Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering
Authors: Xinran Chen, Ben He, Xuanang Chen, Le Sun
Accepted at: acl25

Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering

Xinran Chen1,2, Ben He1,2, Xuanang Chen2,*, Le Sun2
1School of Computer Science and Technology, University of Chinese Academy of Sciences
2Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences
chenxinran22@mails.ucas.ac.cn, benhe@ucas.ac.cn
chenxuanang@iscas.ac.cn, sunle@iscas.ac.cn

Abstract

The effectiveness of open-domain question answering (ODQA), particularly those employing a retriever-reader architecture, depends on the ability to recall relevant documents - a critical step that enables the reader to accurately extract answers. To enhance this retrieval phase, current query expansion (QE) techniques leverage pre-trained language models (PLM) to mitigate word mismatches and improve the recall of relevant documents. Despite their advancements, these techniques often treat all expanded terms uniformly, which can lead to less-than-optimal retrieval outcomes. In response, we propose a novel Recall-oriented Adaptive Learning (ReAL) method, which iteratively adjusts the importance weights of QE terms based on their relevance, thereby refining term distinction and enhancing the separation of relevant terms. Specifically, ReAL employs a similarity-based model to classify documents into pseudo-relevant and pseudo-irrelevant sets, and then optimizes term weights via two tailored loss functions to maximize the scoring gap between them. Experiments on four ODQA datasets and five QE methods show that ReAL consistently enhances retrieval accuracy and overall end-to-end QA performance, providing a robust and efficient solution for improving QE strategies in ODQA scenarios.

1 Introduction

Open-Domain Question Answering (ODQA) is a pivotal task in Natural Language Processing (NLP) that focuses on producing accurate answers to a broad range of factual questions across diverse domains (Kwiatkowski et al., 2019). ODQA systems typically adopt a retriever-reader architecture, where the retriever finds relevant documents from the corpus, and the reader extracts or synthesizes answers (Chen et al., 2017). Although more advanced retrieval and re-ranking models, such as dual-encoders (Karpukhin et al., 2020; Chen et al., 2022; Wen et al., 2023), cross-encoders (Chen et al., 2023a,b) and pairwise ranking prompting (Luo et al., 2024; Zhuang et al., 2024) are effective, sparse retrieval models (Salton et al., 1975; Robertson and Zaragoza, 2009) are still widely used for their speed and lack of training requirements, making them well-suited for large-scale applications (Thakur et al., 2021; Chen et al., 2021). However, sparse retrievers often struggle with word mismatches, leading to suboptimal recall of relevant documents (Mitra and Craswell, 2017) and undermining ODQA performance, especially given the reader’s context length limitations (Lewis et al., 2020). To address this challenge, Query Expansion (QE) techniques augment the original query with additional terms (Rocchio Jr, 1971; Lavrenko and Croft, 2001), bridging the semantic gap. With the rapid advancement of large pre-trained language

![Illustration of query expansion with ReAL. Traditional QE retrieves documents predominantly containing weakly relevant terms, such as "Friday", "13th", and "killer". ReAL enhances retrieval by assigning higher importance to key terms, such as "Jason", "first" and "Ari", resulting in improved recall and accuracy of relevant documents.](page_682_370_682_312.png)

Figure 1: Illustration of query expansion with ReAL. Traditional QE retrieves documents predominantly containing weakly relevant terms, such as "Friday", "13th", and "killer". ReAL enhances retrieval by assigning higher importance to key terms, such as "Jason", "first" and "Ari", resulting in improved recall and accuracy of relevant documents.
models (PLMs), their strong generative capabilities have been increasingly utilized in various information retrieval (Li et al., 2023b; Xiong et al., 2024) and ODQA tasks (Xin et al., 2025; Li et al., 2023d, 2025). In particular, PLM-based QE techniques utilize these models to enrich the original queries with semantically relevant terms, thereby enhancing document recall (Mao et al., 2021; Chuang et al., 2023; Chen et al., 2024). However, these methods often generate a broad set of potentially relevant terms to enrich the original queries without considering that not all expansion terms are equally important (Lavrenko and Croft, 2017).

Nevertheless, in the context of using sparse retrievers, accurately weighting query terms is of critical importance because they assign relevance scores for each term individually. In practice, PLM-aided query expansions often include many common terms alongside relevant ones, which intuitively should not be weighted the same as more critical terms. As illustrated by the examples in Figure 1, the top retrieved document fails to provide an accurate answer because several expanded terms, such as "Friday" and "13th", are only weakly relevant and deviate from the original query’s intent, which is to find information about "Ari Lehman". Therefore, the shortcoming of these QE approaches is particularly evident in the inadequate importance weighting of expanded terms, which can lead to imbalances where certain terms are either underemphasized or overemphasized, ultimately resulting in suboptimal retrieval outcomes. Although traditional QE methods like relevance models (Rocchio Jr, 1971; Lavrenko and Croft, 2001) and SPLADE (Formal et al., 2021a,b), assign term weights as part of the query expansion process, they are not well-suited for modern PLM-aided approaches. These methods lack the scalability and flexibility to capture more nuanced relationships between terms that PLMs can model effectively.

To address the challenges of inadequate term weighting and limited retrieval performance in existing PLM-aided QE methods, we propose Recall-oriented Adaptive Learning (ReAL), which enhances QE by adaptively optimizing a term importance vector for ODQA tasks. ReAL assigns a one-dimensional weight vector corresponding to the query terms, which is integrated into the retrieval model and iteratively refined using relevance signals from a classifier alongside original term frequency data. First, ReAL employs a relevance classifier to evaluate the relationship between expanded queries and initial retrieved documents, categorizing them into pseudo-relevant and pseudo-irrelevant sets. Next, ReAL optimizes the weight vector to consistently maximize the score disparity between the pseudo-relevant documents and the pseudo-irrelevant ones through two designed loss functions. Extensive experiments on four widely-used ODQA datasets and five popular QE methods demonstrate that ReAL not only improves retrieval recall but also enhances the overall performance of end-to-end QA systems.

Our contributions are three-fold: 1) We introduce a recall-oriented adaptive learning method ReAL\(^1\), which accounts for the varying importance of expansion terms, leading to more accurate retrieval. 2) Extensive experiments show that ReAL improves both retrieval quality and end-to-end QA performance across diverse datasets, highlighting its utility in practical applications. 3) Compared to previous PLM-aided QE methods, ReAL assigns importance level to the expanded query terms, aiding in the analysis of their role in retrieval.

2 Related Work

2.1 Query Expansion for ODQA

Query expansion (QE) has long been a central technique in information retrieval for enhancing retrieval by enriching queries with related terms (Croft et al., 2009; Carpineto and Romano, 2012). Especially, with the development of pre-trained language models (PLM) in various natural language processing tasks (Li et al., 2023a,c), current QE methods have shifted towards using these models to generate contextually relevant expansions (Zheng et al., 2020; Brown et al., 2020; Naseri et al., 2021). Researches like GAR (Mao et al., 2021) and EAR (Chuang et al., 2023) have leveraged sequence-to-sequence models to improve the retrieval accuracy in Open-Domain Question Answering (ODQA) tasks. Building on this foundation, large language models (LLMs) have further advanced QE for ODQA. Methods like Query2Doc (Wang et al., 2023) and AGR (Chen et al., 2024) utilize LLMs to generate more semantically enriched expansions that resolve word mismatch issues to QDQA tasks.

