MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance
Authors: Joseph J Peper, Wenzhao Qiu, Ali Payani, Lu Wang
Accepted at: acl25

MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance

Joseph J. Peper¹    Wenzhao Qiu¹    Ali Payani²    Lu Wang¹

¹Computer Science and Engineering, University of Michigan, Ann Arbor, MI
²Cisco Research, San Jose, CA
{jpeper,qwzhao,wangluxy}@umich.edu
apayani@cisco.com

Abstract
Natural language processing evaluation has made significant progress, largely driven by the proliferation of powerful large language models (LLMs). New evaluation benchmarks are of increasing priority as the reasoning capabilities of LLMs are expanding at a rapid pace. In particular, while multi-document (MD) reasoning is an area of extreme relevance given LLM capabilities in handling longer-context inputs, few benchmarks exist to rigorously examine model behavior in this setting. Moreover, the multi-document setting is historically challenging for benchmark creation due to the expensive cost of annotating long inputs.

In this work, we introduce MDBENCH, a new dataset for evaluating LLMs on the task of multi-document reasoning. Notably, MDBENCH is created through a novel synthetic generation process, allowing us to controllably and efficiently generate challenging document sets and the corresponding question-answer (QA) examples. Our novel technique operates on condensed structured seed knowledge, modifying it through LLM-assisted edits to induce MD-specific reasoning challenges. We then convert this structured knowledge into a natural text surface form, generating a document set and corresponding QA example. We analyze the behavior of frontier LLMs and prompting techniques, finding that MDBENCH poses significant challenges for all methods, even with relatively short document sets. We also see our knowledge-guided generation technique (1) allows us to readily perform targeted analysis of MD-specific reasoning capabilities and (2) can be adapted quickly to account for new challenges and future modeling improvements.

remarkable improvements in various tasks, including understanding, generating, and reasoning over text. However, despite these advancements, evaluation frameworks for NLP systems have struggled to keep pace (Chang et al., 2024), notably for tasks involving reasoning over multiple documents (Mavi et al., 2024).

Multi-document (MD) reasoning involves synthesizing and inferring information across multiple diverse texts (Caciularu et al., 2021), posing unique challenges not addressed by traditional single-document benchmarks. While LLMs are increasingly capable of handling longer-context multi-document inputs, there is a scarcity of benchmarks that rigorously examine the specific reasoning characteristics that are prominent in this setting. In addition, many existing benchmarks consist of static, hand-crafted datasets, which are labor-intensive to produce. These datasets are often susceptible to data contamination (Xu et al., 2024) over time, e.g., LLMs are exposed to public benchmarks during training. This can compromise the integrity of the evaluation.

In this work, we address these limitations with MDBENCH¹, a benchmark using a novel generation technique for multi-document reasoning evaluation. Our benchmark is generated through a synthetic process that leverages structured knowledge as seed information. This process uses a strong LLM (GPT-4o) to augment structured knowledge by injecting complexities that require advanced reasoning skills, then generates text documents from the augmented knowledge. Synthetic generation allows for efficient example creation and can be easily adapted to incorporate new reasoning skills and objectives.

Our benchmark generation pipeline (overviewed in Figure 1) begins with a structured knowledge

¹Benchmark can be found at https://huggingface.co/launch/MDBench. Code and prompts are accessible at https://github.com/jpeper/MDBench.

1 Introduction

The rapid advancements in natural language processing (NLP) have been largely driven by the development and deployment of large language models (LLMs). These models have showcased
Figure 1: MDBENCH generation pipeline overview. We 1) source structured knowledge, then use in-context multi-document reasoning demonstrations to 2) intentionally modify the existing knowledge with challenging dependencies. We then 3) map this seed knowledge into document form to produce the multi-document QA example. Finally, we 4) employ an added validity check to filter inconsistent examples that yield erratic outputs in an oracle setting where example construction knowledge is provided.

source serving as the seed information. Each knowledge entry (i.e., a row of a table) encapsulates distinct knowledge that forms the basis of a document in the generated set. We use tabular data as these are widely utilized and inherently structured, with row entries often sharing topical connections, making them an ideal foundation for evaluating cross-document reasoning. We follow a multi-step augmentation process to source knowledge, augment knowledge, and generate high-quality document sets with multi-document reasoning challenges:

1. Source Seed Knowledge (Fig. 1, Step 1) We collect tabular data where each row contains information that will contribute to a generated document.

2. Augment Knowledge (Fig. 1, Step 2) Using GPT-4o, we edit the structured knowledge to inject challenging reasoning dependencies and enrich the context for document creation. By treating rows as proxies for documents, we model cross-document dependencies through cross-row knowledge interactions. In this step, we also generate question-answer pairs that utilize the introduced reasoning dependencies.

3. Generate Natural Text (Fig. 1, Step 3) We map the augmented knowledge into natural text by generating a corresponding multi-document set from the augmented table. This process allows us to systematically inject critical reasoning challenges while producing examples that are realistic and fluent.

4. Automated Quality Validation (Fig. 1, Step 4) To ensure high quality and consistency, we apply targeted consistency prompts and a novel oracle-setting self-consistency check that effectively filters out low-quality examples, enabling rapid benchmark scaling.

We produce 1,000 multi-document QA examples using this pipeline (300 human-validated, and 700 more automatically-validated for quality) and evaluate the performance of models from several prominent LLM families including GPT (OpenAI et al., 2024), Claude (Anthropic), Gemini (Team et al., 2024a), and LLaMA (Dubey et al., 2024). We find that:

• MDBENCH poses a strong challenge, even for state-of-the-art methods, with the best ones achieving ~81% accuracy and ~62% exact-match performance on this MD reasoning task.
• Frontier models such as GPT-4o, GPT-o1 and Gemini-2.5-Flash significantly outperform smaller LLMs across different prompting methods. This highlights the importance of model capacity and sophistication in handling complex multi-document reasoning tasks.

• When comparing performance on document reasoning versus tabular reasoning (i.e., structured format pre-document generation), we find that strong models are mostly performant in both settings, but all struggle more in the document setting. This suggests that multi-document reasoning is influenced by both the fundamental reasoning complexity, and also from the nuances of the surface form.

• Prompting techniques such as chain-of-thought (Wei et al., 2022) can improve performance across strong models. However, they are insufficient to significantly enhance the performance of weaker models like LLaMA-3-7B and even GPT-3.5. This indicates that while prompting strategies can aid reasoning, underlying model capabilities remain a limiting factor for this task, which makes MD-BENCH suitable for future, advanced model evaluation.

2 Related Work

Evaluating the capabilities of LLMs is a critical aspect of NLP research. As LLMs continue to improve rapidly, existing evaluation frameworks often lag behind, particularly in assessing complex reasoning abilities such as multi-document (MD) reasoning. As LLMs rapidly increase in reasoning capacity, there is a pressing need to develop evaluation methods that can capture these higher-order reasoning skills.

Multi-Document Reasoning MD reasoning involves synthesizing and inferring information across multiple texts. Existing work in this area includes datasets targeting specific phenomena such as temporal reasoning (Xiong et al., 2024; Wan, 2007), summarization (Xiao et al., 2021; Peper et al., 2023; Lior et al., 2024), multi-hop question answering with HotPotQA, MuSiQue and 2Wiki-MultiHop (Yang et al., 2018; Ho et al., 2020; Trivedi et al., 2022) and ambiguous entity resolution (Lee et al., 2024). Notably, many of these MD datasets are publicly-sourced and often reliant on significant human effort to curate. For example, Zhu et al. (2024) introduce FanOutQA, a recent multi-hop, multi-document question answering dataset, which targeted decomposable QA examples sourced from public Wikipedia knowledge and relied on thousands of manual annotations. Our work seeks to use knowledge-controlled generation to offer a scalable alternative for producing nuanced and unseen multi-document reasoning examples.

Tabular Reasoning with LLMs LLMs have demonstrated strong performance in tasks involving structured knowledge, such as tabular data or knowledge bases (Lu et al., 2024; Li et al., 2023a). Recent studies have observed success in applying LLMs to table reasoning, manipulation, and augmentation (Lu et al., 2024; Li et al., 2023a). While there are limitations in LLM pre-training which can lead to formatting sensitivities and limitations with handling large tables, Nahid and Rafiei (2024) find improved performance by decomposing the tabular knowledge into a digestible size. Similarly, leveraging tabular knowledge within reasoning chains allows for compact and effective representation of complex problems, as explored in the Chain-of-Tables framework (Wang et al., 2024). These insights highlight the potential of using condensed knowledge as a foundation for generating challenging reasoning tasks. Although our work draws on structured data to simplify the creation of examples, our ultimate goal is to produce longer, more nuanced natural-language text document sets.

LLM-Supported Synthetic Benchmark Creation To address the need for more dynamic evaluation datasets, LLM-powered synthetic benchmark creation has gained significant traction (Long et al., 2024; Liu et al., 2024; Li et al., 2023b), particularly as there is growing concern of benchmark data contamination (Xu et al., 2024). Some work has been done in the multi-document setting, although automation is largely used for extending existing annotated multi-document benchmarks to more complex tasks (Schnitzler et al., 2024). While not directly modeling multi-document tasks, works like BoardGameQA (Kazemi et al., 2023) and MuSR (Sprague et al., 2023) explore synthetic generation in the related multi-step reasoning setting, with MuSR using a neurosymbolic generation algorithm which maps synthetic structure into natural text examples. Our method seeks to build off
related work in synthetic generation to address efficient multi-document benchmark creation.

3 MDBENCH Generation Pipeline

In this section, we motivate and overview the generation process, and provide details on the components and steps taken to produce the MDBENCH evaluation benchmark.

