DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates
Authors: Qian Lin, Junyi Li, Hwee Tou Ng
Accepted at: acl25

DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates

Qian Lin        Junyi Li        Hwee Tou Ng
Department of Computer Science, National University of Singapore
qlin@u.nus.edu    junyi_cs@nus.edu.sg    nght@comp.nus.edu.sg

Abstract

The rapidly changing nature of real-world information presents challenges for large language models (LLMs), which are typically trained on static datasets. This limitation makes it difficult for LLMs to accurately perform tasks that require up-to-date knowledge, such as time-sensitive question answering (QA). In this paper, we introduce DynaQuest, a Dynamic Question answering dataset reflecting knowledge updates in the real world. DynaQuest is based on Wikipedia Infoboxes, which are frequently updated to reflect real-world changes. Our dataset is created by automatically identifying and comparing changes between different versions of Wikipedia pages and generating question-answer pairs based on these updates. To address the challenges posed by our dynamic dataset, we propose CARL, a Context-Aware Reinforcement Learning framework to improve the performance of LLMs on time-sensitive question answering. We conduct experiments on our collected dataset across recent time periods and demonstrate the effectiveness of our approach. Furthermore, we maintain a dynamic knowledge updating process, providing a periodically evolving benchmark to continually evaluate LLMs’ ability to answer time-sensitive questions.1

1 Introduction

Large language models (LLMs) have demonstrated strong capabilities in encoding extensive amounts of knowledge from massive training datasets (Radford et al., 2019; Brown et al., 2020; Touvron et al., 2023a). These models are widely applied in knowledge-intensive tasks such as question answering (QA) and reasoning, where they utilize stored world knowledge to generate appropriate answers to given queries. However, despite their strengths, LLMs face limitations when addressing time-sensitive questions, where knowledge evolves over time. The static nature of LLMs’ knowledge, derived from training on fixed datasets, prevents them from integrating real-time updates, leading to inaccurate or outdated responses as real-world information changes.

Several datasets have been proposed to study time-related questions (Chen et al., 2021; Dhingra et al., 2022; Tan et al., 2023, 2024). TimeQA (Chen et al., 2021) includes temporal questions derived from Wikidata and formulates the QA task in an open-book setting. TempReason (Tan et al., 2023) and Complex-TR (Tan et al., 2024) also primarily consist of temporal questions derived from a static Wikidata corpus. These datasets lack questions to reflect real-time updates, as they rely on a fixed snapshot of knowledge. Recent works have also utilized Wikipedia articles to create time-sensitive QA datasets. GrowOver (Ko et al., 2024) and EvolvingQA (Kim et al., 2024) generate QA pairs by detecting changes in text between different versions of Wikipedia pages. While these approaches are valuable for studying time-sensitive QA, their reliance on full-text comparisons often struggles to identify precise and relevant updates, as article edits can be unstructured and noisy.

Recent dynamic QA datasets such as RealtimeQA (Kasai et al., 2023), FreshQA (Vu et al., 2023) and CDQA (Xu et al., 2025) have been introduced to address the challenges of dynamically changing knowledge. RealtimeQA (Kasai et al., 2023) collects questions and answers from news quizzes regularly published by a small number of news websites. The dataset captures evolving knowledge from current events but is limited by its reliance on the publication schedules and content preferences of selected websites, leading to restricted domain coverage. FreshQA (Vu et al., 2023) consists of natural question-answer pairs written by human annotators. Similarly, CDQA (Xu et al., 2025) focuses on dynamic QA but is

1Our dataset and source code are available at https://github.com/nusnlp/DynaQuest.
specifically designed for the Chinese language. Both datasets rely on human annotators for regular answer updates, ensuring high-quality annotations. However, this labor-intensive approach limits scalability in both question volume and scope diversity.

To address the challenge of evolving world knowledge and aforementioned drawbacks, we aim to construct a dynamic time-sensitive QA dataset to reflect real-world change in time and serve as a testbed to evaluate the model’s knowledge and its ability to integrate with recent knowledge updates. We propose DynaQuest, a novel dynamic QA dataset consisting of questions reflecting knowledge updates over time, for evaluating LLMs over rapidly changing real-world knowledge. We introduce an automated pipeline for dataset construction, enabling regular, accurate and efficient updating of questions and answers. We leverage Wikipedia Infoboxes as an efficient knowledge source for building time-sensitive QA datasets. Infoboxes offer structured and concise summaries of key information, allowing precise identification of changes while reducing computational overhead. Focusing on Infoboxes instead of passages addresses scalability challenges in existing datasets, enables regular updates, and provides a reliable testbed for evaluating LLMs on real-time knowledge integration. Our dataset provides a periodically evolving testbed to continually evaluate LLMs’ ability to answer time-sensitive questions.

Furthermore, we conduct experiments on our constructed datasets using recent state-of-the-art LLMs. The results show that without access to retrieved up-to-date knowledge, even advanced LLMs struggle to perform well, reflecting the challenges posed by the dynamic and time-sensitive nature of our datasets. To address this challenge, we propose Context-Aware Reinforcement Learning (CARL), a learning approach that refines how the model utilizes retrieved knowledge to solve dynamic knowledge updating. CARL optimizes the balance between retrieved and parametric knowledge, guiding the model to selectively incorporate time-sensitive information while mitigating model bias towards either retrieved context or parametric knowledge. By incorporating a reward mechanism that adjusts knowledge integration, CARL enhances the model’s ability to align with evolving real-world knowledge in retrieval-augmented generation settings. Experimental results across multiple dataset periods indicate that CARL achieves performance improvements over strong baselines, demonstrating its capability to address evolving knowledge and improve LLM outputs for real-world, time-sensitive QA tasks.

In summary, our contributions are as follows:

• We propose a dynamic question answering dataset, DynaQuest, based on knowledge changes in Wikipedia Infoboxes to evaluate LLMs’ knowledge and their ability to integrate knowledge updates over time. Our data collection pipeline enables regular, accurate, and efficient updates to both questions and answers.

• We propose a novel context-aware reinforcement learning method to enhance the model’s ability to integrate retrieved knowledge while optimizing its reliance on both retrieved and parametric knowledge. Experimental results demonstrate that our method improves alignment with evolving real-world knowledge and achieves notable performance gains over strong baselines.

2 Dataset

In this section, we introduce DynaQuest, a novel dynamic dataset for evaluating LLMs on rapidly changing real-world information by constructing questions that reflect knowledge updates over time. We present the construction process of DynaQuest in Figure 1.

2.1 Source Selection

Wikipedia has become a reliable and frequently updated knowledge source for most prior work (Kim et al., 2024; Ko et al., 2024). Different from previous work utilizing unstructured passages, we consider the Infobox in Wikipedia, which is one of the most informative parts for a Wikipedia page and offers structured summaries of key facts about the subjects in this page. The infobox consists of attributes and values that highlight important facts, making them a valuable resource for identifying and tracking knowledge updates. Infobox updates reflect the latest real-world knowledge developments, further enhancing their worth for building dynamically updating datasets.

By leveraging the structured nature of Infobox, we aim to create an efficient and scalable approach for generating question-answer pairs that align with real-time knowledge updates. This focus allows us
Figure 1: Illustration of the construction process of our DynaQuest benchmark.

to provide a practical testbed for evaluating the ability of LLMs to handle evolving information.

2.2 Data Collection

The data collection involves retrieving Wikipedia pages containing specific types of Infoboxes (e.g., “country”, “person”) using Wikipedia API.

To identify relevant Infobox types, we analyze the entries listed on the “List of Infoboxes” page from Wikipedia. Our selection criteria focuses on Infobox types that 1) are associated with a large number of Wikipedia pages and 2) exhibit meaningful and frequent changes in their attributes. This selection process results in a total of 88 types of Wikipedia Infoboxes, which are further organized into different domains, detailed in Appendix A.1.

After determining the Infobox types, we retrieve Wikipedia pages under the selected types and identify changes in associated attributes and values. We obtain approximately 2.33 million English Wikipedia pages. To address the overwhelming presence of questions from “Sportsperson” domain, we only select 30,000 most frequently updated pages in this domain since 1 September 2023. This refinement finally resulted in approximately 1.98 million Wikipedia pages for dataset collection.

2.3 Benchmark Construction

Evolving Fact Identification. To construct a time-sensitive QA benchmark, we need to identify dynamically evolving facts within a pre-defined time frame. Thus, we first specify a time frame consisting of two dates and then retrieve the corresponding versions of a given Wikipedia page. Next, we compare the Infoboxes from the two versions to identify the fact changes in attributes and values. For example, as shown in Figure 1, by comparing the Wikipedia page “France” from 15 September 2024 and 1 September 2024, we identify a knowledge change in the attribute Prime Minister, where the old value Gabriel Attal was replaced by a new value Michel Barnier, corresponding to the real-world event in which French President Emmanuel Macron appointed Michel Barnier as the Prime Minister of France on 5 September 2024, succeeding Gabriel Attal.