However, these PLM-aided QE methods often struggle with the static selection and weighting of expanded terms, leading to suboptimal retrieval per-

\footnotetext{1Our code and data are publicly available at https://github.com/process-cxr/ReAL.}
Figure 2: Overview of ReAL. Firstly an initial set of documents is retrieved through sparse retriever with expand query. Then an iterative optimization through a recall-oriented adaptive learning is used for term importance vector.

formance. Our approach, ReAL, addresses this by introducing an adaptive learning process that optimizes term importance based on relevance signals, resulting in improved retrieval effectiveness.

2.2 Term Weighting in Sparse Retrieval

Sparse retrieval methods are foundational to many information retrieval systems, widely adopted for their simplicity and efficiency. These methods offer a straightforward approach to retrieving relevant documents (Salton and Buckley, 1988; Robertson and Zaragoza, 2009), making them particularly well-suited for large-scale applications where retrieval speed is crucial. However, when integrated with modern PLM-aided QE methods, they struggle with dynamically adapting term importance based on the relevance of retrieved documents (Lv and Zhai, 2011). Although traditional studies about term weighting in sparse retrieval like relevance models (Lavrenko and Croft, 2001) and SPLADE (Formal et al., 2021a,b), assign term weights as part of the query expansion process, they are not well-suited for PLM-aided QE approaches.

In contrast, ReAL offers a more efficient, adaptive learning strategy based on relevance-aware feedback. This allows for real-time adjustments with minimal computational overhead, enhancing retrieval precision in ODQA tasks and the performance of end-to-end QA systems.

3 Method

3.1 Overview

As shown in Figure 2, given an original query \( q \) and its query expansion \( q_e \) generated by a QE technique such as Query2Doc (Wang et al., 2023), the final input query \( \overline{q} \) of ReAL is a concatenation of \( q \) and \( q_e \), containing \( m \) query terms: \( \overline{q} = q + q_e = \{t_1, t_2, \ldots, t_m\} \). ReAL first utilizes a sparse retriever capable of providing token-level scores to retrieve the top-\( n \) relevant documents \( D_{\overline{q}} = \{d_1, d_2, \ldots, d_n\} \) from the corpus, while obtaining a token-level scores vector as \( \mathbf{S}_{\overline{q}} = [s_{t_1}, s_{t_2}, \ldots, s_{t_m}] \). However, the initial vector \( \mathbf{S}_{\overline{q}} \), while indicative of statistical importance, is not differentiable and poses challenges for dynamic optimization through feedback signals. Therefore, ReAL introduces a weight vector \( \mathbf{W}_{\overline{q}} = [\mathbf{w}_{t_1}, \mathbf{w}_{t_2}, \ldots, \mathbf{w}_{t_m}] \) as an additional factor to enable dynamic adaptation. Ultimately, the optimized query \( \overline{q} \), along with the optimized weight vector \( \mathbf{w}_{\overline{q}}^{\text{last}} \) in adaptive learning, is applied to calculate the score for each document \( d_k \) via Eq. 1, improving the final retrieval precision of \( \hat{D}_{\overline{q}} \).

\[
S_k = \text{Retriever}(\overline{q}, \mathbf{W}_{\overline{q}}, d_k) = \sum_{t_i \in \overline{q} \cap d_k} \mathbf{w}_{t_i} \times \mathbf{s}_{t_i}
\]

where \( \overline{q} \cap d_k \) means the shared terms for expanded query \( \overline{q} \) and document \( d_k \).

3.2 Adaptive Learning

Relevance Classifier The relevance classifier plays a pivotal role in the ReAL framework by assessing the relevance of retrieved documents \( D_{\overline{q}} = \{d_1, d_2, \ldots, d_n\} \) based on the expanded query \( \overline{q} \). It categorizes \( D_{\overline{q}} \) into pseudo-relevant (\( D_{pr} \)) and pseudo-irrelevant (\( D_{pi} \)) sets. This classification process continues until \( D_{pr} \) contains \( s \) relevant documents and \( D_{pi} \) holds the remaining \( n - s \)
documents. The output of the relevance classifier provides foundational feedback for the subsequent optimization process. By analyzing the distributional differences in document terms between the \( D_{pr} \) and \( D_{pi} \) sets, as well as their respective subsets, the term weights of important query words are dynamically adjusted during optimization. Various types of relevance classifiers can be employed in the ReAL framework and comparative performance is discussed in Section 4.3.

Loss Function Design To optimize the term importance vector based on the relevance classifier’s output, we propose two complementary loss functions: Distinction of Slight Related Term and Separation of Clear Relevant Term. These functions work together to ensure that key query terms are prioritized while avoiding overfitting to incorrect terms.

Distinction of Slight Related Term establishes a broad separation between relevant and irrelevant documents. It ensures that query terms appearing exclusively in \( D_{pr} \), and not in \( D_{pi} \), are assigned higher weights. These terms, which are typically common across many relevant documents, play a crucial role in enhancing retrieval accuracy. This design is implemented through the following loss function, which penalizes cases where \( D_{pr} \) does not consistently achieve higher scores than \( D_{pi} \).

\[
\mathcal{L}_{Dis} = \sum_{d_i \in D_{pr}} \sum_{d_j \in D_{pi}} - \log \left( Sig(s_i - s_j) \right)
\] (2)

where \( s_i \) is the revised retrieval score of document \( d_i \) as defined in Eq. 1, and \( Sig \) is the sigmoid function to adjust the score difference into [0, 1].

Separation of Clear Relevant Term further refines the optimization by narrowing the focus to the most relevant terms. This function specifically targets terms that appear in the most relevant documents \( D_{pr}^l \) but are unlikely to be present in the bottom-ranked pseudo-irrelevant documents \( D_{pi}^b \). By emphasizing these critical terms, it increases their weight, ensuring they are properly prioritized. The loss function is formulated as:

\[
\mathcal{L}_{Sep} = \sum_{d_i \in D_{pr}^l} \sum_{d_j \in D_{pi}^b} \max \left( 0, 1 - \frac{s_i - s_j}{\tau} \right)
\] (3)

where \( \tau \) is the score difference between the median scores of document sets \( D_{pr}^l \) and \( D_{pi}^b \).

While \( \mathcal{L}_{Sep} \) focuses on a narrow set of highly relevant terms, it can lead to bias, especially if incorrect answer related terms are overly emphasized. To mitigate this risk, \( \mathcal{L}_{Dis} \) provides a broader adjustment to the term weights, ensuring that the importance of relevant terms is not overestimated at the cost of others. Together, these two loss functions complement each other: \( \mathcal{L}_{Dis} \) ensures a wide, foundational separation, while \( \mathcal{L}_{Sep} \) sharpens the focus on the most crucial terms, avoiding bias and overfitting. The effectiveness of both functions is discussed in Section 4.3.