Our design decisions for the MDBENCH benchmark are guided by several key objectives to ensure the resulting examples test a broad and relevant range of reasoning skills:

• Contain Novel and Unseen Text: We aim to produce examples that are not merely scraped from public datasets but rather contain newly-generated content. This ensures that models are tested on scenarios they have not encountered during training, avoiding overfitting to pre-existing benchmarks.

• Contains Cross-Document Knowledge Dependencies: A key focus is to produce examples that require reasoning across multiple documents. We design our benchmark to have intentional cross-document dependencies, making them particularly challenging to effectively test multi-document reasoning capabilities.

• Grounded in Real-World Scenarios: Even though the examples are synthetically generated, they should ideally remain grounded in real-world concepts and situations. This ensures that the reasoning challenges presented are realistic and relevant to practical NLP applications.

• Counterfactual Alterations: To further mitigate data contamination and leakage risks from public sources, we allow slight counterfactual or fictional twists on the real-world scenarios occurring in the seed dataset to necessitate that models leverage the extrinsic knowledge found in the input. This allows for a fresh take on familiar domains while maintaining the integrity of the benchmark.

• Scalability and Control: Our approach is designed to offer control during benchmark generation. We allow one to specify seed information such as domain and behavior types, and can control the complexity and nature of the reasoning tasks present in the benchmark.

<table>
  <tr>
    <th></th>
    <th>Min</th>
    <th>Mean (std)</th>
    <th>Max</th>
  </tr>
  <tr>
    <td># Rows per Table</td>
    <td>5</td>
    <td>8.31 (2.4)</td>
    <td>17</td>
  </tr>
  <tr>
    <td># Table Columns</td>
    <td>3</td>
    <td>5.17 (1.2)</td>
    <td>9</td>
  </tr>
  <tr>
    <td>Token Length (Tab.)</td>
    <td>121</td>
    <td>256.0 (81.6)</td>
    <td>554</td>
  </tr>
  <tr>
    <td>Token Length (Doc.)</td>
    <td>1048</td>
    <td>2397.4 (738.4)</td>
    <td>6493</td>
  </tr>
  <tr>
    <td>Avg. Doc Length</td>
    <td>177</td>
    <td>268.1 (37.6)</td>
    <td>388</td>
  </tr>
</table>

Table 1: MDBENCH benchmark statistics. Each row in the tabular representation ultimately corresponds to a document within the multi-document example. We see a roughly nine-fold increase in surface form length when mapping the structured knowledge to natural text document format.

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>belgium</td>
    <td>600</td>
    <td>october 20, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
  <tr>
    <td>october 25, 2006</td>
    <td>turkey</td>
    <td>500</td>
    <td>october 25, 2006</td>
    <td>turkey</td>
    <td>500</td>
  </tr>
</table>

<table>
  <tr>
    <th colspan="3">Baseline Question: Which country had the most showings and how many total?</th>
    <th colspan="3">Harder Example: Which country had the most showings and how many total?</th>
  </tr>
  <tr>
    <td>Answer: Belgium had the most with 600 showings.</td>
    <td>Answer: Turkey had the most with 700 showings.</td>
  </tr>
  <tr>
    <td>Answer Rationale: Turkey had 200 showings and Belgium had 600. 600 &gt; 200, therefore Turkey had the most showings.</td>
    <td>Answer Rationale: Turkey had 200 showings, 700 &gt; Belgium’s 600, therefore Turkey had the most.</td>
  </tr>
  <tr>
    <td>Commentary: This is a simple reasoning process as it requires a simple comparison of two values with no additional reasoning required.</td>
    <td>Commentary: By adding a new row with complementary information, we necessitate an additional reasoning hop to correctly answer the question. Note that this table was edited specifically such that the answer (Turkey) is flipped from the original answer (Belgium) in the simple example.</td>
  </tr>
</table>

Figure 2: Example skill description on “Multi-hop Reasoning”. During knowledge augmentations, we demonstrate the multi-document skills relevant to the document sets.

3.1 Pipeline Overview

Our benchmark generation pipeline begins with structured knowledge sourced from tabular data, which serves as the seed for the augmentation process. This structured knowledge is systematically enriched and refined through a strong LLM to inject reasoning dependencies that challenge models to infer information across multiple documents. Figure 1 overviews the pipeline. Below, we provide details of the four major steps:

Step 1: Obtaining Seed Knowledge  We start with an intuition that compressed structured knowledge provides an effective foundation for multi-document reasoning. Several valid sources of this exist, such as knowledge bases, tabular information, or even by performing information extraction to consolidate data from existing documents and text corpora. For the MDBENCH benchmark, we utilize the TabFact (Chen et al., 2020) dataset, which comprises 16,000 tables sourced from Wikipedia. Our motivation for exploring this dataset is threefold: (1) TabFact tables provide a reliable and curated
<table>
  <tr>
    <th>Reasoning Type</th>
    <th>Description</th>
  </tr>
  <tr>
    <td>Multi-hop Reasoning</td>
    <td>Solving problems requiring multiple steps to arrive at the solution.</td>
  </tr>
  <tr>
    <td>Numeric Reasoning</td>
    <td>Handling numeric values and performing numerical operations.</td>
  </tr>
  <tr>
    <td>Temporal Reasoning</td>
    <td>Handle temporal information and temporal dependencies.</td>
  </tr>
  <tr>
    <td>Knowledge Aggregation</td>
    <td>Aligning, comparing and/or contrasting knowledge that may be present.</td>
  </tr>
  <tr>
    <td>Soft Reasoning</td>
    <td>Reasoning abductively and making informed decisions in cases where some uncertainty or fuzziness may be present, such as cross-document entity linking.</td>
  </tr>
</table>

Table 2: Reasoning skills overview. For our benchmark, we focus on five goals which are especially relevant for the multi-document setting. We provide demonstrations of these reasoning types to inspire relevant knowledge edits during the generation process.

source of seed knowledge; (2) the data spans a wide range of domains, including news, sports, media, and technology; and (3) it has an emphasis on human-readability both in scale and content. This structured knowledge serves as the starting point for our knowledge augmentation process, which significantly transforms the raw data into more challenging and complex reasoning tasks. We heuristically filter the dataset to select tables which are rich in content yet manageable in size, choosing those with 5 to 17 rows and 3 to 9 columns.

Step 2: Knowledge Augmentation An important component of our technique is the knowledge augmentation step. This step modifies information, applying operations that inject complex knowledge dependencies and reasoning challenges. Figure 1 overviews our pipeline, while full detailed examples of the knowledge augmentation prompts are provided in Appendix A. Below, we describe two key techniques we employ:

• Multi-document Reasoning Demonstrations Prior to altering the existing information, we first demonstrate relevant skills for multi-document reasoning. Each skill is demonstrated in both ‘simple’ and ‘challenging’ forms (e.g., Figure 2). The demonstrations include examples, along with explanations and rationales for solving them. For the purpose of this benchmark, we define and focus on five reasoning components which are particularly relevant in the multi-document setting: (1) Multi-hop, (2) Numeric, (3) Temporal, (4) Soft reasoning, and (5) Knowledge Aggregation.

We briefly describe these skills in Table 2, and provide full demonstrations of each in Appendix A.

• Knowledge Augmentation Demonstrations In addition to demonstrating relevant reasoning skills, we next provide knowledge edit demonstrations. These demonstrations illustrate plans for how simple tables can be enhanced to form nuanced QA examples. Each demonstration consists of an initial table, a series of edits, and a resultant augmented table and QA annotation. When performing knowledge augmentation, we sample one demonstration from a small set of high-quality curated examples. Figure 6 demonstrates a knowledge edit plan.

Through these two steps, we modify the tabular knowledge to form a more nuanced QA example with cross-row knowledge dependencies.

Step 3: Document Set Generation Once the tabular knowledge has been augmented, we map this information into natural language text; each row in the table is used to generate a document, with the augmented knowledge ensuring that reasoning across documents (rows) is required to solve the accompanying QA task. We independently generate each document, with the generation prompt parameterized by the following components: (1) the augmented table and title, (2) the column names, and (3) a specific row of content within the table indicated for generation. Iterating this process over all n rows in the table, we generate an n-document set. This approach of knowledge-grounded generation ensures the generated document set maintains logical coherence while presenting unique cross-document reasoning challenges. Figures 12 and 13 demonstrate a full generated MDBENCH multi-document example.

Step 4: Automated Quality Validation To ensure a high-quality and scalable benchmark, our
pipeline employs a multi-step automated validation process. First, we apply targeted prompts (Appendix D) to verify that each component—the seed table, edit plans, intermediate edited table, and final document set—adheres to the intended instructions. Next, we perform an oracle self-consistency check based on the intuition that the procedural steps used to construct an example can also verify its consistency. Specifically, for each QA example, we prompt GPT-4o under three variations of oracle knowledge (original table and edit plan) and context (generated table and generated document set) as shown in Figure 1. An example is rejected if any of the three variations yield an answer which does not match the generated ground-truth. This comprehensive validation ensures internal consistency and underpins scalable, automated quality control.

3.2 MDBENCH Benchmark Generation Details

We utilize GPT-4o for both table augmentation and document generation. Table 1 summarizes the statistics of the resultant benchmark. We produce a benchmark comprising 1,000 examples.

Automated Validation Our pipeline integrates automated validation (including both localized validation checks as well as the oracle self-consistency check) to ensure quality and consistency throughout generation. During generation, we find the automated filtering retained approximately 32% of the generated examples.

Human Validation To assess the quality of the generated and auto-verified examples, we hire and train a native English-speaking graduate student with linguistics background for this task (compensated at $15 per hour for training and annotation). The annotator is instructed to validate the consistency of the generated multi-document QA examples (see Appendix 5). We find our automated validation yields an overall example validity of rate of 87%. Our final released benchmark comprises 300 human-validated examples and 700 additional machine-validated examples.