Question Generation and Filtering. With identified changes in attributes and values, we generate questions reflecting the involved world knowledge in the Infobox changes. Specifically, we manually design the question templates for each type of attribute in the Infobox and synthesize questions by filling in corresponding attributes. The templates are shown in Appendix A.2. To ensure the quality of generated questions, we apply a filtering process to filter out the undesired questions with patterns specified in Appendix A.3. Additionally, we observe that some identified changes result from minor refinements to the older version. These changes include typo corrections (e.g., Szentlőrinci SE → Szentlőrinc SE), equivalent entity names (e.g., Bucharest, Romania → Bucharest, Kingdom of Romania), abbreviation changes (e.g., United States → U.S.) and equivalent names in different
languages (e.g., Antonio Busce → Antonio Buscè). To avoid incorporating these false positive changes, we leverage a powerful LLM (e.g., Llama-3.1-70B-Instruct) to assess whether each detected difference represents a genuine change. The prompt employed in this verification process is shown in Table 5.

2.4 Benchmark Analysis and Evaluation
The generated question-answer pairs are categorized into three types: (1) Outdated, where the answer is derived from the older version of Wikipedia pages; (2) Updated, where the answer is based on the newer version of Wikipedia pages, representing updated knowledge and an updated answer; and (3) New, where the attribute and corresponding answer do not exist in the older page and are newly added in the newer page.

In our dataset, we include question-answer pairs from the Updated and New categories within a specified time frame to capture real-world knowledge changes occurring over short periods. Currently, we conduct the benchmark maintenance and update semi-monthly by comparing Wikipedia pages for two time periods: from the 1st to the 15th day of the month, and from the 16th to the last day of the month. Our dataset collection is actively ongoing. In this work, we present the statistics of a snapshot of the benchmark, collected between 16 August 2024 and 15 October 2024, in Table 1. We also present the distribution of questions across domains in Figure 2.

For evaluation on our benchmark, we use Exact Match (EM) and F1 score as metrics, as the answers in DynaQuest are typically short and concise.

The resources and time required for dataset construction are detailed in Appendix A.5.

<table>
  <tr>
    <th></th>
    <th>0816-0831</th>
    <th>0901-0915</th>
    <th>0916-0930</th>
    <th>1001-1015</th>
  </tr>
  <tr>
    <th>Updated</th>
    <td>4440</td>
    <td>3126</td>
    <td>2113</td>
    <td>2764</td>
  </tr>
  <tr>
    <th>New</th>
    <td>3107</td>
    <td>2486</td>
    <td>2409</td>
    <td>2141</td>
  </tr>
  <tr>
    <th>Total</th>
    <td>7547</td>
    <td>5612</td>
    <td>4522</td>
    <td>4905</td>
  </tr>
</table>

Table 1: The statistics of datasets collected between 16 August 2024 and 15 October 2024.

![Pie chart showing the distribution of questions across domains: Person (20.2%), Sportsperson (23.2%), Geo location (12.3%), Officeholder (15.5%), Company (10.1%), Sports club (6.8%), University (3.3%), Political party (2.9%), Others (5.7%)](page_1012_120_410_320.png)

Figure 2: The distribution of questions across domains. The percentages are averaged over the four datasets collected between 16 August 2024 and 15 October 2024.

has become a critical technique to tackle this problem with supplemented knowledge, where the input to the model \( \mathcal{M}_\theta \) parameterized by \( \theta \) includes a prompt \( p \), question \( q \), and context \( c \):

\[
y \sim \mathcal{M}_\theta(y|p, q, c) = \prod_{t=1}^T \mathcal{M}_\theta(y_t|p, q, c, y_{<t}).
\]

where \( y \) represents the generated answer, and \( y_t \) denotes the \( t \)-th token in the output. In this work, the context \( c \) refers to the concatenation of retrieved pieces of text, which can be either the full text of a web page or a segment of texts, e.g., snippet.

3.2 Improving Knowledge Integration

LLMs learn knowledge from pre-training, but their parametric knowledge often becomes outdated due to fixed cut-off dates in pre-training corpora. This leads to challenges for tasks requiring up-to-date information in dynamic, time-sensitive domains. A knowledge conflict can arise when the knowledge retrieved from external sources contrasts with the LLM’s internal knowledge (Zhou et al., 2023; Xie et al., 2024). In RAG, documents are retrieved from external, up-to-date knowledge sources, such as the Internet, to provide the most current and comprehensive context to the LLM. This introduces potential knowledge conflict between the LLM’s outdated parametric knowledge and the time-sensitive information in the retrieved context.

To address this challenge, improving knowledge integration with retrieved knowledge is important. Some studies (Zhou et al., 2023; Zhang et al., 2024) demonstrated that it is feasible to enhance the inherent capability of LLMs to flexibly leverage the contextual, parametric knowledge, or both of them
to answer questions in cases of knowledge conflict. In this work, we propose to train the LLM to identify the useful information from the retrieved knowledge in time-sensitive QA while achieving a good balance between retrieved and parametric knowledge to guide the model to selectively incorporate updated information.

3.3 Context-Aware Reinforcement Learning

To better align the model with time-sensitive knowledge in retrieval-augmented generation, we propose Context-Aware Reinforcement Learning (CARL), a learning approach that guides the LLM in identifying and utilizing relevant information from the retrieved context while regulating its reliance on parametric knowledge. The core idea is to construct pseudo time-sensitive QA data and introduce a simple yet effective reinforcement learning algorithm, time-sensitive alignment tuning, to optimize the model’s integration of retrieved and parametric knowledge through a reward mechanism.

Pseudo Time-Sensitive Data. For existing time-sensitive QA data, the questions and answers are derived from earlier versions of Wikipedia, where the knowledge is likely already encoded in the LLM’s pre-trained parameters. Training the LLM on such data risks diminishing its effectiveness, as the model may generate answers directly from its internal knowledge rather than referencing the provided context. To address this issue, we propose to construct pseudo time-sensitive QA data where the involved knowledge has not previously been encountered by LLMs. Specifically, given a QA example from existing RAG datasets where an entry consists of a question \( q \), original retrieved context \( c_o \) with retrieved documents \( \{d_1, ..., d_k\} \), and an original answer \( a_o \), we define a knowledge modification process which modifies the retrieved context \( c_o \) and answer \( a_o \) to a pseudo context \( c_m \) and corresponding pseudo answer \( a_m \) while keeping the question \( q \) unchanged.

We introduce four types of operations to transform existing data \( (q, c_o, a_o) \rightarrow (q, c_m, a_m) \) in order to align the model with time-sensitive knowledge. We leverage the training data from TimeQA (Chen et al., 2021) to construct pseudo QA data in the following four cases. This approach creates explicit training data that is not covered by the model’s parametric knowledge. To facilitate understanding of our creation process, we present illustrative examples in Table 13 and Table 14.

• Case 1: For a time-sensitive question \( q \), we assign an irrelevant entity to \( a_m \) that is unrelated to \( q \) or \( a_o \) but with the same relation type. We then replace all occurrences of \( a_o \) in \( c_o \) with \( a_m \) to form the modified context \( c_m \).

• Case 2: For a time-sensitive question \( q \) associated with an answer \( a_o \) and corresponding time frame \( t \), we identify another question \( q' \) with the same subject as \( q \) but involves different answer \( a' \) and time frame \( t' \). We remove sentences containing \( a' \) to ensure that the answer \( a' \) is not present in the original context \( c_o \). Then we assign \( a' \) to \( a_m \) and replace all occurrences of \( a_o \) in \( c_o \) with \( a_m \) to create \( c_m \). This manner will create pseudo context \( c_m \) only containing pseudo answer \( a_m \) at time frame \( t \) but without the original answer \( a_o \).

• Case 3: For a time-sensitive question \( q \) associated with an answer \( a_o \) and corresponding time frame \( t \), we identify another question \( q' \) with the same subject as \( q \) but involves different answer \( a' \) and time frame \( t' \). We ensure that the answer \( a' \) is present in the original context \( c_o \). Then we assign \( a' \) to \( a_m \) and swap all occurrences of \( a_o \) and \( a_m \) in \( c_o \) to form \( c_m \). This way will create pseudo context \( c_m \) containing both \( a_o \) and \( a_m \) but they are in different occurrences.

• Case 4: Considering the scenario where the answer is not present in the context, for a time-sensitive question \( q \) associated with an answer \( a_o \) and corresponding time frame \( t \), we remove sentences containing \( a_o \) from the original context \( c_o \) to form the new context \( c_m \) where the answer \( a_o \) is not present and keep the answer \( a_m \) as \( a_o \). This way will prevent the model bias towards the retrieval information when the context does not contain answer and keep the reliance on the parametric knowledge of LLMs to answer the question.

Cases 1–3 target different types of conflicts between the model’s parametric knowledge and retrieved context, while Case 4 includes the case where the answer is absent from retrieval and the model must rely on parametric knowledge. These cases are based on common patterns observed during retrieval. During training data generation, we ensure that only one of the four cases is generated for each original question, avoiding any conflict from repeated questions with differing contexts and answers.