3.3 Iterative Optimization

Given an expanded query \( \overline{q} \) and the retrieved document list \( D_{\overline{q}} = \{d_1, d_2, \ldots, d_n\} \) by the retrieval model with initial scores \( S^{(0)}_{D_{\overline{q}}} = [s_1^{(0)}, s_2^{(0)}, \ldots, s_n^{(0)}] \) for each document, we initialize the term importance vector as \( \mathbf{W}^{(0)}_{\overline{q}} = [1, 1, \ldots, 1]^m \), and iteratively optimize it by minimizing a combined objective of Eq. 2 and Eq. 3 with a weight factor \( \alpha \in [0, 1] \) as in Eq. 4.

\[
\mathcal{L}_{ReAL} = \alpha \times \mathcal{L}_{Dis} + (1 - \alpha) \times \mathcal{L}_{Sep}
\] (4)

During the \( i \)-th iteration, we compute the document scores using the term importance vector \( \mathbf{W}^{(i-1)}_{\overline{q}} \) from the previous iteration, and update it using a gradient descent algorithm with learning rate \( lr \) as in Eq. 5. The iteration continues until the loss converges (i.e., \( \mathcal{L}_{ReAL}^{(i)} - \mathcal{L}_{ReAL}^{(i-1)} \leq \delta \)) or the maximum number of steps is reached (i.e., \( i = N \)).

\[
\mathbf{W}^{(i)}_{\overline{q}} = \mathbf{W}^{(i-1)}_{\overline{q}} - lr \times \frac{\partial \mathcal{L}_{ReAL}^{(i)}}{\partial \mathbf{W}^{(i-1)}_{\overline{q}}}
\] (5)

After stopping the iteration, the optimized term importance vector \( \mathbf{W}^{(j)}_{\overline{q}} \) undergoes a scaling operation, including proportional adjustment and averaging regression. This operation restores the importance of certain key terms whose significance may have diminished during optimization due to frequent occurrence.

\[
\mathbf{W}^{last}_{\overline{q}} = \frac{\sum_{d_k \in D_{\overline{q}}} s_k^{(j)} \times \mathbf{W}^{(j)}_{\overline{q}} + \mathbf{W}^{(0)}_{\overline{q}}}{2}
\] (6)

where \( s_k^{(j)} \) is the weighted retrieval score for document \( d_k \in D_{\overline{q}} \) using term importance vector \( \mathbf{W}^{(j)}_{\overline{q}} \).

The final weight vector \( \mathbf{W}^{last}_{\overline{q}} \) is then used in a new retrieval round to obtain more relevant documents, improving both retrieval precision and overall end-to-end QA performance.
<table>
  <tr>
    <th rowspan="2">Dataset</th>
    <th colspan="2">Natural Questions</th>
    <th colspan="2">TriviaQA</th>
    <th colspan="2">WebQuestion</th>
    <th colspan="2">CuratedTREC</th>
  </tr>
  <tr>
    <th>Hit@20</th>
    <th>Hit@100</th>
    <th>Hit@20</th>
    <th>Hit@100</th>
    <th>Hit@20</th>
    <th>Hit@100</th>
    <th>Hit@20</th>
    <th>Hit@100</th>
  </tr>
  <tr>
    <td>w/o QE</td>
    <td>62.99</td>
    <td>78.22</td>
    <td>76.40</td>
    <td>83.04</td>
    <td>62.30</td>
    <td>75.49</td>
    <td>80.69</td>
    <td>89.91</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>65.59</b></td>
    <td><b>79.36</b></td>
    <td><b>77.51</b></td>
    <td><b>83.85</b></td>
    <td><b>65.35</b></td>
    <td><b>77.21</b></td>
    <td><b>83.43</b></td>
    <td><b>91.21</b></td>
  </tr>
  <tr>
    <td>Query2Doc</td>
    <td>71.77</td>
    <td>83.96</td>
    <td>79.26</td>
    <td>84.81</td>
    <td>75.39</td>
    <td>83.21</td>
    <td>89.91</td>
    <td>93.94</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>73.43</b></td>
    <td><b>84.71</b></td>
    <td><b>80.11</b></td>
    <td><b>85.54</b></td>
    <td><b>76.62</b></td>
    <td><b>83.65</b></td>
    <td><b>90.78</b></td>
    <td><b>94.38</b></td>
  </tr>
  <tr>
    <td>Gar</td>
    <td>74.40</td>
    <td>83.60</td>
    <td>73.56</td>
    <td>81.60</td>
    <td>66.14</td>
    <td>77.31</td>
    <td>82.85</td>
    <td>90.34</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>76.23</b></td>
    <td><b>85.01</b></td>
    <td><b>75.87</b></td>
    <td><b>82.56</b></td>
    <td><b>68.11</b></td>
    <td><b>78.69</b></td>
    <td><b>84.73</b></td>
    <td><b>91.79</b></td>
  </tr>
  <tr>
    <td>EAR-RI</td>
    <td>72.57</td>
    <td>83.51</td>
    <td>78.21</td>
    <td>84.27</td>
    <td>64.86</td>
    <td>78.64</td>
    <td>85.59</td>
    <td>92.79</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>74.13</b></td>
    <td><b>84.35</b></td>
    <td><b>79.17</b></td>
    <td><b>84.73</b></td>
    <td><b>66.98</b></td>
    <td><b>79.43</b></td>
    <td><b>87.61</b></td>
    <td><b>93.18</b></td>
  </tr>
  <tr>
    <td>EAR-RD</td>
    <td>75.45</td>
    <td>84.12</td>
    <td>79.55</td>
    <td>84.47</td>
    <td>68.01</td>
    <td>79.57</td>
    <td>89.19</td>
    <td>93.37</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>76.84</b></td>
    <td><b>85.04</b></td>
    <td><b>80.07</b></td>
    <td><b>84.96</b></td>
    <td><b>69.19</b></td>
    <td><b>80.56</b></td>
    <td><b>90.05</b></td>
    <td><b>93.69</b></td>
  </tr>
  <tr>
    <td>AGR</td>
    <td>77.25</td>
    <td>85.76</td>
    <td>81.87</td>
    <td>86.01</td>
    <td>74.55</td>
    <td>82.82</td>
    <td>93.37</td>
    <td>94.95</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>78.14</b></td>
    <td><b>86.04*</b></td>
    <td><b>82.43</b></td>
    <td><b>86.39*</b></td>
    <td><b>75.25</b></td>
    <td><b>83.23</b></td>
    <td><b>93.80</b></td>
    <td><b>95.39</b></td>
  </tr>
</table>

Table 1: Hit@k retrieval accuracy (%) on test sets across four open-domain QA datasets. “+ ReAL” indicates the application of our ReAL method to various QE approaches or original queries (w/o QE). All improvements are statistically significant at \( p < 0.01 \) according to the paired t-test, except for those marked with * where \( p < 0.1 \).