4 Experimental Setup

To assess the challenges of MDBENCH, we test the performance of many popular LLMs in combination with conventional prompting setups. Concretely, we test open-weights LLMs with Meta’s LLaMA-3 (Dubey et al., 2024), using the 8B-Instruct and 70B-Instruct variants. For API-based proprietary methods, we use models from the Anthropic Claude, OpenAI GPT, and Google Gemini families, which represent current state-of-the-art LLMs. For Claude we evaluate Claude-3-Opus 20240229 and Claude-3.5-Sonnet 202406202. For GPT we test GPT-3.5-Turbo-16k 06133 (Ouyang et al., 2022), GPT-4o 2024-08-064, and GPT-o1 2024-12-175. For Gemini we include Gemini-1.5-Pro 0514 (Team et al., 2024b) and the recent Gemini-2.5-Flash-preview 05-206.

We explore both zero-shot and one-shot QA prompting scenarios, noting that when prompting in the one-shot case we use a single representative demonstration across models for consistency. We use a conventional question-answering prompt, and also further instruct the models to ‘think step by step’ to additionally produce chain-of-thought (CoT) rationales. Examples of these prompt formats are provided in Appendix B. To evaluate on the QA task, we use GPT-4o as a reference-based scorer, first parsing the final answer from each output, then comparing the similarity of the predicted answer with the ground-truth answer (conditioned on the original question). We calculate

![Bar chart showing overall exact-match performance of models on MDBENCH, comparing Table Reasoning and Document Reasoning for various models](page_1012_120_410_312.png)

Figure 3: Overall exact-match performance of models on MDBENCH. Table Reasoning is when evaluated with the intermediate table QA examples. Document Reasoning refers to the performance on the final task of multi-document reasoning. Document reasoning poses a harder challenge, speaking to the importance of surface form in reasoning performance.

2https://www.anthropic.com/claude
3https://platform.openai.com/docs/models/gpt-3-5
4https://platform.openai.com/docs/models/gpt-4o
5https://platform.openai.com/docs/models/o1
6https://ai.google.dev/gemini-api/docs/models/2.5-flash
<table>
  <tr>
    <th>Model</th>
    <th>Zero-shot</th>
    <th>Zero-shot CoT</th>
    <th>One-shot</th>
    <th>One-shot CoT</th>
    <th>Overall</th>
  </tr>
  <tr>
    <td>LLaMA-3-8B-Instruct</td>
    <td>42.7</td>
    <td>43.1</td>
    <td>39.3</td>
    <td>38.9</td>
    <td>41.0</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B-Instruct</td>
    <td>51.2</td>
    <td>50.2</td>
    <td>45.0</td>
    <td>49.3</td>
    <td>48.9</td>
  </tr>
  <tr>
    <td>Claude-3-Opus</td>
    <td>55.5</td>
    <td>54.5</td>
    <td>56.4</td>
    <td>53.6</td>
    <td>55.0</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>59.2</td>
    <td>56.4</td>
    <td>58.3</td>
    <td>55.9</td>
    <td>57.5</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Pro</td>
    <td>54.0</td>
    <td>57.8</td>
    <td>54.0</td>
    <td>54.0</td>
    <td>55.0</td>
  </tr>
  <tr>
    <td>Gemini-2.5-Flash</td>
    <td>57.8</td>
    <td>58.3</td>
    <td>58.8</td>
    <td><b>60.2</b></td>
    <td>58.8</td>
  </tr>
  <tr>
    <td>GPT-3.5-Turbo</td>
    <td>49.8</td>
    <td>37.4</td>
    <td>44.5</td>
    <td>36.5</td>
    <td>42.1</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td><b>59.7</b></td>
    <td><b>62.1</b></td>
    <td><b>59.7</b></td>
    <td>58.3</td>
    <td><b>60.0</b></td>
  </tr>
  <tr>
    <td>GPT-o1</td>
    <td>57.3</td>
    <td>60.7</td>
    <td>59.7</td>
    <td>59.2</td>
    <td>59.2</td>
  </tr>
</table>

<table>
  <tr>
    <th>Model</th>
    <th>Zero-shot</th>
    <th>Zero-shot CoT</th>
    <th>One-shot</th>
    <th>One-shot CoT</th>
    <th>Overall</th>
  </tr>
  <tr>
    <td>LLaMA-3-8B-Instruct</td>
    <td>65.7</td>
    <td>66.8</td>
    <td>60.7</td>
    <td>63.5</td>
    <td>64.2</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B-Instruct</td>
    <td>76.1</td>
    <td>75.6</td>
    <td>67.4</td>
    <td>71.6</td>
    <td>72.7</td>
  </tr>
  <tr>
    <td>Claude-3-Opus</td>
    <td>79.3</td>
    <td>74.2</td>
    <td>78.8</td>
    <td>74.2</td>
    <td>76.6</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>78.0</td>
    <td>77.3</td>
    <td>80.3</td>
    <td>77.9</td>
    <td>78.4</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Pro</td>
    <td>77.1</td>
    <td>80.0</td>
    <td>77.7</td>
    <td>74.9</td>
    <td>77.4</td>
  </tr>
  <tr>
    <td>Gemini-2.5-Flash</td>
    <td>77.9</td>
    <td>79.2</td>
    <td>79.7</td>
    <td>79.7</td>
    <td>79.1</td>
  </tr>
  <tr>
    <td>GPT-3.5-Turbo</td>
    <td>70.4</td>
    <td>57.3</td>
    <td>66.3</td>
    <td>58.7</td>
    <td>63.2</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>79.1</td>
    <td>79.8</td>
    <td>80.8</td>
    <td>78.3</td>
    <td>79.5</td>
  </tr>
  <tr>
    <td>GPT-o1</td>
    <td><b>81.1</b></td>
    <td><b>83.3</b></td>
    <td><b>82.2</b></td>
    <td><b>82.4</b></td>
    <td><b>82.2</b></td>
  </tr>
</table>

Table 3: Document Reasoning Overall Results. We report exact-match (top) and accuracy (bottom) results on the MDBENCH multi-document examples.

both an exact match score as well as an accuracy score, where the scorer can assign partial correctness credit (0-10 scale) for multi-part questions. Full post-processing and scoring prompts are provided in Appendix C.

5 Results and Analysis

Overall Findings Figure 3 overviews the overall task performance on the multi-document and intermediate tabular versions of the dataset, while Table 3 covers the detailed performance in the target multi-document setting. MDBENCH poses a strong challenge, with the best methods achieving roughly 60% exact-match performance. GPT-4o leads with 60.0% overall EM and 62.1% in the zero-shot CoT setting, with GPT-o1 close behind at 59.2% EM. Notably, we see o1 attains the highest accuracy score across the board in the document setting (82.2% overall). We generally observe that o1, with its deep reasoning capacities, performs better in the CoT and one-shot settings than in zero-shot. Gemini-1.5 Pro struggles relatively amongst proprietary models, however, the recent Gemini-2.5-Flash improves consistently over it in most settings, peaking at 60.2% EM under one-shot CoT.

Overall, we see relatively strong performance from one or more models from each family, with Claude-3.5-Sonnet performing best among the Claude family. The use of CoT prompting yields performance gains in larger models like Gemini-Flash-2.5, GPT-o1 and GPT-4o, although the effect varies. We see open-weight models struggle overall, although LLaMA-3-70B bests GPT-3.5-Turbo, with the 8B variant comparable in performance to GPT-3.5.

Document vs. Tabular Reasoning To ascertain the impact of surface form on the reasoning task, we compare the performance of models on the full multi-document version of the benchmark versus the table version (i.e., stopping after step 2 in our pipeline). Table 4 overviews the table-reasoning results, and the comparison of overall results can be seen in Figure 3. We observe that performance is noticeably higher on the condensed tabular format than in the document setting, with GPT-4o achieving the highest table-reasoning performance at 71.2% overall exact-match, and GPT-o1 similarly performing strongly in the accuracy metric. A key exception to the tabular vs. document trend is with the LLaMA models, which struggle consider-
<table>
  <tr>
    <th>Model</th>
    <th>Zero-shot</th>
    <th>Zero-shot CoT</th>
    <th>One-shot</th>
    <th>One-shot CoT</th>
    <th>Overall</th>
  </tr>
  <tr>
    <td>LLaMA-3-8B-Instruct</td>
    <td>38.4</td>
    <td>45.0</td>
    <td>19.9</td>
    <td>39.8</td>
    <td>35.8</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B-Instruct</td>
    <td>57.3</td>
    <td>64.9</td>
    <td>30.8</td>
    <td>56.4</td>
    <td>52.4</td>
  </tr>
  <tr>
    <td>Claude-3-Opus</td>
    <td>65.4</td>
    <td>62.1</td>
    <td>65.9</td>
    <td>66.4</td>
    <td>64.9</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>69.2</td>
    <td>67.3</td>
    <td>67.3</td>
    <td>64.0</td>
    <td>66.9</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Pro</td>
    <td>63.0</td>
    <td>67.3</td>
    <td>62.1</td>
    <td>64.0</td>
    <td>64.1</td>
  </tr>
  <tr>
    <td>Gemini-2.5-Flash</td>
    <td>64.0</td>
    <td>65.4</td>
    <td>63.5</td>
    <td>64.9</td>
    <td>64.5</td>
  </tr>
  <tr>
    <td>GPT-3.5-Turbo</td>
    <td>51.2</td>
    <td>49.8</td>
    <td>54.5</td>
    <td>52.6</td>
    <td>52.0</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td><b>71.6</b></td>
    <td><b>69.2</b></td>
    <td><b>73.0</b></td>
    <td><b>71.1</b></td>
    <td><b>71.2</b></td>
  </tr>
  <tr>
    <td>GPT-o1</td>
    <td>66.3</td>
    <td>68.7</td>
    <td>69.7</td>
    <td>66.8</td>
    <td>67.9</td>
  </tr>
</table>