Time-Sensitive Alignment Tuning. Based on the created pseudo QA data, we propose time-sensitive alignment tuning by using reinforcement learning
(RL) to align the model’s behavior to generate answers based on retrieved and time-sensitive context. Aligning LLMs through RL has the advantage of preserving the language style of the original pre-trained models (Zhang et al., 2024), whereas supervised fine-tuning may alter the style based on the training data. Specifically, given the pseudo data \((q, c, a)^2\), we design a deterministic reward function \(r(y, a, c)\) through comparing the model prediction \(y\), the gold answer \(a\), and the context \(c\) as follows:

\[
r(y, a, c) =
\begin{cases}
+ \alpha_0, & \text{if } y = a \\
+ \alpha_1, & \text{if } (y \neq a) \land ((y \subseteq a) \lor (a \subseteq y)) \\
+ \alpha_2, & \text{if } (a \in c) \land (y \in c) \land (y \not\subseteq a) \land (a \not\subseteq y), \\
- \alpha_3, & \text{if } (a \in c) \land (y \not\in c), \\
- \alpha_4, & \text{if } (a \notin c) \land (y \in c), \\
0, & \text{Otherwise}
\end{cases}
\]

where \(\alpha_k\) are positive scalars, \(y \subseteq a\) indicates that \(y\) is a substring of \(a\), and \(y \in c\) denotes that \(y\) appears as an exact span in \(c\). Based on the reward function, we employ the Proximal Policy Optimization (PPO) algorithm to fine-tune the model (Schulman et al., 2017). The reward design accounts for both scenarios in which the answer is either present or absent in the retrieved context. Heuristically, the model receives a positive reward (\(+\alpha_0\) or \(+\alpha_1\)) when it generates a correct or partially correct answer. When the answer is present in the context, the model is encouraged (\(+\alpha_2\)) to generate responses using the retrieved knowledge while being penalized (\(-\alpha_3\)) for relying on outdated parametric knowledge. Conversely, when the answer is absent, the model is penalized (\(-\alpha_4\)) for generating responses based on the retrieved context, as this may lead to the hallucination problem. With this approach, the model learns to identify and utilize the most relevant and up-to-date information from external sources during question answering, rather than relying preferably on or altering its pre-trained internal knowledge that might be outdated.

4 Experiments

4.1 Experimental Settings

We conduct experiments using three retrieval strategies: without retrieval, with top-5 passages retrieved by a dense retriever based on Google Search results and Wikipedia pages, and with top-5 full texts retrieved from Google Search results and Wikipedia pages. The details of retrieval are provided in Appendix A.6.

In CARL training, for rewards in Eq. 2, we set the values of \(\alpha_0, \alpha_1, \alpha_2, \alpha_3\) and \(\alpha_4\) to 5.0, 4.0, 2.5, 2.5 and 2.5, respectively. The reward values are selected based on a hyperparameter search guided by the training reward graph. We adopt the Kullback-Leibler (KL) divergence between the training policy and the reference policy in regularization and set the penalty coefficient to 0.2. We implement CARL using the TRL (von Werra et al., 2022) library and LoRA (Hu et al., 2022) for parameter-efficient training. We conduct experiments of CARL based on Llama 3.1 8B Instruct, and we denote our trained model as CARL 8B. For comparison, we train Llama3.1-8B-Instruct with supervised fine-tuning (SFT) using LoRA and denote the model as SFT 8B. For both CARL and SFT, training is conducted using the General prompt template (detailed in Section 4.2).

Moreover, we perform additional evaluation on our datasets using two different evaluation metrics, Answer Rate and F1-Recall\(^3\) introduced in Xu et al. (2025).

4.2 Baselines

We compare our approach to baselines including close-source GPT-4o (OpenAI API checkpoint gpt-4o-2024-08-06) (OpenAI, 2024), open-source Llama 3.1 (Llama Team, 2024), state-of-the-art RAG model Self-RAG (Asai et al., 2024), and RiLM (Ko et al., 2024). These approaches are evaluated with the following five types of settings and prompts:

• No Retrieval. We use the prompt template in Table 6 to instruct the LLM to generate answers solely based on the question.

• Retrieval with General Prompt. We use the general prompt template in Table 7. The LLM is provided with a concatenation of retrieved full texts or passages as the context.

• Retrieval with Extractive Prompt. We use the extractive prompt template in Table 8 to instruct the LLM to extract the answer from a concatenation of retrieved full texts or passages.

\(^3\)The Answer Rate (AR) represents the percentage of effective responses, excluding refusal responses, out of all questions, while the F1-Recall (F1-R) is the F1 score computed on effective responses.
<table>
  <tr>
    <th rowspan="2">Prompt</th>
    <th colspan="2">0816-0831</th>
    <th colspan="2">0901-0915</th>
    <th colspan="2">0916-0930</th>
    <th colspan="2">1001-1015</th>
  </tr>
  <tr>
    <th>EM</th><th>F1</th>
    <th>EM</th><th>F1</th>
    <th>EM</th><th>F1</th>
    <th>EM</th><th>F1</th>
  </tr>
  <tr>
    <td colspan="9"><i>Without retrieval</i></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>No Retrieval</td><td>4.8</td><td>11.7</td><td>4.0</td><td>10.2</td><td>4.6</td><td>11.1</td><td>4.5</td><td>11.4</td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>No Retrieval</td><td>8.9</td><td>20.1</td><td>11.4</td><td>20.8</td><td>10.4</td><td>19.3</td><td>10.4</td><td>18.0</td>
  </tr>
  <tr>
    <td colspan="9"><i>With top-5 passages retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>Self-RAG 13B</td><td>Self-RAG</td><td>38.6</td><td>47.5</td><td>38.5</td><td>46.7</td><td>39.9</td><td>48.5</td><td>38.5</td><td>46.5</td>
  </tr>
  <tr>
    <td>RiLM</td><td>RiLM</td><td>32.3</td><td>47.2</td><td>34.7</td><td>49.4</td><td>35.3</td><td>49.1</td><td>35.9</td><td>49.2</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>CoT</td><td>29.1</td><td>41.2</td><td>32.0</td><td>44.0</td><td>33.8</td><td>45.4</td><td>31.2</td><td>42.9</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>Extractive</td><td>37.8</td><td>50.7</td><td>39.5</td><td>52.4</td><td>40.0</td><td>54.0</td><td>40.4</td><td>53.1</td>
  </tr>
  <tr>
    <td>SFT 8B</td><td>Extractive</td><td>39.1</td><td>51.5</td><td>41.1</td><td>53.4</td><td>41.9</td><td>54.2</td><td>42.0</td><td>53.3</td>
  </tr>
  <tr>
    <td>CARL 8B</td><td>Extractive</td><td>39.9</td><td>51.9</td><td>41.5</td><td>53.2</td><td>42.4</td><td>54.5</td><td>42.3</td><td>53.5</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>General</td><td>37.5</td><td>50.2</td><td>39.2</td><td>52.8</td><td>39.3</td><td>53.3</td><td>40.2</td><td>52.7</td>
  </tr>
  <tr>
    <td>SFT 8B</td><td>General</td><td>38.2</td><td>51.4</td><td>39.6</td><td>53.2</td><td>40.5</td><td>54.2</td><td>41.1</td><td>53.6</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td><td>General</td><td>38.7</td><td>51.2</td><td>40.9</td><td>53.7</td><td>41.5</td><td>54.5</td><td>41.5</td><td>53.4</td>
  </tr>
  <tr>
    <td colspan="9"><i>With top-5 full texts retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>RiLM</td><td>RiLM</td><td>47.1</td><td>56.6</td><td>50.9</td><td>59.0</td><td>50.5</td><td>59.6</td><td>50.7</td><td>59.6</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>CoT</td><td>36.0</td><td>46.2</td><td>41.9</td><td>52.4</td><td>41.3</td><td>52.1</td><td>39.0</td><td>49.3</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>Extractive</td><td>47.6</td><td>58.8</td><td>49.2</td><td>60.1</td><td>49.4</td><td>60.3</td><td>50.0</td><td>60.0</td>
  </tr>
  <tr>
    <td>SFT 8B</td><td>Extractive</td><td>47.7</td><td>59.1</td><td>51.5</td><td>62.5</td><td>49.5</td><td>60.5</td><td>51.0</td><td>61.3</td>
  </tr>
  <tr>
    <td>CARL 8B</td><td>Extractive</td><td>48.7</td><td>60.2</td><td>52.5</td><td>64.5</td><td>50.4</td><td>62.8</td><td>52.8</td><td>63.7</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td><td>General</td><td>47.5</td><td>58.9</td><td>51.8</td><td>60.3</td><td>52.0</td><td>60.9</td><td>52.4</td><td>60.7</td>
  </tr>
  <tr>
    <td>SFT 8B</td><td>General</td><td>50.3</td><td>59.2</td><td>54.6</td><td>62.7</td><td>53.0</td><td>61.9</td><td>54.0</td><td>62.1</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td><td>General</td><td>51.4</td><td>59.7</td><td>56.2</td><td>63.8</td><td>54.9</td><td>63.1</td><td>55.5</td><td>62.9</td>
  </tr>
</table>

Table 2: Performance on collected datasets. The column headers indicate dataset collection periods in 2024.

