical analysis demonstrating the proposed method's effectiveness in improving the performance of a trained fid model when deployed in environments with different context quality than those used during training. ## experimental setup in this section, we describe the task ( §2.1) and model architecture ( §2.2) used in our experiments, and we define quality and quantity that we used in this paper ( §2.3, 2.4). ## case studies in this section, we describe our experiments to investigate how context quantity and quality during training affect the performance of fid models. throughout the experiments in this section, we created various training and evaluation environments with controlled context quantity or quality by sampling n + relevant passages from r(q) and n -irrelevant passages from r(q) for each question q, without replacement foot_5 . in the rest of this paper, we define @xmath0. we will use subscripts • train and • eval respectively to denote the values of training and evaluation environments if required. ## adapting models to different context quality while fid models overfit to context quality during training as shown in §3, it is not desirable to train a dedicated model for each target environment that has potentially different context qualities from each other. thus, in this section, we propose a method to mitigate the effect of overfitting and adapt an already trained fid model to an environment with different context quality. ## conclusion in this paper, we investigate how context quality and quantity affect the training of fid models in extractive open-domain question answering tasks. we show that fid models tend to overfit to the context quality during training, resulting in degraded performance when evaluated in environments with different context qualities. additionally, our research reveals that the overfitting to context quality is partially explained by different patterns in the model's cross-attention probability. based on these observations, we propose changing the selectivity of the cross-attention probability to mitigate the effect of overfitting to context quality. the results of this paper suggest a broad spectrum of future work, including more sophisticated adaptation methods and investigations of the effect of other context characteristics on the training of retrieval-augmented generation models.