<table>
  <tr>
    <th>Model</th>
    <th>Zero-shot</th>
    <th>Zero-shot CoT</th>
    <th>One-shot</th>
    <th>One-shot CoT</th>
    <th>Overall</th>
  </tr>
  <tr>
    <td>LLaMA-3-8B-Instruct</td>
    <td>59.0</td>
    <td>65.1</td>
    <td>28.0</td>
    <td>54.1</td>
    <td>51.5</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B-Instruct</td>
    <td>75.3</td>
    <td>79.4</td>
    <td>39.7</td>
    <td>73.5</td>
    <td>66.9</td>
  </tr>
  <tr>
    <td>Claude-3-Opus</td>
    <td>79.8</td>
    <td>80.4</td>
    <td>80.1</td>
    <td>80.3</td>
    <td>80.1</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>84.1</td>
    <td>83.3</td>
    <td>82.8</td>
    <td>79.7</td>
    <td>82.5</td>
  </tr>
  <tr>
    <td>Gemini-2.5-Flash</td>
    <td>79.3</td>
    <td>78.8</td>
    <td>79.4</td>
    <td>79.6</td>
    <td>79.3</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Pro</td>
    <td>80.8</td>
    <td>81.8</td>
    <td>78.3</td>
    <td>81.0</td>
    <td>80.5</td>
  </tr>
  <tr>
    <td>GPT-3.5-Turbo</td>
    <td>69.1</td>
    <td>67.9</td>
    <td>70.0</td>
    <td>67.3</td>
    <td>68.6</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td><b>85.7</b></td>
    <td><b>81.8</b></td>
    <td><b>86.9</b></td>
    <td><b>83.6</b></td>
    <td><b>84.5</b></td>
  </tr>
  <tr>
    <td>GPT-o1</td>
    <td>84.5</td>
    <td><b>84.9</b></td>
    <td>83.5</td>
    <td><b>84.1</b></td>
    <td>84.2</td>
  </tr>
</table>

Table 4: Table Reasoning Overall Results. We report exact-match (top) and accuracy (bottom) when applying models to the augmented tabular format QA examples (as opposed to documents).

<table>
  <tr>
    <th>Ablation</th>
    <th>GPT-4o</th>
    <th>GPT-3.5</th>
  </tr>
  <tr>
    <td>Original Doc. Set</td>
    <td>59.7</td>
    <td>49.8</td>
  </tr>
  <tr>
    <td>- No Delimiter</td>
    <td>55.5</td>
    <td>45.0</td>
  </tr>
  <tr>
    <td>- Shuffled</td>
    <td>53.1</td>
    <td>39.3</td>
  </tr>
  <tr>
    <td>- No Delimiter + Shuff.</td>
    <td>50.2</td>
    <td>41.7</td>
  </tr>
</table>

Table 5: We analyze the significance of document ordering and delimitation within our benchmark (in the zero-shot setting). We see that both shuffling the order of the original docs, and removing “Document (num)” delimiters impact performance, implying the existence of temporal and cross-document dependencies within our reasoning problems.

ably more on the tabular reasoning task, performing significantly worse than GPT-3.5 in this setting.

Characteristic Breakdown We additionally evaluate the performance as a function of the example difficulty. To do this, we prompt GPT-4o to generate characteristic-level difficulty scores for each example. We use the same five characteristics as demonstrated in the generation process, and prompt the model with these definitions. Rather than generating absolute scores, we instead approximate difficulty by prompting GPT-4o to perform comparative ranking with two other randomly sampled examples for each characteristic. We aggregate these relative rankings over the entire dataset to form two difficulty bins per characteristic, as overviewed in Figure 4.

We see mostly consistent trends across characteristics, with temporal reasoning posing the steepest dropoff between the simple and hard bins. Interestingly, we see soft reasoning is impacted inversely, with performance increasing on the split of examples ranked to have harder soft-reasoning components. While some of this may be due to small sample size for for the hard bin (only 45 of 300 examples), we suspect there is an inverse relationship between soft reasoning and more ‘explicit’ characteristics such as numeric and temporal. For example, a table/example well-suited for temporal reasoning may naturally contain less ‘soft’ information requirements. Conversely, an example with significant soft reasoning requirements likely contains fewer hard reasoning requirements.
Figure 4: Characteristic-level performance breakdown. We partition the examples by difficulty for each characteristic (as assessed by an LLM judge) and report each model’s overall accuracy on each of the bins (simple and hard).

Document Ordering Analysis  Finally, we conduct a brief case study with GPT-4o and GPT-3.5 to assess the significance of document ordering. Table 5 shows that both explicit document delimiters and the canonical ordering of the documents are substantive cues for current LLMs. Removing the “Document (num)” tags lowers GPT-4o accuracy by 4.2 points and GPT-3.5 by 4.8 points, indicating that models leverage these textual anchors to partition the multi-document contexts reliably. Shuffling the document order (while keeping delimiters) produces an even steeper decline (6.6 points for GPT-4o, 10.5 points for GPT-3.5), suggesting that the questions often rely on temporal or positional relationships established by the original document sequence. When both cues are ablated simultaneously, performance drops even further relative to the baseline. Taken together, the results confirm that MDBENCH examples are not solvable by treating each document in isolation. Rather, models must track cross-document dependencies that are sensitive to both ordering and explicit boundary markers.

6 Conclusion

We present MDBENCH, a novel benchmark for evaluating multi-document reasoning in LLMs. Leveraging structured seed knowledge and augmenting it with nuanced reasoning dependencies, MDBENCH enables the systematic creation of challenging QA examples while addressing data contamination and annotation costs. Our results reveal significant challenges for current models, and our approach facilitates scalable, targeted evaluation of multi-document reasoning capabilities, paving the way for more rigorous evaluation of models’ abilities to handle real-world, multi-source information.

Acknowledgments

This work is supported by Cisco Research. We thank Greg Durrett, Grace Kim, Ramya Namuduri, Liyan Tang and Anisha Gunjal for their insights and valuable discussions. We also thank the reviewers of ICLR and ARR for their thoughtful comments.

Limitations

While MDBENCH presents a novel approach to evaluating multi-document reasoning in large language models, some directions warrant further ex-
ploration: First, we evaluate this method in an automation-focused setting, manually defining just a few foundational reasoning skills and demonstrations as prompts. Future work could explore the trade-offs in adopting more human-centric benchmark processes, such as identifying which pipeline components yield the greatest reduction in human effort while still allowing for dynamic human engagement during generation. Second, while our work has demonstrated the efficacy and potential of knowledge-grounded generation within the Wiki domain—spanning diverse topics such as sports, politics, and science—it is worthwhile to investigate the generalization and adaptability of this method to other, more niche domains, such as law or medicine. These specialized areas may introduce unique challenges due to their inherent complexity. Tabular knowledge may not be readily available or meaningful in such settings, however, it is likely there are other forms of structured knowledge representations that can be consumed in a similar manner. Understanding which prompt and pipeline design choices are best-suited for such targeted domains remains a promising avenue for future research.

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Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Milicicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024. Chain-of-table: Evolving tables in the reasoning chain for table understanding. Preprint, arXiv:2401.04398.

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837.

Wen Xiao, Iz Beltagy, Giuseppe Carenini, and Arman Cohan. 2021. Primera: Pyramid-based masked sentence pre-training for multi-document summarization. arXiv preprint arXiv:2110.08499.

Siheng Xiong, Ali Payani, Ramana Kompella, and Faramarz Fekri. 2024. Large language models can learn temporal reasoning. Preprint, arXiv:2401.06853.

Cheng Xu, Shuhao Guan, Derek Greene, and M-Tahar Kechadi. 2024. Benchmark data contamination of large language models: A survey. Preprint, arXiv:2406.04244.

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP).

Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024. Fanoutqa: A multi-hop, multi-document question answering benchmark for large language models. Preprint, arXiv:2402.14116.

A Multi-document Reasoning Skills Demonstrations

Figures 7, 8, 9, 10, 11 overview the five reasoning skills we demonstrate during the creation of MDBENCH. Figure 6 demonstrates an edit plan provided to inspire the table augmentation.

B Model Evaluation Prompts

Simple QA Prompt

"You will be presented with a question and a context. You should answer the question based on the context. The last thing you generate should be ANSWER:[your answer here]"

Chain-of-thought QA Prompt

"You will be presented with a question and a context. You should answer the question based on the context. Explain your reasoning step by step before you answer. The last thing you generate should be ANSWER:[your answer here]"

C Example Post-processing and Scoring Prompts

These prompts are applied after example construction to (a) normalise the ground-truth format, (b) extract concise model predictions after inference, and (c) assign a scalar correctness score to the processed predictions.
QA Example Formatting Prompt
"Adjust the question and answer format such that the question includes output formatting instructions like “output the answer as a JSON with fields <fields>”, and ensure the answer follows a corresponding JSON structure.
Question: {question}
Answer: {answer}"

Predicted Answer Extraction Prompt
"Take this response (which may include verbose reasoning) and concisely extract only the final answer to the provided question.
Question: {question}
Answer: {model_response}"

Scoring Prompt
"On a scale of 0–10, how consistent is the predicted answer with the ground-truth? Partial credit is acceptable if a portion of the response matches the ground-truth. RETURN ONLY AN INTEGER.
Question: {question}
Predicted Answer: {predicted_answer}
Ground-truth Answer: {ground_truth}"

Validity Prompt 1
Original Table Name: {table_title}
Original Table: {original_table}
Table Edits Applied: {edits_applied}
Resultant Table: {generated_table}
Resultant Question: {generated_question}
Resultant Answer: {generated_answer}

Prompt: I have provided an original table, and then an updated version (using the provided knowledge edits) which resulted in an augmented table with a corresponding new question and answer. Use this context and think step by step to come up with a solution rationale that provides a justification for the answer. Note that the original table + edits are provided mostly for added reference. Output the rationale as a string.