• Retrieval with Chain-of-Thought (CoT) Prompt. The prompt template is shown in Table 9. The LLM is provided with a concatenation of retrieved full texts or passages as the context, and guided to generate reasoning steps before providing the final answer.
• Self-RAG (Asai et al., 2024) uses special reflection tokens to improve factuality by evaluating retrieval needs, relevance, and supportiveness of documents. It generates answers per retrieved document, selecting the final answer based on inference scores from these tokens. We use prompt template in Table 10. Self-RAG 13B was built on Llama-2-13B (Touvron et al., 2023b), and we conducted Self-RAG experiments using passages due to the input token limit.
• RiLM (Ko et al., 2024) is a framework which reviews its own generated answers, generates feedback based on the retrieved context, and produces improved responses. We adopt the Llama 3.1 8B version released by Ko et al. (2024) and use prompt template in Table 11.

4.3 Main Results and Analysis

Table 2 presents the results of our models and baselines across four versions of our DynaQuest datasets collected from different time periods.

In the experiments without retrieval, GPT-4o consistently achieves the best performance among the evaluated models, with EM scores ranging from 8.9 to 11.4 and F1 scores from 18.0 to 20.8. The overall performance across all these state-of-the-art LLMs is low, exhibiting the significant challenge posed by knowledge updates in our constructed datasets for time-sensitive QA. When incorporating top-5 passages retrieved by Contriever, Llama 3.1 8B Instruct exhibit notable performance improvements compared to the Without Retrieval setting. Without RAG training, Llama 3.1 8B Instruct performs on par with Self-RAG on our datasets. With both General and Extractive prompts, CARL 8B outperforms its base model Llama 3.1 8B Instruct.

The CoT prompt shows limited effectiveness in leveraging multiple full texts, underperforming compared to all other prompt settings. Excessive reasoning can introduce unsupported inferences, increasing the risk of hallucinations in dynamic QA tasks. This aligns with the observed performance of CoT in Xu et al. (2025).

Recent LLMs like Llama 3.1, with context windows over 100 thousand tokens, enable full text to be a more effective RAG configuration. Across all
four datasets, all Llama 3.1 pre-trained models and CARL 8B consistently achieve performance gains over the passages setting using the same prompt. CARL 8B demonstrates further improvements with both General and Extractive prompts, outperforming Llama 3.1 8B Instruct and SFT 8B. We performed a paired bootstrap significance test on the F1 scores of CARL and SFT outputs, and the improvement achieved by CARL is statistically significant (\( p < 0.05 \)). More results on further periods can be observed in the additional results shown in Table 15.

The evaluation results for Answer Rate and F1-Recall are presented in Table 16 and Table 17. With an overall Answer Rate exceeding 93%, the F1-Recall comparison aligns with the F1 results in Table 2 and 15. CARL 8B achieves the best performance across all datasets.

<table>
  <tr>
    <th rowspan="2"> </th>
    <th colspan="2">0816–0831</th>
    <th colspan="2">0901–0915</th>
  </tr>
  <tr>
    <th>EM</th>
    <th>F1</th>
    <th>EM</th>
    <th>F1</th>
  </tr>
  <tr>
    <td>CARL</td>
    <td><b>51.4</b></td>
    <td><b>59.7</b></td>
    <td><b>56.2</b></td>
    <td><b>63.8</b></td>
  </tr>
  <tr>
    <td>w/o Case 1</td>
    <td>49.4</td>
    <td>59.3</td>
    <td>53.9</td>
    <td>62.0</td>
  </tr>
  <tr>
    <td>w/o Case 2</td>
    <td>49.5</td>
    <td>59.2</td>
    <td>54.0</td>
    <td>62.1</td>
  </tr>
  <tr>
    <td>w/o Case 3</td>
    <td>49.8</td>
    <td>59.4</td>
    <td>54.3</td>
    <td>62.4</td>
  </tr>
  <tr>
    <td>w/o Case 4</td>
    <td>50.2</td>
    <td>59.4</td>
    <td>54.5</td>
    <td>62.5</td>
  </tr>
  <tr>
    <th colspan="5">0916–0930 1001–1015</th>
  </tr>
  <tr>
    <th> </th>
    <th>EM</th>
    <th>F1</th>
    <th>EM</th>
    <th>F1</th>
  </tr>
  <tr>
    <td>CARL</td>
    <td><b>54.9</b></td>
    <td><b>63.1</b></td>
    <td><b>55.5</b></td>
    <td><b>62.9</b></td>
  </tr>
  <tr>
    <td>w/o Case 1</td>
    <td>53.4</td>
    <td>61.9</td>
    <td>53.8</td>
    <td>61.8</td>
  </tr>
  <tr>
    <td>w/o Case 2</td>
    <td>53.5</td>
    <td>62.1</td>
    <td>54.2</td>
    <td>62.0</td>
  </tr>
  <tr>
    <td>w/o Case 3</td>
    <td>53.9</td>
    <td>62.2</td>
    <td>54.1</td>
    <td>62.2</td>
  </tr>
  <tr>
    <td>w/o Case 4</td>
    <td>53.8</td>
    <td>62.3</td>
    <td>54.3</td>
    <td>62.1</td>
  </tr>
</table>

Table 3: Ablation study on different training case types. Results are based on the full-text setting with the General prompt.

<table>
  <tr>
    <th> </th>
    <th>PopQA</th>
    <th>TriviaQA</th>
  </tr>
  <tr>
    <td>ChatGPT*</td>
    <td>50.8</td>
    <td>65.7</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>52.0</td>
    <td><b>75.1</b></td>
  </tr>
  <tr>
    <td>Self-RAG 13B*</td>
    <td><b>55.8</b></td>
    <td>69.3</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>53.5</td>
    <td>68.9</td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>53.6</td>
    <td>70.8</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td><b>55.6</b></td>
    <td><b>72.9</b></td>
  </tr>
</table>

Table 4: Results of RAG experiments on additional datasets. The best and second-best results are bolded and underlined respectively. * results from Asai et al. (2024).

4.4 Further Analysis

We present ablation study on different training case types in Table 3. Removing any single case from CARL leads to a consistent drop in EM/F1 across all time windows, confirming that each case contributes to performance. Case 1 shows the largest drop when removed, indicating its importance in alignment between parametric and retrieved knowledge. Case 2 and Case 3 help the model adapt to conflicting information, and their removal reduces robustness. Case 4 addresses reliance on outdated parametric knowledge and improves model when no correct answer is retrieved.

To evaluate the generalization capability of CARL, we conduct experiments on QA datasets where some answers are not present in the retrieved context. Specifically, we adopt the evaluation of two short-form open-domain QA datasets used in Asai et al. (2024), including a PopQA long-tail subset (Mallen et al., 2023) and a TriviaQA test set (Joshi et al., 2017). Following the RAG experimental setup, we utilize the top-5 documents and the evaluation metric of Accuracy provided in Asai et al. (2024).

The experimental results are presented in Table 4. CARL 8B demonstrates improved performance on both datasets compared to its base model, Llama 3.1 8B Instruct. In PopQA experiments, CARL 8B achieves performance comparable to Self-RAG 13B. In TriviaQA experiments, CARL 8B outperforms Self-RAG and ChatGPT by a noticeable margin, while only remaining behind GPT-4o. These results suggest that the improved knowledge integration capability of CARL enhances its generalization to QA scenarios where the correct answer is not present in the retrieved context.

5 Related Work

Time-sensitive Question Answering. Recent works on time-related QA datasets adopt diverse approaches to address temporal reasoning and knowledge-based queries. TempReason (Tan et al., 2023) and Complex-TR (Tan et al., 2024) focus on temporal reasoning with questions derived from a static Wikidata corpus. Mousavi et al. (2024) proposed DyKnow framework to evaluate model completions of time-sensitive facts using Wikidata. In contrast, our dataset continuously updates question-answer pairs, providing a dynamic QA benchmark for evolving knowledge. RealtimeQA (Kasai et al., 2023) focuses on time-sensitive QA with questions and answers curated from news quizzes on news websites. FreshQA (Yu et al., 2023) includes natural questions with answers regularly updated by human annotators. CDQA (Xu et al., 2025) consists of dynamic Chinese questions from news
media, annotated by humans. Our dataset introduces an automated collection method, covering diverse domains with key updates derived from Wikipedia Infoboxes. GrowOver (Ko et al., 2024) and EvolvingQA (Kim et al., 2024) rely on full-text comparisons of Wikipedia versions to generate QA pairs but encounter incompleteness and difficulty in identifying precise changes. Our dataset leverages Wikipedia Infoboxes for clearer and more accurate real-world updates.