4 Experiments

4.1 Experimental Setup

Datasets For the evaluation, we select four diverse datasets pertinent to ODQA task, including Natural Questions (NQ) (Kwiatkowski et al., 2019), TriviaQA (Trivia) (Joshi et al., 2017), WebQuestions (WebQ) (Berant et al., 2013), and CuratedTREC (TREC) (Baudis and Sedivý, 2015). A comparative analysis is conducted to assess the improvements achieved by the ReAL method across different PLM-aided QE approaches, with a focus on its impact on sparse retriever performance across all datasets. Furthermore, within the Retriever-Reader framework for ODQA, we evaluate the end-to-end performance of ReAL on NQ and TriviaQA, measuring its overall effect on the complete ODQA pipeline.

Details of ReAL In this study, we adopt the BM25 model (Robertson and Zaragoza, 2009) as the retriever, due to its widespread use and efficient retrieval speed, particularly for models that provide token-level scores. As for the relevance classifier, our primary implementation employs a cross-encoder model, specifically the “cross-encoder/marco-MiniLM-L-12” provided by Sentence Transformers (Reimers and Gurevych, 2019). In addition, we evaluate two alternative sources of relevance signals: a bi-encoder model “BAAI/bge-base-en-v1.5” (Xiao et al., 2023), and a large language model “Mistral-7B-Instruct-v0.2” (Jiang et al., 2023). These variants are analyzed in Section 4.3 to assess their impact on retrieval performance within the ReAL framework. During the iterative optimization process, the gradient descent optimization algorithm Adam (Kingma and Ba, 2015) is used, the number of pseudo-relevant documents s used in \( L_{Dis} \) objective is set as 30, the range parameter c for defining the top and bottom documents in the \( L_{Sep} \) objective is set as 10, the loss weighting factor \( \alpha \) is set to 0.5, and the learning rate \( lr \) is configured at 0.5. The influence of these hyper-parameters is thoroughly analyzed in Section 4.3. Additionally, we use the Fusion-in-Decoder (FiD) model (Izacard and Grave, 2021) as the reader for end-to-end QA experiments.

Baselines We evaluate the performance of the ReAL method based on five retrieval approaches that process the original query \( q \) in different ways: w/o QE means directly using BM25 (Robertson and Zaragoza, 2009) model to retrieve without performing query expansion; Gar (Mao et al., 2021) adopts three types of query expansion generators based on trained seq2seq models; EAR (Chuang et al., 2023) further uses trained query rankers to reorganize the QEs by GAR; Query2doc (Wang et al., 2023) uses LLMs to generate answer-oriented passages as QEs; and AGR (Jagerman et al., 2023) proposes a multi-step generation framework with quality control mechanisms to produce more refined expansions. To ensure a fair comparison, we
<table>
  <tr>
    <th rowspan="2">Dataset</th>
    <th colspan="4">Natural Questions</th>
    <th colspan="4">TriviaQA</th>
  </tr>
  <tr>
    <th>EM@20</th>
    <th>EM@100</th>
    <th>LLM@20</th>
    <th>LLM@100</th>
    <th>EM@20</th>
    <th>EM@100</th>
    <th>LLM@20</th>
    <th>LLM@100</th>
  </tr>
  <tr>
    <td>w/o QE</td>
    <td>36.93</td>
    <td>45.26</td>
    <td>55.51</td>
    <td>62.02</td>
    <td>64.08</td>
    <td>69.03</td>
    <td>69.66</td>
    <td>73.98</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>39.06</b></td>
    <td><b>46.45</b></td>
    <td><b>57.34</b></td>
    <td><b>63.19</b></td>
    <td><b>65.45</b></td>
    <td><b>69.54</b></td>
    <td><b>70.94</b></td>
    <td><b>74.74</b></td>
  </tr>
  <tr>
    <td>Query2Doc</td>
    <td>43.57</td>
    <td>49.64</td>
    <td>63.49</td>
    <td>68.00</td>
    <td>67.35</td>
    <td>70.33</td>
    <td>73.11</td>
    <td>75.74</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>45.10</b></td>
    <td><b>50.50</b></td>
    <td><b>64.79</b></td>
    <td><b>69.14</b></td>
    <td><b>68.27</b></td>
    <td><b>70.63</b></td>
    <td><b>74.19</b></td>
    <td><b>76.26</b></td>
  </tr>
  <tr>
    <td>GAR</td>
    <td>46.09</td>
    <td>50.42</td>
    <td>63.79</td>
    <td>68.03</td>
    <td>59.64</td>
    <td>65.17</td>
    <td>65.04</td>
    <td>69.97</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>47.06</b></td>
    <td><b>51.22</b></td>
    <td><b>64.82</b></td>
    <td><b>68.50</b></td>
    <td><b>61.99</b></td>
    <td><b>66.82</b></td>
    <td><b>67.26</b></td>
    <td><b>71.65</b></td>
  </tr>
  <tr>
    <td>EAR-RI</td>
    <td>44.79</td>
    <td>49.17</td>
    <td>62.79</td>
    <td>66.12</td>
    <td>65.54</td>
    <td>69.51</td>
    <td>71.13</td>
    <td>74.15</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>45.71</b></td>
    <td><b>49.64</b></td>
    <td><b>63.43</b></td>
    <td><b>66.68</b></td>
    <td><b>66.56</b></td>
    <td><b>69.89</b></td>
    <td><b>71.76</b></td>
    <td><b>74.95</b></td>
  </tr>
  <tr>
    <td>EAR-RD</td>
    <td>46.29</td>
    <td>49.92</td>
    <td>63.40</td>
    <td>66.86</td>
    <td>66.69</td>
    <td>69.62</td>
    <td>71.75</td>
    <td>74.49</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>47.04</b></td>
    <td><b>50.44</b></td>
    <td><b>64.68</b></td>
    <td><b>67.84</b></td>
    <td><b>67.22</b></td>
    <td><b>70.08</b></td>
    <td><b>72.63</b></td>
    <td><b>75.03</b></td>
  </tr>
  <tr>
    <td>AGR</td>
    <td>48.53</td>
    <td>51.47</td>
    <td>67.83</td>
    <td>69.97</td>
    <td>70.33</td>
    <td>72.20</td>
    <td>75.61</td>
    <td>77.03</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>49.34</b></td>
    <td><b>51.91</b></td>
    <td><b>68.67</b></td>
    <td><b>70.53</b></td>
    <td><b>70.79</b></td>
    <td><b>72.48</b></td>
    <td><b>76.18</b></td>
    <td><b>77.57</b></td>
  </tr>
</table>

Table 2: End-to-end performance on the NQ and TriviaQA test datasets. @20/100 refers to the evaluation setup where the top-20 or top-100 retrieved documents are fed into the FiD model, with EM representing the exact match metric and LLM denoting the evaluation metric based on a large language model (Mistral-7B). All improvements are statistically significant at \( p < 0.01 \) according to the paired t-test.

keep the hyper-parameters and semantic similarity model configurations consistent across all QE methods when combined with ReAL.