Validity Prompt 2
How consistent/valid is this reasoning in the following process for generating an example from a table? Score the validity and consistency of the resultant table+question+answer on a scale of 0-5. I want to be able to identify and ignore examples with low scores that I shouldn’t include in my dataset. Output as a json with ’score’ and ’explanation’ fields. Here is the example: {prompt_1_output}

D MDBENCH Pipeline Validity Prompts

We use the following prompts during the knowledge augmentation step to validate the edit plan execution and resultant QA example. Prompt 1 works through the generated problem (leveraging the full knowledge augmentation history) and attempts to rationalize the QA example. Then, prompt 2 evaluates whether this rationalization from Prompt 1 is valid and generates a 0-5 validity scalar, discarding values below 5.

E Characteristic Breakdown

Table 6 overviews the overall model performance when binning examples by difficulty for each of the five considered characteristics.
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="2">Aggregation</th>
    <th colspan="2">Multi-hop</th>
    <th colspan="2">Numeric</th>
    <th colspan="2">Soft</th>
    <th colspan="2">Temporal</th>
  </tr>
  <tr>
    <th>E</th>
    <th>H</th>
    <th>E</th>
    <th>H</th>
    <th>E</th>
    <th>H</th>
    <th>E</th>
    <th>H</th>
    <th>E</th>
    <th>H</th>
  </tr>
  <tr>
    <td>Support</td>
    <td>77.0</td>
    <td>130</td>
    <td>89</td>
    <td>211</td>
    <td>124</td>
    <td>176</td>
    <td>255</td>
    <td>45</td>
    <td>208</td>
    <td>92</td>
  </tr>
  <tr>
    <td>LLaMA-3-8B-Instruct</td>
    <td>68.0</td>
    <td>62.6</td>
    <td>73.6</td>
    <td>62.0</td>
    <td>67.1</td>
    <td>64.8</td>
    <td>64.4</td>
    <td>77.0</td>
    <td>71.5</td>
    <td>54.0</td>
  </tr>
  <tr>
    <td>LLaMA-3-70B-Instruct</td>
    <td>77.3</td>
    <td>79.3</td>
    <td>82.5</td>
    <td>75.9</td>
    <td>78.6</td>
    <td>78.0</td>
    <td>76.3</td>
    <td>90.9</td>
    <td>79.5</td>
    <td>75.2</td>
  </tr>
  <tr>
    <td>Claude-3-Opus</td>
    <td>79.3</td>
    <td>79.4</td>
    <td>80.8</td>
    <td>78.5</td>
    <td>81.6</td>
    <td>78.0</td>
    <td>78.1</td>
    <td>88.5</td>
    <td>82.4</td>
    <td>73.5</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>79.3</td>
    <td>76.0</td>
    <td>83.3</td>
    <td>75.7</td>
    <td>85.1</td>
    <td>73.7</td>
    <td>77.3</td>
    <td>84.8</td>
    <td>81.5</td>
    <td>70.6</td>
  </tr>
  <tr>
    <td>Gemini-2.5-Flash</td>
    <td>74.2</td>
    <td>82.4</td>
    <td>76.4</td>
    <td>78.8</td>
    <td>80.8</td>
    <td>74.2</td>
    <td>78.7</td>
    <td>72.5</td>
    <td>80.0</td>
    <td>68.4</td>
  </tr>
  <tr>
    <td>Gemini-1.5-Pro</td>
    <td>78.2</td>
    <td>75.6</td>
    <td>83.9</td>
    <td>74.0</td>
    <td>85.4</td>
    <td>72.3</td>
    <td>75.3</td>
    <td>88.8</td>
    <td>81.1</td>
    <td>68.9</td>
  </tr>
  <tr>
    <td>GPT-3.5-Turbo</td>
    <td>68.6</td>
    <td>72.9</td>
    <td>74.4</td>
    <td>68.6</td>
    <td>72.6</td>
    <td>69.2</td>
    <td>69.5</td>
    <td>75.9</td>
    <td>74.0</td>
    <td>62.9</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>79.1</td>
    <td>79.0</td>
    <td>83.5</td>
    <td>77.0</td>
    <td>84.0</td>
    <td>76.3</td>
    <td>79.1</td>
    <td>78.7</td>
    <td>83.9</td>
    <td>69.0</td>
  </tr>
  <tr>
    <td>GPT-o1</td>
    <td>79.6</td>
    <td>82.9</td>
    <td>80.7</td>
    <td>81.3</td>
    <td>80.3</td>
    <td>82.2</td>
    <td>81.2</td>
    <td>80.4</td>
    <td>81.9</td>
    <td>77.4</td>
  </tr>
  <tr>
    <th>Overall</th>
    <th>76.0</th>
    <th>76.7</th>
    <th>79.9</th>
    <th>74.6</th>
    <th>79.5</th>
    <th>74.3</th>
    <th>75.5</th>
    <th>81.9</th>
    <th>79.5</th>
    <th>68.9</th>
  </tr>
</table>

Table 6: Characteristic-level Performance Breakdown. We report overall accuracy.
Multi-document Dataset Annotation Guidelines

Your task is to evaluate the consistency of generated multi-document question answering examples.

You will be provided with the following for each example:

- A set of related documents (labeled Document 1 - Document N)
    - These contain the source information needed for the problem
- A question
    - This question will rely reasoning over the provided documents.
- An answer
    - You will solve the problem yourself, and compare your answer with the provided answer
- An example UUID
    - You will use this to refer to the example when recording your progress in the tracker spreadsheet

You will record your progress in the tracking spreadsheet. Please contact <redacted> if you run into any issues or have questions.

For each example, follow these steps:

1. Read the Question:
   Carefully read the question to understand what is being asked.
2. Examine the Context:
   Read through the entire provided document set. Understand the scenario, relevant details, and any nuances in the text. This may take a couple passes, and it may be helpful to highlight sections of text as you go, or feel free to take physical notes if this is helpful.
3. Answer the Question:
   Based solely on the context provided, determine what you believe the correct answer should be.
4. Compare Answers:
   Compare your answer with the provided ground-truth answer.
       o If your answer aligns with the provided answer, mark the example as VALID in the tracking sheet.
       o If your answer does not match, or if you find discrepancies indicating an inconsistency or error in the example, mark the example as INVALID in the sheet.
5. Provide Comments:
   If there are any unique observations or trends, please note them in the tracking sheet

Figure 5: MDBench human validation guidelines.
Original Table | Table Summary: Movie Sales by Country

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>screens</th>
    <th>rank</th>
    <th>gross ($)</th>
  </tr>
  <tr>
    <td>october 20 , 2006</td>
    <td>turkey</td>
    <td>378</td>
    <td>1</td>
    <td>146268</td>
  </tr>
  <tr>
    <td>october 25 , 2006</td>
    <td>belgium</td>
    <td>6</td>
    <td>19</td>
    <td>38916</td>
  </tr>
  <tr>
    <td>october 25 , 2006</td>
    <td>germany</td>
    <td>52</td>
    <td>12</td>
    <td>133228</td>
  </tr>
  <tr>
    <td>october 26 , 2006</td>
    <td>austria</td>
    <td>4</td>
    <td>13</td>
    <td>41780</td>
  </tr>
  <tr>
    <td>october 26 , 2006</td>
    <td>netherlands</td>
    <td>17</td>
    <td>14</td>
    <td>53749</td>
  </tr>
  <tr>
    <td>october 27 , 2006</td>
    <td>united kingdom</td>
    <td>4</td>
    <td>24</td>
    <td>34704</td>
  </tr>
</table>

Edit 1: Come up with a interesting question about this table. The question MUST have a concise verifiable answer. The question should go hand in hand with ensuring the augmentation introduces complex cross-row dependencies, as this will be used to create corresponding multi-document examples (one document per row). Make sure that the question + new table can only be answered if the model reasons correctly over documents.
Example: "Rank the movie's sales by country." - requires reasoning/comparing over the different rows in the document. Note: we will edit the table further to make this even more challenging.

Edit 2: Remove extraneous columns to avoid overspecification in the resultant documents
Example: Remove the screens and rank columns since they're not relevant

Edit 3: Round some of the numeric values to eventually make the information more realistic in the articles
Example: Round the gross sales numbers to thousands

Edit 4: Add 'multi-hop' information, or additional rows that necessitate synthesizing information across documents
Example: Add an October 26th entry for Germany for $195k (now there are two rows for Germany) – These need to be added into order to calculate the Germany sales.

Edit 5: Add secondary / peripheral / fictional information to contextualize/personalize the documents
Example: Add a "context" column with some additional guidance to guide the document generation. This should include instructions on document length + writing style as well as superfluous content that might naturally occur in a document of this type. Also add a fictionalized film name (Nightmares of Glory).

Edit 6: Introduce cross-document dependencies by obfuscating some linked information. The dependencies must be utilized within the question generation process:
Example: The Germany Oct. 26th entry was modified. The country information was obfuscated, but the daily revenue was defined in terms of the prior day, allowing the model to refer back to the Oct. 25 row.