Retrieval-Augmented Generation (RAG). LLMs, reliant on pre-training data, often lack up-to-date knowledge. RAG mitigates this by retrieving relevant documents and incorporating them into model inputs, as shown in Lewis et al. (2020) and Borgeaud et al. (2022). With the increasing context window size of recent LLMs (Llama Team, 2024; OpenAI, 2024), RAG has become more effective by allowing substantial retrieved information to be included in the input. Fine-tuning retrievers and readers jointly has been shown to align retrieval and generation, as explored in works like ATLAS (Izard et al., 2023), Promptgator (Dai et al., 2023), and Shao et al. (2023). Recently, Self-RAG (Asai et al., 2024) introduced a self-reflective mechanism that uses critique tokens and enables models to evaluate the relevance and utility of retrieved information, enhancing the integration of retrieved knowledge into the generation process. RetRobust (Yoran et al., 2024) is designed to prompt LLMs to assess document relevance before answering, enhancing robustness to irrelevant context. Research has also explored improved decoding strategies, such as iterative retrieval (Trivedi et al., 2023; Shao et al., 2023), contrastive decoding (Shi et al., 2024), and dynamic context refinement (Jiang et al., 2023) to improve the generation quality. Toolformer (Schick et al., 2023) incorporates tools during generation for knowledge-intensive tasks, further enhancing the effectiveness of retrieval-augmented systems.

6 Conclusion

In this paper, we introduce DynaQuest, a dynamic question answering dataset reflecting real-world knowledge updates. Built using Wikipedia Infoboxes, DynaQuest offers an evolving benchmark for evaluating LLMs on time-sensitive QA tasks. To address the challenges posed by dynamic knowledge updates in DynaQuest, we propose CARL, a context-aware reinforcement learning method that improves LLM integration with retrieved, up-to-date knowledge. Experimental results across multiple dataset periods demonstrated the effectiveness of our approach against strong baselines.

Limitations

Our dataset relies on automated pipelines to generate question-answer pairs from Wikipedia Infoboxes. Due to the flexible editing nature of Wikipedia, real-world changes may occasionally experience delays in being reflected, and minor errors in validating these changes can occur. While proposed CARL effectively improves integration with retrieved knowledge, its reliance on reinforcement learning introduces computational overhead, which could be further optimized in future research.

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A Appendix

A.1 Infobox Types

We present the Infobox types used in our data collection process, organized into different domains detailed below.

Sportsperson: AFL biography, athlete, badminton player, baseball biography, basketball biography, boxer, Canadian Football League biography, CBB biography, CFL biography, CFL player, chess player, college basketball, college coach, college football player, cyclist, darts player, F1 driver, field hockey player, figure skater, football biography, golfer, gridiron football person, gymnast, handball biography, ice hockey biography, ice hockey player, judoka, martial artist, motorcycle rider, NFL biography, NFL player, NPB player, professional wrestler, rugby biography, rugby league biography, rugby union biography, skier, snooker player, Speedway rider, sportsperson, swimmer, table tennis player, tennis biography, volleyball biography, volleyball player

Person: academic, actor, architect, artist, astronaut, comedian, comics creator, criminal, economist, medical person, military person, model, musical artist, pageant titleholder, person, philosopher, police officer, scientist, serial killer, Twitch streamer, writer, YouTube personality
Officeholder: judge, officeholder, politician
Company: airline, company
Sports club: basketball club, football club
Political party: political party
Geographical location: country, islands, settlement
University: university
Others: award, bishop styles, Christian leader, journal, monarch, noble, religious biography, royalty, saint

A.2 Templates for Question Generation

Templates for question generation and corresponding examples are shown in Table 12. Here, {page} refers to the title of the page, {ARG1} denotes an attribute, and {ARG2} represents additional information associated with {ARG1}, as retrieved from the Wikipedia API. For instance, in the example of “Who won Golden Pen Award at Writers and Illustrators of the Future?”, the retrieved {ARG1} is “Award 1 Name” and {ARG2} specifies the award name “Golden Pen Award”, only {ARG2} is included in the question. In the example of “What is the Gini index of Syria in 2022?”, the retrieved {ARG1} is “Gini index” and {ARG2} specifies the year “2022”, both {ARG1} and {ARG2} are included in the question.

A.3 Undesired Questions

Undesired questions are those where the answers are either not expected to change, involve numbers that are not fixed, or are overly open-ended, making them difficult to evaluate. Such questions are filtered out from the dataset, including the following patterns:

    What is the birth date of ... ?
    What is the death date of ... ?
    Where is the birth place of ... ?
    Where is the death place of ... ?
    What is ... known for?
    What is the population of ... ?
    What is the capacity of home stadium of ... ?

A.4 Resources

Wikipedia API: https://www.mediawiki.org/wiki/API:Main_page
Wikipedia “List of Infoboxes” page: https://en.wikipedia.org/wiki/Wikipedia:List_of_infoboxes
Google Custom Search: https://developers.google.com/custom-search/v1/overview
Contriever-MSMARCO: https://huggingface.co/facebook/contriever-msmarco
GPT-4o API: https://platform.openai.com/docs/models
Llama 3.1 8B Instruct: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

A.5 Cost of Dataset Construction
The dataset construction pipeline consists of Wikipedia data collection, question generation, and question filtering. In each dataset construction cycle, Wikipedia data collection takes about 6 hours, primarily due to the Wikipedia API rate limit. Question generation, based on Infobox comparisons, takes less than 5 minutes and requires minimal computational resources. The question filtering process, which uses Llama-3.1-70B-Instruct, takes around 20 minutes with two H100 80GB GPUs. The pipeline does not require human annotation, enabling timely and scalable updates.

A.6 Retrieval Setup
Google Search is a widely used and intuitive knowledge source, making it a realistic scenario for real-world knowledge retrieval. To ensure that retrieved information reflects real-time knowledge, we retrieve relevant web pages by querying the generated question on Google Search engine and collecting the search results on the same day as dataset construction. Search results are obtained using the Google Custom Search API. The top 5 web pages are selected as the initial retrieval set. If the ground-truth Wikipedia page corresponding to the question is not included among the top 5 pages from Google Search results, we retain the top 4 pages and additionally include the ground-truth Wikipedia page to ensure the presence of the expected knowledge, effectively simulating the gold retrieval setting.

Based on this retrieval setup, we use the 5 selected web pages as the knowledge source for each question. Following Asai et al. (2024), for experiments involving passage-level retrieval, the full text of each page is segmented into 100-word passages, which are then ranked using Contriever (Izacard et al., 2022) (MSMARCO version). The top 5 passages are selected for corresponding experiments. For over 78.8% of the questions, the ground-truth passage containing the correct answer is included in the top 5 results by Contriever.

A.7 Implementation Details
The size of training data generated from TimeQA is 10,000. We train CARL with PPO using the TRL library. LoRA is applied with a rank of 64 and targeting modules {q_proj,v_proj,k_proj,o_proj}. The batch size is set to 2, and the learning rate is configured as \(2 \times 10^{-7}\). The training epoch is set to 2. During SFT model training, LoRA is applied with a rank of 64 and target modules {q_proj,v_proj,k_proj,o_proj}. The batch size is set to 24, and the learning rate is configured as \(2 \times 10^{-5}\). The training epoch is set to 2. The training of each model was performed once on a Nvidia H100 GPU. The training times of CARL and SFT are 12 and 8 hours, respectively.

A.8 Dataset Licensing and Availability
Wikipedia is licensed under CC BY-SA 4.0. The DynaQuest datasets will be released under the same CC BY-SA 4.0 license.

The datasets used in this paper are collected semi-monthly, and we plan to continue semi-monthly updates in the future. We will provide QA pairs along with Google Search results and Wikipedia pages for the dataset used in this paper.

Instruction:
Given the context of the question and the provided texts as answers, verify whether Text B is genuinely different from Text A for the specified question. Take into account cases such as typographical corrections, place name equivalence, abbreviations, or name variations across languages. Answer only "Yes" if Text B is genuinely different from Text A, or "No" if they represent the same entity.

Page: {page_name}
Question: {question}
Text A: {old_answer}
Text B: {new_answer}

Table 5: Prompt template for verifying the generated question-answer pairs.

Provide the shortest answer to the question without any introductory phrases or explanations: {question}

Table 6: Prompt template of No Retrieval.
Additional information: {context}
Provide the shortest answer to the question without any introductory phrases or explanations: {question}

Table 7: Prompt template of General.

Context: {context}
Question: {question}
Instruction: Extract the shortest possible answer directly from the context. The answer must be taken exactly as it appears in the context, without any modifications, rewording, or additions. Do not infer or generate content beyond what is explicitly stated. If multiple possible answers exist, choose the shortest one that fully answers the question.

Table 8: Prompt template of Extractive.

Context: {context}
Question: {question}
Based on the provided context and your knowledge, please reason through the question step by step. First, provide a clear explanation of your reasoning process. Then, provide the shortest answer as the final answer at the end in the following format:
"The final answer is ..."

Table 9: Prompt template of Chain-of-Thought (CoT) prompting.

### Instruction:
Provide the shortest response without any introductory phrases or explanations to answer the following question.

## Input:
{question}

## Response:
[Retrieval]<paragraph>
{context}
</paragraph>

Table 10: Prompt template of Self-RAG. Adapted from Asai et al. (2024).