Metrics Building on prior research in ODQA, we employ two traditional metrics (Mao et al., 2021) and a novel LLMs-based metric (Kamaloo et al., 2024) within the retriever-reader task paradigm. For retrieval accuracy, *Hit@k* is defined as the proportion of queries in which at least one relevant answer span appears within the top-*k* retrieved documents. For end-to-end QA performance, exact match score *EM@k* is employed, assessing the proportion of instances where the predicted answer span exactly matches one of the ground-truth answers after string normalization. Meanwhile, to address the limitations of string-matching evaluation, *LLM@k* metric implemented by qa-eval (Kamaloo et al., 2024) based on Mistral-7B-Instruct-v0.2 (Jiang et al., 2023) is used, it reflects the proportion of instances in which LLM with few-shot prompting determines that the predicted answer correctly aligns with the ground-truth content.

4.2 Results

Retrieval Evaluation As shown in Table 1, we assess ReAL’s performance across four datasets under different baseline methods. For the WebQuestions and CuratedTREC experiments, GAR and EAR utilized seq2seq models transferred from the NQ dataset. The key findings from the retrieval evaluations are summarized as follows:

1) **ReAL consistently enhances retrieval performance over all baseline methods.** ReAL shows notable gains in retrieval accuracy, measured by Hit@20 and Hit@100, across various datasets and baseline methods, including direct retrieval without QE, supervised QE models like GAR and EAR, and LLM-based approaches such as Query2Doc and AGR. For instance, on the NQ dataset, ReAL achieves Hit@20 improvements between 0.9% and 2.6%, and even for Hit@100, where baseline values are already high, ReAL yields gains of 0.3% to 1.5%. Similar improvements are observed across other datasets, demonstrating ReAL’s consistent effectiveness in optimizing query expansion and enhancing retrieval outcomes across different QE methods and datasets.

End-to-End QA Evaluations As shown in Table 2, we performed end-to-end QA evaluations using the Natural Questions and TriviaQA datasets. In addition to traditional exact match metrics, we employed automated evaluation using LLMs (Leval@20/100) for a more comprehensive assessment of answer quality. The key observations from these evaluations are as follows:

2) **ReAL provides notable improvements in end-to-end QA performance across various datasets.** This is evident from the EM score improvements on both the Natural Questions and TriviaQA datasets. On NQ, for example, ReAL im-
Figure 3: The impact of hyper-parameters on the performance of ReAL in terms of Hit@20 and Hit@100, including the number of pseudo-relevant documents s, the loss weighting factor \( \alpha \), the range parameter c for defining top and bottom documents, and the learning rate \( lr \).

proves EM@20 by approximately 0.8% to 2.1% across various baseline methods, demonstrating substantial benefits for ODQA tasks. Meanwhile, ReAL also shows gains in EM@100, with improvements of approximately 0.5% to 1.2%. These enhancements are consistently observed on TriviaQA as well, underscoring ReAL’s capacity to deliver more accurate answer predictions and elevate overall end-to-end QA performance.

3) LLMs-based QA evaluation further highlights the refined quality of ReAL in end-to-end tasks. The incorporation of LLMs for semantic-level evaluation offers a more comprehensive assessment of answer quality. A comparison of Leval@20/100 with EM@20/100 demonstrates that the LLM-based method more accurately evaluates cases where the generated answer partially aligns with the ground truth, capturing subtleties that traditional metrics may miss. Under this advanced evaluation approach, ReAL continues to deliver substantial improvements across all baseline methods on both the NQ and TriviaQA datasets, reinforcing its positive impact. These results further confirm that ReAL’s optimization of the query term weighting vector effectively improves overall end-to-end performance in ODQA tasks.

4.3 Analysis

In this section, all analysis experiments are conducted on a randomly sampled subset of 500 queries from the NQ dev dataset, with Query2Doc employed as the query expansion method.

Ablation Study To better comprehend the utility of ReAL, we perform ablation studies to examine the contribution of key components within the method. Specifically, we establish three variants to investigate the necessity of each component: a) w/o \( \mathcal{L}_{Dis} \) means only the \( \mathcal{L}_{Sep} \) loss is applied during iterative optimization; b) w/o \( \mathcal{L}_{Sep} \) means only the \( \mathcal{L}_{Dis} \) loss is used; c) w/o Scale means the post-processing of scale operation on the term importance vector is omitted after iterative optimization.

<table>
  <tr>
    <th>Method</th>
    <th>Hit@100</th>
    <th>EM@100</th>
    <th>LLM@100</th>
  </tr>
  <tr>
    <td>Query2Doc</td>
    <td>81.2</td>
    <td>45.2</td>
    <td>65.2</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td><b>83.0</b></td>
    <td><b>47.4</b></td>
    <td><b>67.4</b></td>
  </tr>
  <tr>
    <td>w/o \( \mathcal{L}_{Dis} \)</td>
    <td>81.6</td>
    <td>46.4</td>
    <td>65.8</td>
  </tr>
  <tr>
    <td>w/o \( \mathcal{L}_{Sep} \)</td>
    <td>82.0</td>
    <td>46.8</td>
    <td>66.2</td>
  </tr>
  <tr>
    <td>w/o Scale</td>
    <td>80.4</td>
    <td>45.0</td>
    <td>64.8</td>
  </tr>
</table>

Table 3: Ablation study results of ReAL on the adaptive learning losses and scaling operation.

From Table 3, we can draw the following conclusions: a) ReAL outperforms the variants lacking certain components, validating the effectiveness of the complete ReAL method. The full configuration demonstrates a more substantial improvement in both sparse retrieval accuracy and end-to-end QA performance when compared to its incomplete counterparts. b) While each loss function individually contributes to some improvements, their combined use proves more effective in refining the term importance vector during iterative optimization, allowing the weighted query to better align with relevant documents. c) The post-processing of scale operation is crucial to the effectiveness of ReAL. Ablation results indicate that ReAL without this operation even performs worse than when ReAL is not applied. Through analysis of the updated weight vectors, we observe that the significance of certain important terms, which appear in both relevant and non-relevant documents, is reduced during iterative optimization due to their frequent occurrence. The scaling operation, similar to a residual connection, restores the importance of these terms, ensuring that the term importance
<table>
  <tr>
    <th>Group Setting</th>
    <th>Good</th>
    <th>Bad</th>
    <th>#Query</th>
  </tr>
  <tr>
    <td>top-30</td>
    <td>24.8%</td>
    <td>13.6%</td>
    <td>368/132</td>
  </tr>
  <tr>
    <td>top-50</td>
    <td>23.1%</td>
    <td>12.7%</td>
    <td>389/111</td>
  </tr>
  <tr>
    <td>top-100</td>
    <td>26.1%</td>
    <td>10.6%</td>
    <td>406/94</td>
  </tr>
</table>

Table 4: The impact of initial retrieval quality for ReAL. “Good” initial retrieval includes at least one ground-truth document in the top-\( k \) retrieved documents, while “Bad” initial retrieval does not.