Resultant Augmented Table

<table>
  <tr>
    <th>date</th>
    <th>country</th>
    <th>daily revenue ($)</th>
    <th>film</th>
    <th>context</th>
  </tr>
  <tr>
    <td>october 20 , 2006</td>
    <td>turkey</td>
    <td>146200</td>
    <td>Nightmares of Glory</td>
    <td>short article about total movie sales</td>
  </tr>
  <tr>
    <td>october 25 , 2006</td>
    <td>belgium</td>
    <td>39000</td>
    <td>Nightmares of Glory</td>
    <td>article about total movie sales</td>
  </tr>
  <tr>
    <td>october 25 , 2006</td>
    <td>germany</td>
    <td>135000</td>
    <td>Nightmares of Glory</td>
    <td>mid-length article about daily movie sales</td>
  </tr>
  <tr>
    <td>october 26 , 2006</td>
    <td>austria</td>
    <td>42000</td>
    <td>Nightmares of Glory</td>
    <td>article about total movie sales</td>
  </tr>
  <tr>
    <td>october 26 , 2006</td>
    <td>netherlands</td>
    <td>54000</td>
    <td>Nightmares of Glory</td>
    <td>report of national movie sales</td>
  </tr>
  <tr>
    <td>october 27 , 2006</td>
    <td>united kingdom</td>
    <td>34700</td>
    <td>Nightmares of Glory</td>
    <td>article about total movie sales, and interviewing a fictional moviegoer</td>
  </tr>
  <tr>
    <td>october 26 , 2006</td>
    <td>[not explicitly stated]</td>
    <td>195,000; 60,000 more than yesterday's sales</td>
    <td>Nightmares of Glory</td>
    <td>article about total movie sales</td>
  </tr>
</table>

Augmented Table Question: Rank the movie's sales by country.
Augmented Table Answer: Germany (133000+195000), Turkey (146200), Netherlands (54000), Austria (42000), Belgium (39000), United Kingdom (34700)

Figure 6: Demonstration of table edit plan used during the knowledge augmentation component of the MDBENCH pipeline.
[Knowledge Aggregation] – The ability to align, compare and/or contrast knowledge that may be present. This includes non-numeric knowledge.

Baseline Example: Rank the teams by number of wins in the series.

<table>
  <tr>
    <th>race</th>
    <th>pole position</th>
    <th>winning team</th>
  </tr>
  <tr>
    <td>May 7, 1992</td>
    <td>nico valencia</td>
    <td>ferrari</td>
  </tr>
  <tr>
    <td>May 21, 1992</td>
    <td>mark steedman</td>
    <td>bmw</td>
  </tr>
  <tr>
    <td>June 4, 1992</td>
    <td>bonnie bobcat</td>
    <td>mclaren</td>
  </tr>
  <tr>
    <td>June 18, 1992</td>
    <td>ello muchin</td>
    <td>renault</td>
  </tr>
  <tr>
    <td>July 2, 1992</td>
    <td>tammy tiger</td>
    <td>ford</td>
  </tr>
  <tr>
    <td>July 16, 1992</td>
    <td>tyrell eshar</td>
    <td>ferrari</td>
  </tr>
  <tr>
    <td>July 30, 1992</td>
    <td>alain prost</td>
    <td>ferrari</td>
  </tr>
  <tr>
    <td>August 13, 1992</td>
    <td>tigre trees</td>
    <td>renault</td>
  </tr>
</table>

Answer: Ferrari, Renault, and T-3 are BMW, McLaren and Ford.
Answer Rationale: Ferrari was listed as the winning team three times, Renault twice, and the others once each.
Commentary: This is a simple example that required calculating the number of appearances of each team in the ‘winning team’ column.

Harder Example: Identify the top two teams in this race series, and explain any correlation between their success and the weather.

<table>
  <tr>
    <th>race</th>
    <th>pole position</th>
    <th>winning team</th>
    <th>notable conditions</th>
  </tr>
  <tr>
    <td>May 7, 1992</td>
    <td>nico valencia</td>
    <td>ferrari</td>
    <td>sunny + dry</td>
  </tr>
  <tr>
    <td>May 21, 1992</td>
    <td>mark steedman</td>
    <td>bmw</td>
    <td>rainy</td>
  </tr>
  <tr>
    <td>June 4, 1992</td>
    <td>bonnie bobcat</td>
    <td>mclaren</td>
    <td>heavy rain</td>
  </tr>
  <tr>
    <td>June 15, 1992</td>
    <td>ello muchin</td>
    <td>renault</td>
    <td>slick roads</td>
  </tr>
  <tr>
    <td>July 2, 1992</td>
    <td>tammy tiger</td>
    <td>ford</td>
    <td>cold and blustery</td>
  </tr>
  <tr>
    <td>July 16, 1992</td>
    <td>tyrell eshar</td>
    <td>ferrari</td>
    <td>sunny</td>
  </tr>
  <tr>
    <td>July 30, 1992</td>
    <td>alain prost</td>
    <td>ferrari</td>
    <td>overcast</td>
  </tr>
  <tr>
    <td>August 13, 1992</td>
    <td>tigre trees</td>
    <td>renault</td>
    <td>damp</td>
  </tr>
</table>

Answer: Ferrari finished first and Renault finished second. Ferrari’s wins were exclusively in conditions with dry pavement, whereas Renault won only in wet conditions.
Answer Rationale: Ferrari had three wins, and Renault had two wins. The rest of the teams had only one. Notably, Ferrari winning races were only in conditions where the roads were presumably dry (sunny+dry, sunny, and overcast), and Renault’s wins were only on day where the conditions were wet (slick roads, and damp).
Commentary: This answer requires not only understanding the winning teams, but also realizing that there were patterns in the conditions for both teams. Namely, one had to ascertain that Ferrari performed well on dry days, whereas Renault did well on wet roads. This requires aggregating, comparing, and contrasting values across different rows and teams.

Figure 7: Knowledge Aggregation Skill Description
[Multi-hop Reasoning] – The ability to solve problems requiring multiple steps to arrive at the solution.

Baseline Example: Which country had the most showings and how many was this in total?

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
</table>

Answer: Belgium had the most with 600 showings.
Answer rationale: Turkey had 200 showings and Belgium had 600. 600 > 200, therefore Turkey had the most showings.
Commentary: This is a simple reasoning process as it requires a simple comparison of two values with no additional reasoning required.

Harder Example: Which country had the most showings and how many was this in total?

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
  <tr>
    <td>october 25, 2006</td>
    <td>turkey</td>
    <td>500</td>
  </tr>
</table>

Answer: Turkey had the most with 700 showings.
Answer Rationale: Turkey had showings on two different days, so the total is 200+500=700 showings. 700 > Belgium’s 600, therefore Turkey had the most.
Commentary: By adding a new row with complementary information, we necessitate an additional reasoning hop to correctly answer the question. Note that this table was edited specifically such that the answer (Turkey) is flipped from the original answer (Belgium) in the simple example. Edits like these ensure the reasoning cannot be shortcutted (e.g., by simply selecting the row with the highest showings).

Figure 8: Multi-hop Reasoning Skill Description
[Numeric Reasoning] – The ability to handle numeric values and perform numerical operations

Baseline Example: Rank each day by the total showings.

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>turkey</td>
    <td>400</td>
  </tr>
  <tr>
    <td>november 22, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
</table>

Answer: November 21st had the most showings with 1000, followed by November 22nd, then October 20th.

Answer Rationale: November 21st had 1000 totals showings – 600 in Belgium and 400 in Turkey. This was greater than the 600 on November 22nd and the 200 on October 20th.

Commentary: This is a simple case of performing numeric operations, having to sum values over different rows to identify the correct answer.

Harder Example: Rank each day by the total sales

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
    <th>Avg. sales per showing ($)</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
    <td>6000</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>belgium</td>
    <td>600</td>
    <td>1000</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>turkey</td>
    <td>400</td>
    <td>1000</td>
  </tr>
  <tr>
    <td>november 22, 2006</td>
    <td>belgium</td>
    <td>600</td>
    <td>500</td>
  </tr>
</table>

Answer: October 20th had the highest sales, followed by November 21st, then November 22nd

Answer Rationale: October 20 had 200 showings * $6000 per showing = $1,200,000. November had 600*$1000 = $600,000 from Belgium and 400*$1000 = $400,000 from Turkey, totalling $1,000,000. November 22 had 600 * $500 = $300,000 in sales.

Commentary: This reasoning requires calculating values over two different columns, and then additionally summing values over associated rows (e.g. the november 21 entries).

Figure 9: Numeric Reasoning Skill Description
[Soft Reasoning] – The ability to reason abductively and make informed decision in cases where some uncertainty or fuzziness may be present.

Simple Example: Who had the most championships?

<table>
  <tr>
    <th>Year</th>
    <th>Championship Winner</th>
  </tr>
  <tr>
    <td>2008</td>
    <td>Yusef</td>
  </tr>
  <tr>
    <td>2009</td>
    <td>Mattingly</td>
  </tr>
  <tr>
    <td>2010</td>
    <td>Tigre Trees</td>
  </tr>
  <tr>
    <td>2011</td>
    <td>Yusef "Skeeps" Mattingly</td>
  </tr>
  <tr>
    <td>2012</td>
    <td>Tigre</td>
  </tr>
  <tr>
    <td>2013</td>
    <td>John Smith</td>
  </tr>
  <tr>
    <td>2014</td>
    <td>John Smith</td>
  </tr>
  <tr>
    <td>2015</td>
    <td>Harrison Chevrolet</td>
  </tr>
</table>

Answer: Yusef Mattingly, who had wins in 2008, 2009, and 2011

Answer Rationale: Although not clearly stated, some of the entries likely refer to the same person, just sometimes using only the first name, last name, or a nickname. We can reasonably assume 'Yusef', 'Mattingly', and 'Yusef 'Skeeps' Mattingly' all refer to the same individual. Similarly, we see both a 'Tigre Trees' and 'Tigre' which likely refer to the same individual.
Commentary: This is an example abductive or 'best guess' soft reasoning where one could reasonably assume that some of the entries refer to the same canonical entity/person. Notably, this example is one where a wrong answer would be generated by using a simple exact match heuristic as 'John Smith' appears twice, which is less than Yusef Mattingly.