Use the following context to answer the question.
Context: {context}
Question: {question}
Answer:

Table 11: Prompt template of RiLM. Adapted from Ko et al. (2024)
<table>
  <tr>
    <th>Template</th>
    <th>Example</th>
  </tr>
  <tr>
    <td>What is the {ARG1} of {page}?</td>
    <td>What is the reign period of Muhammad bin Talal Al Rashid?</td>
  </tr>
  <tr>
    <td>What {ARG1} did {page} play?</td>
    <td>What position did Jeremiah Pharms Jr. play?</td>
  </tr>
  <tr>
    <td>Which {ARG1} did {page} attend?</td>
    <td>Which school did Fraser McReight attend?</td>
  </tr>
  <tr>
    <td>When is the {ARG1} of {page}?</td>
    <td>When is the career end of Michael Carter-Williams?</td>
  </tr>
  <tr>
    <td>Which {ARG1} does {page} play in?</td>
    <td>Which league does Skawinka Skawina play in?</td>
  </tr>
  <tr>
    <td>Which {ARG1} does {page} play for?</td>
    <td>Which team does Cole Irvin play for?</td>
  </tr>
  <tr>
    <td>What are the {ARG1} of {page}?</td>
    <td>What are the active years of Clint Eastwood filmography?</td>
  </tr>
  <tr>
    <td>Who is the {ARG1} of {page}?</td>
    <td>Who is the manager of Phu Dong Ninh Binh FC?</td>
  </tr>
  <tr>
    <td>Who is/are the {ARG1} of {page}?</td>
    <td>Who is/are the spouse(s) of Dallas Roberts?</td>
  </tr>
  <tr>
    <td>What {ARG1} does {page} play?</td>
    <td>What sport does Rick McCrank play?</td>
  </tr>
  <tr>
    <td>When did {page} {ARG1}?</td>
    <td>When did Florida Express cease operations?</td>
  </tr>
  <tr>
    <td>How many {ARG1} has {page} recorded?</td>
    <td>How many Moto2 Poles has Fermín Aldeguer recorded?</td>
  </tr>
  <tr>
    <td>What is the {ARG1} by {page}?</td>
    <td>What is the area served by Tata Motors?</td>
  </tr>
  <tr>
    <td>Where is the {ARG1} of {page}?</td>
    <td>Where is the HQ location of Olga TV?</td>
  </tr>
  <tr>
    <td>What does {page} {ARG1}?</td>
    <td>What does Paul Lauterbur known For?</td>
  </tr>
  <tr>
    <td>How many {ARG1} does {page} have?</td>
    <td>How many members does The Way of Courage have?</td>
  </tr>
  <tr>
    <td>What group is {page} a {ARG1}?</td>
    <td>What group is Rick Savage a current member of?</td>
  </tr>
  <tr>
    <td>What group was {page} a {ARG1}?</td>
    <td>What group was El Hefe a past member of?</td>
  </tr>
  <tr>
    <td>What is {page} a {ARG1}?</td>
    <td>What is June of 44 a spinoff of?</td>
  </tr>
  <tr>
    <td>Who are the {ARG1} of {page}?</td>
    <td>Who are the parents of Jacinda Ardern?</td>
  </tr>
  <tr>
    <td>Who were the {ARG1} of {page}?</td>
    <td>Who were the past members of Corky?</td>
  </tr>
  <tr>
    <td>Where are the {ARG1} of {page}?</td>
    <td>Where are the headquarters of Libertarian Party of Maine?</td>
  </tr>
  <tr>
    <td>{page} is {ARG1} what?</td>
    <td>RDCWorld is created by what?</td>
  </tr>
  <tr>
    <td>When was {page} {ARG1}?</td>
    <td>When was Colorado Rapids founded?</td>
  </tr>
  <tr>
    <td>What is the number of {ARG1} caused by {page}?</td>
    <td>What is the number of fatalities caused by Katherine Knight?</td>
  </tr>
  <tr>
    <td>What is {page} {ARG1}?</td>
    <td>What is Jayuya, Puerto Rico named for?</td>
  </tr>
  <tr>
    <td>What is the {ARG1} for {page}?</td>
    <td>What is the number of locations for Zabka (convenience store)?</td>
  </tr>
  <tr>
    <td>What is {page}'s {ARG1} in {ARG2}?</td>
    <td>What is Landmark Theatres's revenue in 2023?</td>
  </tr>
  <tr>
    <td>How many {ARG1} are served by {page}?</td>
    <td>How many destination airports are served by Aurigny?</td>
  </tr>
  <tr>
    <td>Where is/are the {ARG1} of {page}?</td>
    <td>Where is/are the headquarters of FlyErbil?</td>
  </tr>
  <tr>
    <td>What is the {ARG1} of {page} in {ARG2}?</td>
    <td>What is the Gini index of Syria in 2022?</td>
  </tr>
  <tr>
    <td>Who is the {ARG2} of {page}?</td>
    <td>Who is the Additional Deputy Commissioner of Jonai?</td>
  </tr>
  <tr>
    <td>What is the {ARG1} of {page} home stadium? {page} is {ARG1}?</td>
    <td>What is the location of Indiana Mad Ants home stadium?</td>
  </tr>
  <tr>
    <td>What was the {ARG1} of {page}?</td>
    <td>Alden John Bell is consecrated by?</td>
  </tr>
  <tr>
    <td>When was {page}'s {ARG1}?</td>
    <td>What was the succession of Simeon Saxe-Coburg-Gotha?</td>
  </tr>
  <tr>
    <td>When did {page}'s {ARG1}?</td>
    <td>When was Otto IV, Holy Roman Emperor's coronation?</td>
  </tr>
  <tr>
    <td>Where was {page} {ARG1}?</td>
    <td>When did Rogelio del Rosario Martinez's term start?</td>
  </tr>
  <tr>
    <td>{page} was {ARG1}?</td>
    <td>Where was Vinzenz Eduard Milde buried?</td>
  </tr>
  <tr>
    <td>Who is the {ARG1} while {page} is in office?</td>
    <td>Ana de Jesús was beatified by?</td>
  </tr>
  <tr>
    <td>When did {page}'s {ARG1}?</td>
    <td>Who is the governor while Rajayavardhan Singh Rathore is in office?</td>
  </tr>
  <tr>
    <td>In which {ARG1} is {page} awarded?</td>
    <td>When did Pridi Banomyong's term start?</td>
  </tr>
  <tr>
    <td>Who has {ARG1} of {page}?</td>
    <td>In which country is Vayalar Award awarded?</td>
  </tr>
  <tr>
    <td>What are the {ARG1} by {page}?</td>
    <td>Who has most awards/wins of Nandi Award for Best Actor?</td>
  </tr>
  <tr>
    <td>Who won {ARG2} at {page}?</td>
    <td>What are the prizes awarded by National Film Award for Best Lyrics?</td>
  </tr>
  <tr>
    <td>Who won Golden Pen Award at Writers and Illustrators of the Future?</td>
    <td></td>
  </tr>
</table>

Table 12: Templates for question generation and generated examples.
Original data from TimeQA
Question q: What position did Eleftherios Papageorgopoulos take in Dec 1985?
Context c_o:
Eleftherios Papageorgopoulos ( ) is a Greek politician who served as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet from August to September 2015 . Early life and education . Papageorgopoulos was born in Chalcis on 6 October 1947 . He studied law at the University of Athens .
Papageorgopoulos served as President of Chalcis Municipality on the City Council from 1 January 1983 to 4 March 1985 . From 1 May 1985 to 31 December 1986 , Papageorgopoulos served as Mayor of Chalcis .
Papageorgopoulos was first elected as a Member of the Hellenic Parliament for Euboea in the June 1989 election .
He was subsequently re-elected in November 1989 , 1990 , 1993 , 1996 and 2000 . As an MP , he was a member of the Standing Committee on Economic Affairs and a member of the Commission for the Revision of the Constitution . He served for some time as the parliamentary spokesperson for New Democracy .
On 28 August 2015 , Papageorgopoulos was sworn in as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet , serving until 23 September 2015 .
Papageorgopoulos is married to Amalia Passa and has one daughter and two sons .
Answer a_o: Mayor

[Case 1]
Irrelevant entity selected from TimeQA with the same relation type (position held): Deputy Minister of Irrigation
Question q: What position did Eleftherios Papageorgopoulos take in Dec 1985?
Context c_m:
Eleftherios Papageorgopoulos ( ) is a Greek politician who served as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet from August to September 2015 . Early life and education . Papageorgopoulos was born in Chalcis on 6 October 1947 . He studied law at the University of Athens .
Papageorgopoulos served as President of Chalcis Municipality on the City Council from 1 January 1983 to 4 March 1985 . From 1 May 1985 to 31 December 1986 , Papageorgopoulos served as Deputy Minister of Irrigation of Chalcis .
Papageorgopoulos was first elected as a Member of the Hellenic Parliament for Euboea in the June 1989 election . He was subsequently re-elected in November 1989 , 1990 , 1993 , 1996 and 2000 . As an MP , he was a member of the Standing Committee on Economic Affairs and a member of the Commission for the Revision of the Constitution . He served for some time as the parliamentary spokesperson for New Democracy .
On 28 August 2015 , Papageorgopoulos was sworn in as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet , serving until 23 September 2015 .
Papageorgopoulos is married to Amalia Passa and has one daughter and two sons .
Answer a_m: Deputy Minister of Irrigation

