# Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task
## 3 Results
### Experimental Results
#### 3.2.2 Retriever Performance  
After demonstrating the effectiveness of our proposed pseudo-labeled data in improving OpenQA retrieval performance through simple linear weighting, we applied this data to our text representation optimization model AOpt. The AOpt-query model is used to optimize the dense representation of queries, while the AOpt-passage model is used to optimize the dense representation of passages. We conducted retrieval experiments on the NQ and TriviaQA datasets for three cases: \"optimizing query representations only\"; \"optimizing passage representations only\"; and \"optimizing query and passage representations sequentially\". As shown in Table 3, we compared our proposed AOpt model with baseline models and representative models in recent years for OpenQA tasks and found that the AOpt model improved the retrieval performance on the NQ dataset, especially on the Top-1 accuracy, achieving a significant improvement of 11.4% compared to the baseline model DPR. In addition, the AOpt model achieved the best retrieval performance on the TriviaQA dataset, with a 12.1% improvement in the Top-1 accuracy compared to the DPR baseline model. Specifically, we found that the retrieval performance of AOpt-query was slightly higher than AOpt-passage, with a maximum difference of 3.0% (Top-5 accuracy on the NQ dataset). We believe this indicates that optimizing query representations is more effective than optimizing passage representations under the same scale. Figure 6: These are weight combinations experimental results (on NQ dataset) heat map, whose x-axis denotes relevant query representations’ weight \(\beta\) and y-axis denotes irrelevant query representations’ weight \(\gamma\). The retrieval performance (EM) results are as follows: (**a**) Top-1; (**b**) Top-5; (**c**) Top-20; (**d**) Top-100. Although optimizing queries can achieve relatively higher significant results, the data in Table 3 for \"DPR + AOpt(hybrid)\" shows that optimizing both query and passage representations can achieve even bigger improvement. On the NQ dataset, it improved by 4.2% compared to only optimizing query representations and 5.8% compared to only optimizing passage representations. On the TriviaQA dataset, it improved by 3.9% compared to only optimizing query representations and 6.3% compared to only optimizing passage representations. Furthermore, according to Table 3, we found that DPR with AOpt led to a significant improvement in retrieval performance, especially in Top-1 accuracy. As the k value increases, the retrieval improvement starts to decrease. We believe that this phenomenon is caused by our pseudo-labeled data construction strategy, which strengthens the mathematical correlation between query representation and passage representation through several iterations. When retrieving from a large-scale set of passages, our dense representation optimization strategy makes the retriever focus more on the most recent relevant passages that contain the current query answer. Therefore, in the iterative retrieval process, relevant passages will obtain higher-ranking results. In other words, this method can achieve results comparable to adding an additional re-ranker module. During the iterative epochs of the AOpt model, we found that AOpt-passage and AOpt-query resulted in different trends in retrieval performance, as shown in Figure 7.  
Based on the two trends shown in Figure 7, we conduct the following observations: (1) the retrieval performance change curve using the AOpt-passage model is stable and gradually tends to flatten; (2) the retrieval performance change curve using the AOpt-query model quickly reaches its peak and then rapidly declines. We believe that this is due to the significant difference in sequence length between queries and passages. In the QA dataset, the query sequence length is generally less than 15 tokens, while the passage sequence length is around 100 tokens.