<table>
  <tr>
    <th>Method</th>
    <th>Hit@20</th>
    <th>Hit@100</th>
  </tr>
  <tr>
    <td>Query2Doc</td>
    <td>69.0</td>
    <td>81.2</td>
  </tr>
  <tr>
    <td>+ ReAL (w/ LLM)</td>
    <td><b>74.0</b></td>
    <td><b>83.0</b></td>
  </tr>
  <tr>
    <td>+ ReAL (w/ CE)</td>
    <td>72.2</td>
    <td><b>83.0</b></td>
  </tr>
  <tr>
    <td>+ ReAL (w/ BE)</td>
    <td>70.6</td>
    <td>82.0</td>
  </tr>
</table>

Table 5: The impact of relevance classifiers in ReAL, including large language model (LLM), cross-encoder (CE), and bi-encoder (BE) models.

vector accurately captures the relevant terms for optimized query performance.

Hyper-Parameter Sensitivity We further investigate the sensitivity of ReAL’s performance to four key hyper-parameters during the iterative optimization phase, as detailed in Section 4.1. The experiments presented in Figure 3 demonstrate that for parameters \( s \) and \( c \), a moderate increase in the number and range of pseudo-relevant document sets improves retrieval performance, while excessive values degrade it due to the inclusion of irrelevant documents. Accordingly, we set \( s = 30 \) and \( c = 10 \) as the optimal configuration. For hyper-parameters \( \alpha \) and \( lr \), we found that a higher \( \alpha \) favoring the \( \mathcal{L}_{Dis} \) improves Hit@20 but reduces improvements in Hit@100. To balance these effects, we set \( \alpha \) to 0.5. Additionally, we observed that the learning rate (\( lr \)) influences the effectiveness of the iterative optimization. Setting \( lr \) to 0.5 yields a better retriever performance while reducing the number of iterations and accelerating the optimization process.

Initial Retrieval Impact To better understand the dependency of ReAL on the quality of initial retrieval, we conduct a comparative analysis focusing on this aspect. Specifically, we conduct a comparative analysis by categorizing queries into two groups, Good and Bad, based on whether the initial retrieval can successfully retrieve relevant documents into top-30/50/100 results. We compare the retrieval results of the Query2Doc QE method before and after applying ReAL to the QE terms and calculate improvement rates for each group to assess the impact of initial retrieval quality on the effectiveness of ReAL. As seen in Table 4, the results confirm that ReAL’s performance is influenced by the quality of the initial retrieval, as improvements in the Good group were consistently double or more compared to those in the Bad group across all group settings. Nevertheless, despite variations in initial retrieval quality, ReAL consistently enhanced retrieval performance, further validating its effectiveness.

Relevance Model Impact As seen in Table 5, we conduct a comparative analysis of three relevance models within the ReAL framework on the NQ dev dataset, to assess their impact on retrieval performance combined with QE, including the bi-encoder model (i.e., “BAAI/bge-base-en-v1.5”), the cross-encoder model (i.e., “cross-encoder/ms-marco-MiniLM-L-12” in Sentence Transformers), and the large language model (LLM, i.e., Mistral-7B-Instruct-v0.2). The results reveal that all three models effectively serve as relevance classifiers in ReAL, enhancing retrieval accuracy and demonstrating the framework’s effectiveness. Specifically, the LLM with prompt-based natural language inference deliver the highest performance, followed by the cross-encoder models, with the bi-encoder models being less effective. However, considering the higher latency of LLMs, which require multiple inference steps, we select the cross-encoder model in this study, offering a balance between accuracy and efficiency.

<table>
  <tr>
    <th>Query Length</th>
    <th>Gen.</th>
    <th>Retr.</th>
    <th>ReAL-Cls</th>
    <th>ReAL-Iter</th>
  </tr>
  <tr>
    <td>≈ 10 tokens</td>
    <td>-</td>
    <td>0.27s</td>
    <td>0.26s</td>
    <td>0.44s</td>
  </tr>
  <tr>
    <td>≈ 60 tokens</td>
    <td>0.65s</td>
    <td>0.80s</td>
    <td>0.3s</td>
    <td>1.07s</td>
  </tr>
  <tr>
    <td>≈ 110 tokens</td>
    <td>1.27s</td>
    <td>1.95s</td>
    <td>0.33s</td>
    <td>1.67s</td>
  </tr>
</table>

Table 6: Computational latency of ReAL in different stages, including query expansion (Gen.), sparse retrieval (Retr.), cross-encoder relevance classification (ReAL-Cls) and iterative optimization (ReA-Iter).

![Computational latency of ReAL in different stages](page_1012_1012_388_180.png)
<table>
  <tr>
    <th>Method</th>
    <th>+ Rerank</th>
    <th>+ ReAL</th>
  </tr>
  <tr>
    <td>w/o QE</td>
    <td>78.22</td>
    <td><b>79.36</b></td>
  </tr>
  <tr>
    <td>Query2Doc</td>
    <td>83.96</td>
    <td><b>84.71</b></td>
  </tr>
  <tr>
    <td>GAR</td>
    <td>83.60</td>
    <td><b>85.01</b></td>
  </tr>
  <tr>
    <td>EAR-RI</td>
    <td>83.51</td>
    <td><b>84.35</b></td>
  </tr>
  <tr>
    <td>EAR-RD</td>
    <td>84.12</td>
    <td><b>85.04</b></td>
  </tr>
  <tr>
    <td>AGR</td>
    <td>85.76</td>
    <td><b>86.04</b></td>
  </tr>
</table>

Table 7: Comparison of Hit@100 results on NQ test set using Rerank and ReAL.

(Gen), sparse retrieval (Retr), cross-encoder relevance classification (ReAL-Cls), and iterative optimization (ReAL-Iter). The iterative optimization typically involves 50-90 iterations, each taking milliseconds due to the low-dimensional token weight vector, resulting from the retriever’s tokenization. As the dimensionality of the weight vector corresponds to the reduced number of query tokens, the optimization occurs in a compact space, keeping the overall latency within acceptable limits. This increase in latency is balanced by the significant improvements in retrieval accuracy and end-to-end QA performance.

4.4 More Discussion

ReAL vs Rerank  While re-ranking methods, using relevance classifiers as rerankers, reorder the top-ranked documents to improve retrieval accuracy, they are limited in scope. Re-ranking only refines the order within the static top-k documents, without expanding the set of retrieved documents. In contrast, ReAL dynamically optimizes query term weights during retrieval, allowing the framework to retrieve more relevant documents that may not have been included in the initial set. The advantage of ReAL is its ability to identify and retrieve additional relevant documents through iterative optimization, rather than just reordering existing results. This is demonstrated in the comparison in Table 7 between re-ranking and ReAL based on the same cross-encoder model (i.e., “cross-encoder/ms-marco-MiniLM-L-12” available in Sentence Transformers), where ReAL leads to higher retrieval accuracy, showing its potential to enhance retrieval performance by extending the scope of relevant document retrieval.