Harder Example: Rank the countries by total sales.

<table>
  <tr>
    <th>Country</th>
    <th>Sales ($)</th>
    <th>Notes</th>
  </tr>
  <tr>
    <td>October 20 Turkey</td>
    <td>146200</td>
    <td></td>
  </tr>
  <tr>
    <td>October 25 Belgium</td>
    <td>39000</td>
    <td></td>
  </tr>
  <tr>
    <td>October 25 Germany</td>
    <td>134000</td>
    <td></td>
  </tr>
  <tr>
    <td>October 26 Austria</td>
    <td>42000</td>
    <td></td>
  </tr>
  <tr>
    <td>October 26 Netherlands</td>
    <td>54000</td>
    <td></td>
  </tr>
  <tr>
    <td>October 27 United Kingdom</td>
    <td>534700</td>
    <td></td>
  </tr>
  <tr>
    <td><one that was already October 26 mentioned></td>
    <td>195000, roughly 60k more than yesterday's sales.</td>
    <td>A follow-up to a prior entry</td>
  </tr>
</table>

Answer: United Kingdom, Germany, Turkey, Netherlands, Austria, Belgium

Answer Rationale: Most country sales are confined to just one row. However, the final row contains sales information that implicitly refers to a country. We see that this country is already mentioned and that this row is a follow-up to a previous entry with sales numbers. The sales value is $195,000 which is stated as 60k more than the prior day sales. We can use this to ascertain what the country is. Namely, we see that there are two entries for the prior day (October 25). Of these two, Germany's sales were $134,000 which is approximately $60,000 less than $195,000. Belgium's sales were much lower (over $150k less than $195,000). Therefore, we can reasonably conclude that the October 26 entry in mention refers to Germany. Combining the $134,000 from October 25 and $195,000 from October 26, we see Germany's total sales are $329,000, which is less than the United Kingdom, but more than Turkey.
Commentary: This problem requires that one notices that the final row can be linked to a prior row. Once this is done, there is some soft reasoning that clearly leads to the proper solution. So, while there is some abduction reasoning required, it is very clear once you put the pieces together.

Figure 10: Soft Reasoning Skill Description
[Temporal Reasoning] – The ability to handle temporal information and dependencies.

Baseline Example: How many total showings were there in each month?

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>turkey</td>
    <td>400</td>
  </tr>
  <tr>
    <td>november 22, 2006</td>
    <td>belgium</td>
    <td>600</td>
  </tr>
</table>

Answer: October 2006 had 200 showings, while November had 1,600
Answer Rationale: October had just one day with 200 showings. November had 3 showings total, summing to 600+400+600 showings total.
Commentary: This is fairly straightforward as we simply sum all rows sharing the same month.

Harder Example: How many total showings were there in each month?

<table>
  <tr>
    <th>date</th>
    <th>territory</th>
    <th>showings</th>
    <th>notes</th>
  </tr>
  <tr>
    <td>october 20, 2006</td>
    <td>turkey</td>
    <td>200</td>
    <td>Opening day in Turkey</td>
  </tr>
  <tr>
    <td>november 21, 2006</td>
    <td>belgium</td>
    <td>600</td>
    <td>Opening day in Belgium</td>
  </tr>
  <tr>
    <td>the week after opening day</td>
    <td>turkey</td>
    <td>400</td>
    <td></td>
  </tr>
  <tr>
    <td>november 23, 2006</td>
    <td>belgium</td>
    <td>600</td>
    <td></td>
  </tr>
</table>

Answer: October 2006 had 600 showings, while November had 1,200
Answer Rationale: In Turkey, the week after opening day fell in the month of October, therefore there were 200 (from opening day) + 400 (from the week after) = 600 showings in October. November had 500+600 = 1,200 showings, all from Belgium.
Commentary: We introduce a cross-row dependency here that requires temporal reasoning to solve. Namely, we need to intuit that, given opening day is on October 20th, the week immediately following it must fall within the month of October. Again, we intentionally edit the values in the table (and add a 'notes' column) to ensure that the answer (600 in October, 1200 in November) necessarily required resolving this cross-row dependency.

Figure 11: Temporal Reasoning Skill Description
Question: Each article refers to a different game in the running series. Which team won the series and which games did they win?
Answer: Buffalo won the series, winning games 1, 3, 6, 7. Ottawa won games 2, 4, an 5.

Document 1:
The chill of the arena was a stark contrast to the warmth that had begun to grace the city outside. Inside, the tension was palpable as the crowd’s anticipation hung heavily in the air. The local team had been on a losing streak, but tonight there was a spark in their eyes that hadn’t been seen in weeks. The arena was a sea of blue and gold, with a smattering of red among the stands, a small but loyal contingent that had made the trip to support their visiting team. Emma and her brother, Michael, had been looking forward to this game all season. They had grown up in a household divided between loyalty to Buffalo and affection for Ottawa, a dynamic that invigorated their sibling rivalry. As the clock ticked down the final minutes, Michael’s grip tightened on the edge of his seat while Emma’s cheers grew more fervent. The score had been stagnant since the second period when the home team had managed a slick goal that sent the crowd into a frenzy, a play that was now looking like it might secure the game. As the final buzzer sounded, the arena erupted, the home fans’ relief and joy mingling in a deafening roar. Buffalo had managed to hold onto their slender lead, securing a much-needed victory. Emma’s face was lit with a triumphant smile as she turned to her brother, whose disappointment was evident, though he managed a good-natured shrug. They filed out with the rest of the crowd, the crisp night air doing little to cool the elation of the home fans or the resigned contemplation of the visitors. For Emma, the walk home was going to be sweet, her team’s victory a happy echo in her step, while Michael was already looking ahead, optimistic that the next game might tell a different story.

Document 2:
The city of Buffalo was alive with the buzz of anticipation as fans draped in blue and gold swarmed the downtown area, their spirits undampened by the brisk spring breeze. The local cafes and pubs were abuzz with predictions and friendly banter, while the streets echoed with the sounds of camaraderie. It was, after all, a night marked by the ritual of hockey, a sacred time when the community came together, united by their love for the Sabres. In one particular home, nestled in a cozy neighborhood where the scent of lilacs began to mingle with the crisp air, the Larson family gathered in their living room, adorned with memorabilia of games past. The youngest, Lily, wore her oversized jersey with pride, the number of her favorite player still crisp against the fabric. Her father, Michael, a man with a kind face and a voice worn from years cheering from the stands, was setting up the snacks on the coffee table with a quiet intensity. They had skipped the live game tonight, a rare occurrence, but Michael’s work had demanded his presence later than usual. The game was to be a bonding moment; the television their window to the thrill. As the match commenced, the Larsons were perched at the edge of their seats, reacting to each play with a symphony of gasps, cheers, and the occasional groan. The evening waned and the tension within the four walls grew thick, mirroring the energy of the arena miles away. There was a magic to watching the game unfold from the comfort of home, a different kind of ritual observed by families like the Larsons, who lived and breathed by the puck’s dance across the ice. When the final buzzer sounded, it was met with a mixture of emotions; the disappointment of a home defeat was palpable, yet there was a sense of pride in the hard-fought battle. They spoke of plays and strategy, of what might have been, Michael’s arm draped over Lily’s shoulder in a silent promise of next time. The game may have slipped away, but the spirit of it lingered in the hearts of the Buffalo faithful.

Document 3:
As the sun dipped below the horizon, painting the sky in hues of orange and purple, the city of Ottawa buzzed with an electric anticipation. It was more than just the changing seasons that had the locals in a state of excitement; it was the clash on the ice that had everyone talking. The arena was packed, the air thick with the scent of popcorn and the resonant echoes of cheering fans. In the heart of it all, Jeremy and his father, clad in their beloved team’s colors, watched with bated breath as the players darted across the rink. It was a dance of power and precision, a tug of war for dominance that had them on the edge of their seats. The visitors, despite being on foreign ice, played with a relentless drive, their determination as palpable as the chill that hung in the air. The score was close, and each goal was met with a roar that reverberated off the walls, a testament to the passion that filled the space. The final minutes were a blur of motion and emotion. Jeremy’s heart raced as he gripped his father’s hand, both of them silently praying for a miracle. But as the clock dwindled down, it was clear that the visitors had edged out their opponents. The final buzzer sounded, a solemn toll that marked the end of the battle. Disappointment was heavy in the Ottawa crowd as they streamed out of the arena, a stark contrast to the jubilation of the few visiting fans who celebrated their hard-fought victory. On the ride home, Jeremy’s father reminded him that this was the nature of the game—sometimes you win, sometimes you learn. And tonight, they had learned.