[Case 2]
Another question q' with the same subject but a different time frame: What position did Eleftherios Papageorgopoulos take between Dec 1993 and Jul 1996?
with answer a : Member of the Hellenic Parliament
Question q: What position did Eleftherios Papageorgopoulos take in Dec 1985?
Context c_m:
Eleftherios Papageorgopoulos ( ) is a Greek politician who served as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet from August to September 2015 . Early life and education . Papageorgopoulos was born in Chalcis on 6 October 1947 . He studied law at the University of Athens .
Papageorgopoulos served as President of Chalcis Municipality on the City Council from 1 January 1983 to 4 March 1985 . From 1 May 1985 to 31 December 1986 , Papageorgopoulos served as Member of the Hellenic Parliament of Chalcis .
He was subsequently re-elected in November 1989 , 1990 , 1993 , 1996 and 2000 . As an MP , he was a member of the Standing Committee on Economic Affairs and a member of the Commission for the Revision of the Constitution . He served for some time as the parliamentary spokesperson for New Democracy .
On 28 August 2015 , Papageorgopoulos was sworn in as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet , serving until 23 September 2015 .
Papageorgopoulos is married to Amalia Passa and has one daughter and two sons .
Answer a_m: Member of the Hellenic Parliament
(We remove the sentence “Papageorgopoulos was first elected as a Member of the Hellenic Parliament for Euboea in the June 1989 election.” from the context to avoid conflicting information within the content.)

Table 13: Illustration of training data generation for CARL. Finally we use question q, context c_m and answer a_m from each generation process during training.
[Case 3]
Another question \( q' \) with the same subject but a different time frame: What position did Eleftherios Papageorgopoulos take between Dec 1993 and Jul 1996?
with answer \( a' \): Member of the Hellenic Parliament
Question \( q \): What position did Eleftherios Papageorgopoulos take in Dec 1985?
Context \( c_m \):
Eleftherios Papageorgopoulos ( ) is a Greek politician who served as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet from August to September 2015 . Early life and education . Papageorgopoulos was born in Chalcis on 6 October 1947 . He studied law at the University of Athens .
Papageorgopoulos served as President of Chalcis Municipality on the City Council from 1 January 1983 to 4 March 1985 . From 1 May 1985 to 31 December 1986 , Papageorgopoulos served as Member of the Hellenic Parliament of Chalcis .
Papageorgopoulos was first elected as a Mayor for Euboea in the June 1989 election .
He was subsequently re-elected in November 1989 , 1990 , 1993 , 1996 and 2000 . As an MP , he was a member of the Standing Committee on Economic Affairs and a member of the Commission for the Revision of the Constitution . He served for some time as the parliamentary spokesperson for New Democracy .
On 28 August 2015 , Papageorgopoulos was sworn in as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet , serving until 23 September 2015 .
Papageorgopoulos is married to Amalia Passa and has one daughter and two sons .
Answer \( a_m \): Member of the Hellenic Parliament

[Case 4]
The sentence containing the original answer \( a_o \) (Mayor) is removed from the original context \( c_o \) to generate the modified context \( c_m \). Since the answer is no longer present in context \( c_m \), the model is expected to utilize its parametric knowledge to generate the answer.
Question \( q \): What position did Eleftherios Papageorgopoulos take in Dec 1985?
Context \( c_m \):
Eleftherios Papageorgopoulos ( ) is a Greek politician who served as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet from August to September 2015 . Early life and education . Papageorgopoulos was born in Chalcis on 6 October 1947 . He studied law at the University of Athens .
Papageorgopoulos served as President of Chalcis Municipality on the City Council from 1 January 1983 to 4 March 1985 . Papageorgopoulos was first elected as a Member of the Hellenic Parliament for Euboea in the June 1989 election .
He was subsequently re-elected in November 1989 , 1990 , 1993 , 1996 and 2000 . As an MP , he was a member of the Standing Committee on Economic Affairs and a member of the Commission for the Revision of the Constitution . He served for some time as the parliamentary spokesperson for New Democracy .
On 28 August 2015 , Papageorgopoulos was sworn in as a Minister of State in Vassiliki Thanou-Christophilous caretaker cabinet , serving until 23 September 2015 .
Papageorgopoulos is married to Amalia Passa and has one daughter and two sons .
Answer \( a_m \): Mayor

Table 14: (Continued) Illustration of training data generation for CARL. Finally we use question \( q \), context \( c_m \) and answer \( a_m \) from each generation process during training.
<table>
  <tr>
    <th rowspan="2">Prompt</th>
    <th colspan="2">1016-1031</th>
    <th colspan="2">1101-1115</th>
    <th colspan="2">1116-1130</th>
  </tr>
  <tr>
    <th>EM</th><th>F1</th>
    <th>EM</th><th>F1</th>
    <th>EM</th><th>F1</th>
  </tr>
  <tr>
    <td colspan="7"><i>Without retrieval</i></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>No Retrieval</td>
    <td>2.9</td><td>9.5</td>
    <td>3.5</td><td>10.1</td>
    <td>3.0</td><td>9.9</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>No Retrieval</td>
    <td>8.2</td><td>18.5</td>
    <td>9.1</td><td>18.2</td>
    <td>9.9</td><td>20.6</td>
  </tr>
  <tr>
    <td colspan="7"><i>With top-5 passages retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>Self-RAG 13B</td>
    <td>Self-RAG</td>
    <td>38.0</td><td>46.0</td>
    <td>37.5</td><td>45.1</td>
    <td>39.6</td><td>47.6</td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>32.8</td><td>46.8</td>
    <td>34.5</td><td>47.4</td>
    <td>34.8</td><td>48.6</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>29.3</td><td>41.2</td>
    <td>30.1</td><td>41.8</td>
    <td>30.6</td><td>41.5</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>36.7</td><td>49.9</td>
    <td>39.4</td><td>51.0</td>
    <td>39.3</td><td>52.0</td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>38.1</td><td>50.1</td>
    <td>39.6</td><td>51.5</td>
    <td>40.3</td><td>52.7</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>Extractive</td>
    <td>39.1</td><td>50.6</td>
    <td>41.0</td><td>51.7</td>
    <td>43.6</td><td>53.9</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>36.8</td><td>50.1</td>
    <td>38.8</td><td>50.8</td>
    <td>39.0</td><td>52.0</td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>37.8</td><td>51.0</td>
    <td>39.9</td><td>51.9</td>
    <td>40.3</td><td>53.1</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td>38.1</td><td>50.7</td>
    <td>40.1</td><td>51.5</td>
    <td>41.2</td><td>53.3</td>
  </tr>
  <tr>
    <td colspan="7"><i>With top-5 full texts retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>47.0</td><td>54.5</td>
    <td>48.2</td><td>56.0</td>
    <td>50.0</td><td>57.9</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>38.7</td><td>49.1</td>
    <td>38.2</td><td>48.0</td>
    <td>38.3</td><td>48.5</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>47.4</td><td>57.9</td>
    <td>47.9</td><td>57.5</td>
    <td>50.3</td><td>60.2</td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>47.8</td><td>59.8</td>
    <td>48.4</td><td>57.7</td>
    <td>50.8</td><td>61.5</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>Extractive</td>
    <td>48.9</td><td>60.7</td>
    <td>49.4</td><td>60.1</td>
    <td>52.0</td><td>63.8</td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>49.5</td><td>57.7</td>
    <td>49.7</td><td>58.0</td>
    <td>52.0</td><td>60.2</td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>51.6</td><td>59.9</td>
    <td>51.4</td><td>59.5</td>
    <td>54.4</td><td>62.2</td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td><b>53.3</b></td><td><b>60.9</b></td>
    <td><b>53.3</b></td><td><b>60.7</b></td>
    <td><b>56.6</b></td><td><b>63.7</b></td>
  </tr>
</table>