Future Extensions of ReAL  While ReAL has proven effective with sparse retrieval models, its framework is highly adaptable to more advanced architectures, such as dense or neural retrieval models. Specifically, we can replace the token-weight vectors in sparse retrieval with dense representations, allowing ReAL to optimize term weights based on dense retrieval scores. This integration has the potential to improve retrieval performance in large-scale ODQA tasks, enhancing both accuracy and scalability. The ability to work seamlessly with both sparse and dense retrievers would make ReAL a versatile solution for a broader range of retrieval systems, addressing emerging challenges in future research.

5 Conclusion

In this paper, we introduce ReAL, a recall-oriented adaptive learning method that enhances query expansion through an adaptive learning based on relevance feedback, allowing for more precise alignment between query terms and relevant documents. This method addresses the limitations of current QE approaches, which often fail to account for the contextual significance of expanded terms, leading to suboptimal retrieval results. By adopting an adaptive learning strategy, ReAL improves the retrieval accuracy of sparse retrievers and enhances the overall performance of end-to-end QA systems, making it an practical solution for ODQA tasks. Future work will explore extending ReAL’s applicability to more complex retrieval architectures and integrating it with deep retrieval models to further improve retrieval and QA performance.

Limitations

In this work, we focus on the combination of sparse retrieval (BM25) and current PLM-aided query expansion (QE), which is a prevalent and widely adopted approach in open-domain question answering. But actually, our ReAL framework is adaptable to a broader range of retrieval methods, owing to its design, which incorporates a term importance vector at the query level, facilitating seamless integration with additional retrieval models, such as dense retrieval models (e.g., ColBERT (Khatbab and Zaharia, 2020)) and neural sparse retrieval models (e.g., SPLADE (Lassance et al., 2024)). Besides, given the computational constraints, the investigation is limited to widely used QE methods and smaller query token sizes, thereby restricting a comprehensive exploration of ReAL’s full potential. With increased computational resources, it enables ReAL to better handle more complex and longer queries across diverse retrieval settings.
Acknowledgements

We sincerely thank the reviewers for their insightful comments and valuable suggestions. This work is supported by the National Natural Science Foundation of China (62272439) and the Fundamental Research Funds for the Central Universities.

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A  Dataset Information

Natural Questions (NQ) (Kwiatkowski et al., 2019) is a widely-used question answering dataset composed of real, anonymized queries submitted to Google. It contains 79,168 examples for training, 8,757 for development, and 3,610 for testing, making it a valuable resource for evaluating QA models on real-world search engine queries.

TriviaQA (Trivia) (Joshi et al., 2017) is a large-scale question answering dataset that includes over 950,000 question-answer pairs drawn from 662,000 Wikipedia articles and other web documents. It consists of 60,413 training examples, 8,377 development examples, and 11,313 test examples, offering a rich and diverse set of questions that challenge the breadth and adaptability of QA models.

WebQuestions (WebQ) (Berant et al., 2013) designed for question answering tasks, utilizes Freebase as its underlying knowledge base and consists of 6,642 question-answer pairs. This dataset was developed by sourcing questions through the Google Suggest API, followed by obtaining corresponding answers via Amazon Mechanical Turk. It is structured with an original split of 3,778 training examples and 2,032 testing examples. All answers are defined as Freebase entities.

CuratedTREC (TREC) (Baudis and Sedivý, 2015) is a benchmark dataset for QA systems, derived from TREC-8 (1999) to TREC-13 (2004) competitions. It includes 694 annotated entries, providing a concise yet focused set of examples that serve as a standard for evaluating QA system accuracy under controlled conditions.

B  Evaluation on Information Retrieval Benchmarks

While our primary investigation focuses on PLM-aided query expansion within the context of ODQA, we additionally evaluated the broader applicability of the proposed ReAL method in general information retrieval tasks. For this purpose, we conducted experiments on two representative ad-hoc retrieval datasets, namely TREC-DL-2019 (Craswell et al., 2020) and TREC-DL-2020 (Craswell et al., 2021), both constructed from the MS MARCO corpus and widely used for benchmarking document ranking systems. In these supplementary experiments, we adopted the same QE generation pipeline as described in the main text. Query expansion terms were generated by an LLM (i.e., Mistral-7B-Instruct-v0.2) in a zero-shot setting, and subsequently reweighted using the ReAL framework without any additional fine-tuning. Table 8 presents the retrieval performance in terms of NDCG@10, MRR, and MAP.

<table>
  <tr>
    <th>Method</th>
    <th>NDCG@10</th>
    <th>MRR</th>
    <th>MAP</th>
  </tr>
  <tr>
    <th colspan="4">TREC-DL-2019</th>
  </tr>
  <tr>
    <td>BM25</td>
    <td>50.58</td>
    <td>82.45</td>
    <td>29.93</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td>53.27</td>
    <td>86.65</td>
    <td>31.87</td>
  </tr>
  <tr>
    <td>+ QE</td>
    <td>57.57</td>
    <td>88.29</td>
    <td>35.46</td>
  </tr>
  <tr>
    <td>+ QE + ReAL</td>
    <td><b>61.69</b></td>
    <td><b>90.89</b></td>
    <td><b>35.96</b></td>
  </tr>
  <tr>
    <th colspan="4">TREC-DL-2020</th>
  </tr>
  <tr>
    <td>BM25</td>
    <td>47.96</td>
    <td>82.69</td>
    <td>30.27</td>
  </tr>
  <tr>
    <td>+ ReAL</td>
    <td>53.67</td>
    <td>87.96</td>
    <td>32.74</td>
  </tr>
  <tr>
    <td>+ QE</td>
    <td>51.04</td>
    <td>85.00</td>
    <td>32.34</td>
  </tr>
  <tr>
    <td>+ QE + ReAL</td>
    <td><b>55.47</b></td>
    <td><b>86.45</b></td>
    <td><b>37.70</b></td>
  </tr>
</table>

Table 8: Retrieval performance on general Information Retrieval (IR) datasets. ReAL consistently improves results over baselines. All improvements are statistically significant at \( p < 0.01 \) according to the paired t-test.

The experimental results confirm that ReAL consistently improves retrieval performance across all evaluation metrics. Notably, the performance gains are more pronounced when ReAL is applied in combination with query expansions generated by LLMs. These findings underscore the potential of ReAL as a general-purpose term weighting framework that extends beyond ODQA, offering promising applicability to a wider range of information retrieval tasks.

Reviewer paper summaries: Query expansion (QE) methods using pre-trained language models (PLMs) help mitigate word mismatches but often treat all expanded terms equally, leading to suboptimal retrieval. They propose ReAL, an approach that iteratively adjusts QE term weights based on relevance. By prioritizing pseudo-relevant documents and maximizing score differences between relevant and irrelevant ones, ReAL enhances term discrimination. Experiments across multiple ODQA datasets and QE methods show that ReAL consistently improves retrieval accuracy and overall QA performance.