Figure 12: MDBENCH Document QA Example – Part 1
Document 4:
The city was abuzz with the excitement of the game as the evening approached. Downtown streets hummed with the chatter of fans clad in their team’s colors, a sea of red and black flowing towards the arena. Inside the local pubs, patrons argued over potential plays and key players, their voices rising over the clinking of glasses and the low hum of pre-game broadcasts. Among them, Sarah and Jeff found a cozy corner booth, their eyes locked in a friendly but fierce debate about the night’s outcomes. The arena itself was a cacophony of anticipation, with every seat filled and the air electric. The first period passed in a blur of motion, the crowd’s roar ebbing and flowing with each close call and power play. Despite the visitors’ valiant effort, their offense just couldn’t break through the impenetrable defense they faced. Sarah, a hometown girl born and raised with a love for her city’s team, clutched Jeff’s hand tightly as the final minutes ticked down. Jeff, who had moved to the city years ago, had adopted her team as his own, their shared passion for the sport one of the many threads that had woven their lives together. As the final buzzer sounded, the arena erupted, the scoreboard confirming the hard-fought victory. Sarah leaped to her feet, cheers mingling with the thousands around her, as she and Jeff celebrated the triumph. The players on the ice embraced, their faces a mixture of exhaustion and elation. It was a moment of pure joy for the home team, a narrow win that would be recounted in the history of the season. Outside, the night air was crisp, but the warmth of victory and the shared experience seemed to make the atmosphere glow. They walked home, their path lit by the city lights and the lingering excitement of a game that would be etched into the collective memory of the fans, a one-goal difference that had meant everything.

Document 5:
The early spring evening held a crisp chill that settled over the city of Buffalo, a reminder that winter’s grip had only recently loosened. In the heart of downtown, a palpable excitement coursed through the crowd as they streamed towards the arena, their breaths visible in the twilight. The local fans, clad in blue and gold, were a sea of hopeful energy, ready to cheer on their beloved team. Inside the arena, the tension was almost tangible. The players on the home bench tapped their sticks anxiously, exchanging glances that were a mix of determination and nerves. The visiting team from Ottawa, undeterred by the raucous environment, seemed poised and focused during the warm-up, their red jerseys stark against the ice. The game commenced with the drop of the puck, and the home crowd’s roar reached a fever pitch, echoing off the rafters. As the final buzzer sounded, a hush fell over the Buffalo fans, their earlier enthusiasm replaced by a respectful silence. The scoreboard, an unflinching digital adjudicator, displayed the outcome that had unfolded over the past few hours. The visitors had demonstrated a formidable defense and an offense that capitalized on every opportunity. It was clear to everyone leaving the arena that evening that the team from Ottawa had earned their win on foreign ice, besting their hosts with a strategy that was as effective as it was elegant. Later that night, as the Ottawa players boarded their bus, a sense of camaraderie enveloped them. This victory was a testament to their hard work and a crucial addition to their season’s record. Meanwhile, the Buffalo team, though disheartened, knew that the season was far from over. There would be other games, other chances to prove themselves, and the unwavering support of their fans would be there every step of the way.

Document 6:
The air was thick with anticipation as the evening drew in over the capital city. The local arena, a coliseum of modern spectacle, was abuzz as fans donned in their team’s colors made their pilgrimage to witness the clash on ice. Vendors hawked memorabilia, the scent of stadium food filled the air, and the collective heartbeat of the city seemed to pulse in time with the approaching faceoff. In the stands, there was a sea of red and black, punctuated by the occasional bold stripe of blue and gold. Families, friends, and die-hard enthusiasts shared in the camaraderie that only such a game could inspire. The home team had been struggling this season, but hope springs eternal, and there was a sense that tonight could mark a turning point. After all, it was a perfect evening for an underdog story. On the bench, the visiting team was a study in focus. Their strategy was clear, their execution clinical, and as the puck dropped, it became evident that they were not here to play into a narrative of redemption. The home team fought valiantly, their goalie making save after save, but as the final buzzer sounded, it was the silence of the home crowd that spoke volumes. The scoreboard, unyielding, displayed the tale of the tape, a testament to the visitor’s triumph in the city of Ottawa that night.

Document 7:
As the evening sun dipped below the horizon, painting the sky in hues of lavender and orange, the city of Buffalo was alive with a palpable energy. It was a day marked in every local’s calendar, a moment when hometown pride swelled in the hearts of the residents. The streets around the arena buzzed with anticipation, vendors calling out over the chatter, selling memorabilia and hot dogs to the sea of blue and gold. Inside the arena, you could cut the tension with a knife. The game was a nail-biter, with the crowd erupting into cheers and groans as the puck darted across the ice, a blur of potential victory or defeat. The players were relentless, sweat glistening on their brows as they fought for every inch, every opportunity to tip the scales. As the final buzzer sounded, the roar from the stands was deafening, a wave of elation that swept through every aisle and seat. The home team had snatched triumph from the jaws of a well-matched adversary, a testament to their resolve and spirit, much to the chagrin of the visitors who had hoped to silence the boisterous Buffalo fans. Later, as the night claimed the sky and the arena emptied, the streets of Buffalo remained abuzz. Families and friends recounted the game’s most thrilling moments, reliving each goal with animated gestures and wide smiles. The city’s iconic eateries filled with patrons, their conversations a mix of play-by-play analyses and plans for the next game. It was a victory that would be talked about for days to come, another storied chapter in the city’s rich sports history, a testament to the heart and hustle that defined Buffalo not just on the ice, but in every aspect of its character.*

Figure 13: MDBENCH Document QA Example – Part 2

Reviewer paper summaries: This paper introduces MDBENCH, a new benchmark dataset for evaluating large language models' multi-document reasoning capabilities. It uses tabular data as seed knowledge and employs LLM-assisted knowledge augmentation and natural language generation to create multi-document QA examples with complex reasoning dependencies. Even state-of-the-art language models achieve only about 59% accuracy on this benchmark, demonstrating that multi-document reasoning remains a challenging task.

This paper proposes MDBench, an LLM-synthesized benchmark for multi-document reasoning. Based on tabular data, a pipeline is designed to simulate five types of reasoning questions and prompt an LLM to generate the documents step-by-step. Experiment shows that MDBench is challenging for both commercial LLMs and llama 3 models.

The paper introduces MDBench, a novel synthetic benchmark designed for evaluating large language models (LLMs) on multi-document reasoning tasks. It addresses limitations in existing benchmarks, such as reliance on static, human-curated datasets and susceptibility to data contamination, by leveraging a structured, automated generation pipeline.
Summary of strengths: 1. The paper introduces an approach to benchmark creation related to multi-document reasoning evaluation. This data creation methodology transforms structured knowledge into natural language examples while preserving complex reasoning dependencies.

2. The paper reveals significant gaps in current state-of-the-art models' multi-document reasoning capabilities, showing that even the best models achieve only modest performance (~59% accuracy). These results provide valuable insights into how different model sizes and architectures handle various reasoning challenges.

1. This paper shows the potential of using long-horizon reasoning with LLMs to generate questions end-to-end through “edit plans”. Traditionally, LLMs were more generally employed at a specific stage, such as document generation. 
2. A number of LLMs are exhaustively benchmarked, which forms a good baseline for future methods.

1. Introduces a scalable, LLM-assisted pipeline for generating synthetic benchmarks, enabling efficient and controllable creation of diverse multi-document reasoning datasets.

2. MDBench directly addresses the scarcity of robust evaluation benchmarks for multi-document reasoning, a growing challenge given LLMs' expanding capabilities.

3. Provides detailed performance evaluations across prominent LLMs, offering actionable insights into their limitations and strengths in handling complex reasoning tasks.
Summary of weaknesses: 1. While the paper describes a quality control process, there could be concerns about how well synthetic examples capture real-world multi-document reasoning challenges. The paper validates 300 examples manually but relies on automatic validation for 700 others. More extensive analysis of how these synthetic examples compare to naturally occurring multi-document reasoning scenarios would strengthen the work.

2. The benchmark is relatively small compared to some existing datasets (1,000 examples). The paper could better justify why this scale is sufficient

3. More open-ended QA metrics should be used. Currently, the authors only use GPT-4o as a reference-based scorer which is not sufficient.

1. The entire benchmark is generated by LLMs. While it is sensible to use LLMs to convert tabular data into documents, using them to propose questions harms the work’s contribution. One limitation MDBench tries to address is data contamination (line 62). However, LLMs copy the question patterns from existing dataset in its training data as well. 
2. As only one (line 379) human annotator is involved, the quality guarantee of the benchmark could be concerning. That said, as a human annotator is involved (line 379), more details should be given about the human annotation (annotator background, training, instruction, compensation, IRB approval etc.). This paper also should have an ethics statement section since it is releasing a new resource and uses human annotation. The response to checklist part D is inadequate. 
3. The input context length is very short compared to recent benchmarks for multi-document or long-context reasoning (such as [1]). To increase its difficulty, is it possible to extend the context length of MDBench by adding more entries to the tabular data?
4. The writing is not very clear. This paper would benefit from a problem formulation section that describes the problem setup, input, output, evaluation in detail. It is also hard to understand each type of question without an example in the main text. 

References

[1] LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks. Bai et al., 2024.

- The pipeline's manual validation step introduces scalability challenges, reducing its practicality for large-scale applications.
- Heavy reliance on LLMs for dataset generation risks bias or contamination, potentially undermining the benchmark's robustness.
Comments, suggestions and typos: 1. Given that this benchmark is constructed from tabular data, why not evaluate some table-based reasoning methods?

It is unclear what Table 1’s columns mean. Also, this table is not referenced anywhere in the text.

1. The benchmark focuses heavily on Wikipedia-derived data, discuss its applicability to niche or domain-specific contexts (e.g., law, medicine).
2. Conduct ablation studies to evaluate the individual contributions of the augmentation steps to the overall dataset quality.
Overall assessment scores: 2

2

4
Metareview score: 4
Metareview: This paper introduces MDBench, a novel LLM-synthesized (w/ human validation for the test set) benchmark for multi-document reasoning.
Summary of reasons to publish: MDBench addresses the scarcity of evaluation benchmarks for multi-document reasoning.

The paper introduces a scalable, LLM-assisted pipeline for generating synthetic benchmarks, enabling efficient and controllable creation of diverse multi-document reasoning datasets

The paper reveals significant gaps in current state-of-the-art models' multi-document reasoning capabilities, showing that even the best models achieve only modest performance.