Table 15: Additional results on datasets collected between 16 October 2024 and 30 November 2024.
<table>
  <tr>
    <th rowspan="2">Prompt</th>
    <th colspan="2">0816-0831</th>
    <th colspan="2">0901-0915</th>
    <th colspan="2">0916-0930</th>
    <th colspan="2">1001-1015</th>
  </tr>
  <tr>
    <th>F1-R</th><th>AR</th>
    <th>F1-R</th><th>AR</th>
    <th>F1-R</th><th>AR</th>
    <th>F1-R</th><th>AR</th>
  </tr>
  <tr>
    <td colspan="9"><i>Without retrieval</i></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>No Retrieval</td>
    <td>11.8 (99.9)</td>
    <td>10.2 (100)</td>
    <td>11.1 (99.9)</td>
    <td>11.4 (99.8)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>No Retrieval</td>
    <td>20.1 (99.9)</td>
    <td>20.8 (100)</td>
    <td>19.4 (99.7)</td>
    <td>18.1 (99.9)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td colspan="9"><i>With top-5 passages retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>Self-RAG 13B</td>
    <td>Self-RAG</td>
    <td>47.5 (99.9)</td>
    <td>46.7 (99.9)</td>
    <td>48.5 (99.9)</td>
    <td>46.5 (100)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>47.8 (99.6)</td>
    <td>49.4 (99.6)</td>
    <td>49.5 (99.5)</td>
    <td>50.1 (99.6)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>42.2 (96.3)</td>
    <td>45.1 (96.2)</td>
    <td>46.5 (96.3)</td>
    <td>44.1 (96.3)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>51.1 (95.0)</td>
    <td>53.2 (94.0)</td>
    <td>55.0 (93.2)</td>
    <td>54.3 (93.1)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>51.6 (97.6)</td>
    <td>53.9 (97.3)</td>
    <td>55.3 (97.3)</td>
    <td>54.3 (97.0)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>Extractive</td>
    <td>51.8 (98.7)</td>
    <td>53.8 (98.8)</td>
    <td>56.1 (98.5)</td>
    <td>54.9 (98.5)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>51.0 (97.2)</td>
    <td>53.4 (96.7)</td>
    <td>54.4 (95.9)</td>
    <td>53.6 (96.2)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>51.5 (99.2)</td>
    <td>54.0 (99.2)</td>
    <td>54.5 (98.9)</td>
    <td>54.1 (98.9)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td>51.9 (99.0)</td>
    <td>54.1 (98.9)</td>
    <td>55.1 (98.7)</td>
    <td>54.1 (98.7)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td colspan="9"><i>With top-5 full texts retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>59.0 (97.5)</td>
    <td>59.7 (97.0)</td>
    <td>60.5 (96.3)</td>
    <td>61.0 (96.3)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>47.5 (96.8)</td>
    <td>53.2 (96.7)</td>
    <td>53.8 (96.4)</td>
    <td>51.2 (95.9)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>58.9 (97.2)</td>
    <td>60.8 (96.7)</td>
    <td>61.0 (96.6)</td>
    <td>60.8 (96.3)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>59.6 (99.9)</td>
    <td>63.5 (99.9)</td>
    <td>60.5 (99.8)</td>
    <td>61.3 (100)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B</td>
    <td>Extractive</td>
    <td>60.2 (99.9)</td>
    <td><b>64.5</b> (100)</td>
    <td>62.8 (99.9)</td>
    <td><b>63.7</b> (100)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>59.5 (97.9)</td>
    <td>61.7 (97.6)</td>
    <td>61.9 (97.2)</td>
    <td>61.4 (97.3)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>59.6 (99.2)</td>
    <td>63.2 (99.3)</td>
    <td>62.5 (99.1)</td>
    <td>62.6 (99.0)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td><b>60.3</b> (99.0)</td>
    <td>64.4 (99.1)</td>
    <td><b>63.9</b> (98.8)</td>
    <td>63.6 (98.9)</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 16: Performance on datasets collected between 16 August 2024 and 15 October 2024, with F1-Recall (F1-R) and Answer Rate (AR, in %) as evaluation metrics.
<table>
  <tr>
    <th rowspan="2">Prompt</th>
    <th colspan="2">1016-1031</th>
    <th colspan="2">1101-1115</th>
    <th colspan="2">1116-1130</th>
  </tr>
  <tr>
    <th>F1-R</th>
    <th>AR</th>
    <th>F1-R</th>
    <th>AR</th>
    <th>F1-R</th>
    <th>AR</th>
  </tr>
  <tr>
    <td colspan="7"><i>Without retrieval</i></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>No Retrieval</td>
    <td>9.6 (99.9)</td>
    <td>10.1 (99.9)</td>
    <td>9.9 (99.9)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>No Retrieval</td>
    <td>18.5 (99.9)</td>
    <td>18.2 (99.9)</td>
    <td>20.7 (99.7)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td colspan="7"><i>With top-5 passages retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>Self-RAG 13B</td>
    <td>Self-RAG</td>
    <td>46.0 (100)</td>
    <td>45.1 (100)</td>
    <td>47.6 (99.9)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>46.8 (99.1)</td>
    <td>47.4 (99.1)</td>
    <td>48.6 (99.5)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Qwen2.5 7B Instruct</td>
    <td>CoT</td>
    <td>42.8 (96.1)</td>
    <td>42.4 (96.1)</td>
    <td>43.0 (95.8)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>43.0 (95.5)</td>
    <td>42.9 (95.8)</td>
    <td>42.5 (95.3)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>50.1 (93.3)</td>
    <td>52.2 (93.3)</td>
    <td>53.1 (93.7)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>50.3 (97.2)</td>
    <td>52.3 (97.3)</td>
    <td>53.5 (96.9)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>Extractive</td>
    <td>51.3 (98.4)</td>
    <td>52.0 (98.7)</td>
    <td>54.3 (98.3)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>51.0 (96.8)</td>
    <td>52.1 (96.7)</td>
    <td>52.9 (96.3)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>51.5 (99.1)</td>
    <td>52.3 (99.1)</td>
    <td>53.4 (99.0)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td>51.1 (98.8)</td>
    <td>52.1 (98.9)</td>
    <td>53.9 (98.7)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td colspan="7"><i>With top-5 full texts retrieved from Google Search results and Wikipedia page</i></td>
  </tr>
  <tr>
    <td>RiLM</td>
    <td>RiLM</td>
    <td>58.5 (97.0)</td>
    <td>57.8 (98.0)</td>
    <td>60.8 (96.8)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>CoT</td>
    <td>50.0 (96.5)</td>
    <td>49.3 (96.7)</td>
    <td>50.5 (96.2)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>Extractive</td>
    <td>58.4 (96.8)</td>
    <td>57.7 (97.3)</td>
    <td>60.6 (96.5)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>Extractive</td>
    <td>59.8 (99.9)</td>
    <td>57.7 (99.9)</td>
    <td>61.5 (99.9)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>Extractive</td>
    <td>60.7 (100)</td>
    <td>60.1 (99.9)</td>
    <td>63.8 (99.9)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama 3.1 8B Instruct</td>
    <td>General</td>
    <td>59.0 (97.7)</td>
    <td>59.0 (97.8)</td>
    <td>61.1 (97.2)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>SFT 8B</td>
    <td>General</td>
    <td>60.4 (99.2)</td>
    <td>60.0 (99.2)</td>
    <td>62.8 (99.1)</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>CARL 8B (Ours)</td>
    <td>General</td>
    <td><b>61.6</b> (98.9)</td>
    <td><b>61.3</b> (99.0)</td>
    <td><b>64.4</b> (98.8)</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 17: Performance on datasets collected between 16 October 2024 and 30 November 2024, with F1-Recall (F1-R) and Answer Rate (AR, in %) as evaluation metrics.

Reviewer paper summaries: The paper proposes a time-sensitive question-answering dataset called DynaQuest, which is based on Wikipedia Infoboxes and has frequent updates reflecting real-world changes. To address this challenge, a Context-Focused RL framework has been proposed to improve LLMs' performances for time-sensitive questions. The experiments have demonstrated the advantages of the proposed framework over the baselines.

In this paper, the authors introduce DynaQuest, a Dynamic Question answering dataset reflecting knowledge updates in the real world. DynaQuest is based on Wikipedia Infoboxes, which are frequently updated to reflect real-world changes.
Summary of strengths: 1) The curation of the DynaQuest is semi-automatic, which is more efficient and scalable than other benchmarks, such as FreshQA, in evaluating the LLMs' performances of answering questions based on evolving information.

2) The DynaQuest will be updated semi-monthly, which could effectively facilitate future research on time-sensitive question answering.

3) The proposed CFRL is effective in prioritizing LLMs to use retrieved information to generate answers. This also results in higher performance in accurately answering the time-sensitive questions compared to other baselines.

1. The focused direction is promising and important for the community of LLMs.
2. The studied methods, such as Improving Knowledge Integration, Context-Focused Reinforcement Learning, and Time-Sensitive Alignment Tuning are valued for Dynamic Question Answering of LLMs.
3. The results of experiments are effective.
Summary of weaknesses: 1) **The proposed CFRL could be rigid in inducing LLMs to be biased on the retrieved information**: The CFRL uses synthetic data to train the LLMs, which introduces the fabrications of information updates. This could be detrimental to the factuality of LLMs.

2) **Missing related work**: The CDQA paper[1] also uses a similar semi-automatic method in curating the time-sensitive question-answering benchmark, where entities are extracted from the latest news on the Internet, questions are synthesized by GPT-4 and further filtered by human labelers.


[1] Xu, Zhikun, et al. "Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark." arXiv preprint arXiv:2402.19248 (2024).

1. The most important question is, how do you keep your data sets updated dynamically? That said, it's important to come up with reasonable mechanisms to ensure that your dataset labels are always of high quality.
2. I think it's too strict to choose EM and F1 as metrics, especially since we know that LLMs tend to have liberal and diverse responses. In my opinion, the evaluation metrics used in [1], such as F1-recall and Answer Rate are more reasonable, especially for Dynamic QA.
3. Similarly, I recommend sitting down and referring to the setup of the experimental analysis in [1]. As far as I'm concerned, the experiments in your paper are still not detailed enough.
4. I look forward to more analysis of dataset quality, such as annotation quality, human evaluation, etc.

[1] Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark.
Comments, suggestions and typos: - Line 452, a full stop is missed.

See the above weaknesses.
Overall assessment scores: 4

2.5