The paper proposes a novel method to enhance the retrieval phase of Open-Domain Question Answering (ODQA) systems. Specifically, it addresses limitations in Pre-trained Language Model (PLM)-aided Query Expansion (QE) techniques, which often fail to distinguish between critical and less relevant expanded terms, leading to suboptimal retrieval performance.

This paper focuses on the Open-Domain Question Answering (ODQA) task, with the goal of improving the retrieval accuracy of sparse retrievers and addressing the limitations of existing Query Expansion (QE) methods. Specifically, it proposes ReAL, an adaptive learning-based approach that iteratively refines the relevance signal of the retrieved documents and the expanded query terms. ReAL optimizes a weight vector to distinguish between pseudo-relevant and pseudo-irrelevant documents while maximizing the score difference between them. Experiments on four ODQA datasets and five QE methods demonstrate the effectiveness of ReAL. Extensive ablation studies further highlight the significance of each design choice.
Summary of strengths: - This paper addresses a critical issue in PLM-aided QE, where naive expansion can introduce irrelevant terms.
- The proposed method effectively mitigates this issue by adaptively learning term weights per query, demonstrating consistent performance improvements across various QE methods and datasets.
- The paper is well-written and easy to follow.

1. The paper introduces a novel Recall-Oriented Adaptive Learning (ReAL) framework for improving PLM-aided Query Expansion (QE) in ODQA. Unlike existing QE methods that treat all expanded terms uniformly, ReAL dynamically adjusts the importance of each term using iterative optimization guided by relevance-aware objectives. This innovation addresses a critical bottleneck in retrieval systems, making it a significant methodological advancement in the field.
2. The paper provides comprehensive experimental evidence supporting the efficacy of ReAL. Experiments on four widely demonstrate the robustness and generalizability of ReAL. Detailed analyses confirm the effectiveness of ReAL's components, such as adaptive term weighting and iterative optimization, while showcasing its scalability across query lengths and retrieval settings.
3. The paper is well-organized and provides a thorough review of related work.

1. This paper addresses the Open-Domain Question Answering (ODQA) problem from a unique perspective. Instead of directly optimizing the expanded query, it focuses on optimizing the retrieved documents by assessing and discriminating the importance of each term, thereby enhancing retrieval accuracy.
2. The experiments are robust and extensive, featuring significant ablation studies that include separation loss, distinction loss, and scaling operations, effectively highlighting the importance of each design element in the proposed ReAL. Furthermore, additional analyses, such as hyperparameter tuning, relevance modeling, and computational latency, contribute to a comprehensive evaluation of the method.
Summary of weaknesses: - While the performance gains of the proposed method are evident, its effectiveness heavily relies on a strong retrieval model, such as a cross-encoder, for relevance classification. This raises critical concerns about why the dense retrieval models are incorporated into the query expansion frameworks. Using such models for re-ranking is a more effective and computationally efficient alternative to integrating them into QE. The authors already acknowledged that the proposed method (Gen. + Retr. + ReAL-Cls + ReAL-Iter) introduces more latency overhead than a re-ranking approach (Gen. + Retr. + ReAL-Cls), as shown in Table 6.

- The design motivation behind the loss function remains unclear. Both loss terms aim to maximize the separation between pseudo-relevant and irrelevant data, yet their distinct contributions are not well-justified. While an ablation study is provided, a clearer explanation of each term’s impact and how they complement each other would strengthen the argument.

1. While the proposed Recall-Oriented Adaptive Learning (ReAL) framework introduces an adaptive weighting mechanism, much of its conceptual foundation—such as leveraging relevance feedback and iterative optimization—is rooted in established retrieval and query expansion techniques. The core idea of refining term weights dynamically, though innovative in its implementation, may not represent a significant conceptual leap over existing methods like SPLADE or dense retrieval frameworks.
2. The experiments focus heavily on sparse retrieval models, particularly BM25, which, while widely used, are considered less state-of-the-art compared to dense retrieval and hybrid methods in ODQA. The limited focus on sparse retrievers may reduce the broader impact of the findings. The scalability and integration of ReAL with dense or neural retrieval methods remain unexplored, potentially limiting its appeal to researchers working on more advanced retrieval architectures.
3. The description of the relevance classifier used in ReAL, particularly how it integrates with sparse retrieval models, is not entirely clear. For instance, how the classifier balances pseudo-relevant and pseudo-irrelevant document sets during iterative optimization is not sufficiently detailed. While the results are promising, the lack of fine-grained error analysis (e.g., types of queries where ReAL succeeds or fails) reduces the interpretability of the findings.

1. Although the idea of discriminating the importance of each term in the query is intriguing, the performance improvements achieved by ReAL, both in retrieval accuracy and end-to-end QA performance, are relatively small. For instance, as shown in Table 1 and Table 2, the improvements range from only 0.3% to 2.6%. Moreover, no significance testing is provided to validate these results, raising concerns about their statistical reliability.
2. Some critical details about the proposed method are unclear in the paper, which may leave readers confused. The paper could be improved by addressing the following questions:
    - Did you train the relevance classifier on the four datasets, or did you use the cross-encoder model directly for classification?
    - How many active learning iterations are generally required to achieve optimal results?
    - Have you considered using an LLM as the reader model? If so, why did you ultimately choose the FiD model as the reader?
    - Does ReAL work when token-level scores from a sparse retriever are unavailable? Additionally, can ReAL function effectively with a dense retriever or an LLM as the retriever?
Comments, suggestions and typos: - How does the proposed approach compare to re-ranking methods in terms of efficiency and effectiveness? Given that strong retrieval models are used for relevance classification, why not leverage them directly for re-ranking instead of QE?
- Could the authors provide a more detailed justification for the loss function design and a deeper analysis of its individual components to support the design rationale?
- I suggest to use traditional IR-based metrics such as MAP, MRR, or NDCG.

see the weaknesses.

N/A
Overall assessment scores: 3

3

3
Metareview score: 3
Metareview: This paper proposes Recall-Oriented Adaptive Learning (ReAL), a new framework for enhancing query expansion (QE) in Open-Domain Question Answering (ODQA).  ReAL iteratively adjusts the importance weights of expanded query terms based on relevance feedback from a pseudo-relevance classifier. This framework maximises the score separation between pseudo-relevant and irrelevant documents, thereby improving retrieval accuracy and overall QA performance. The authors validate their method on four ODQA datasets across five query expansion methods, demonstrating consistent improvements in retrieval accuracy (Hit@k) and end-to-end QA performance. ReAL shows adaptability for different retrieval scenarios and provides a practical solution for refining query terms without extensive manual annotation or retriever-specific fine-tuning.
Summary of reasons to publish: The paper introduces a new adjustment mechanism for PLM-aided query expansion, enhancing retrieval accuracy by refining term importance through iterative optimisation.

The ReAL framework addresses a critical issue in query expansion and can be integrated with existing retrieval architectures.

The framework is highly extensible and can be adapted to dense retrieval models by replacing token-weight vectors with dense representations.