A Comprehensive Dataset for Verifying Real-World Numerical Claims

Core Concepts

NUMTEMP is a large, diverse dataset of 15,514 real-world numerical claims from various fact-checking domains, designed to evaluate the challenges of verifying claims involving numerical quantities and temporal expressions.

Abstract

The NUMTEMP dataset is a comprehensive collection of 15,514 real-world numerical claims from various fact-checking domains. The dataset addresses the challenge of verifying claims involving numerical quantities and temporal expressions, which are prevalent in political discourse but often overlooked by existing fact-checking datasets.
The dataset construction process involves:

Collecting real-world claims from 45 fact-checking websites worldwide.
Identifying quantitative segments in the claims to extract numerical claims.
Collecting evidence for the claims from web sources, excluding fact-checking websites to avoid leakage.
Enhancing the evidence diversity by using claim decomposition approaches like CLAIMDECOMP and PROGRAM-FC.

The dataset is divided into training, validation, and test sets, with a distribution of 'True', 'False', and 'Conflicting' claims. The authors also categorize the numerical claims into four types: temporal, statistical, interval, and comparison.
The authors evaluate various fact-checking approaches on the NUMTEMP dataset, including claim decomposition, pre-trained models for numerical understanding, and different NLI models. The results show that NUMTEMP poses a significant challenge for fact-checking, with the best approach achieving a weighted-F1 of 64.89 for unified evidence and 69.79 for gold evidence. The authors also find that claim decomposition and models pre-trained on numerical understanding tasks can improve performance on numerical claims.

Stats

Only 8 million households with incomes up to $86k face tax increases under the GOP plan, not all families making $86k.
6.5% of the 122 million households in the bottom three quintiles will face a tax increase under the GOP plan.

Quotes

"Numerical claims are a significant component of political discourse. For instance, our analysis of the CLAIMBUSTER DATASET (Hassan et al., 2017) reveals that a substantial 36% of all check-worthy claims in U.S. presidential debates involve numerical quantities or temporal expressions."
"Numerical claims verification poses a unique challenge, where a fact-checking system must critically analyze and reason about the numerical data presented in both the claim and its evidence."

Key Insights Distilled From

NUMTEMP

by Venktesh V,A... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17169.pdf

Deeper Inquiries

How can the dataset be extended to include more diverse types of numerical claims beyond the four categories identified?

The NUMTEMP dataset could be further extended to include a more diverse range of numerical claims by considering the following approaches:

Expanding Claim Sources: The current dataset is primarily sourced from fact-checking websites. Expanding the claim collection to other domains, such as social media, news articles, and government reports, could introduce a wider variety of numerical claims beyond the four identified categories.

Targeted Claim Elicitation: Researchers could work with domain experts to identify specific types of numerical claims that are underrepresented in the current dataset, such as claims related to scientific findings, financial data, or demographic statistics. These claims could then be actively solicited and included in the dataset.

Automated Claim Generation: While the focus of NUMTEMP is on real-world claims, synthetic claim generation techniques could be employed to supplement the dataset with additional numerical claims. This could involve techniques like template-based generation or language model-based paraphrasing, ensuring the generated claims maintain the complexity and diversity of real-world numerical claims.

Incorporating Temporal Aspects: The current dataset includes a "temporal" category, but the temporal dimension could be further explored by including claims that involve more complex temporal relationships, such as trends, projections, or comparisons across different time periods.

Expanding Numerical Aspects: The four identified categories (temporal, statistical, interval, and comparison) could be expanded to include other numerical aspects, such as claims involving percentages, ratios, or complex mathematical operations.

By adopting these strategies, the NUMTEMP dataset could be extended to encompass a more comprehensive range of numerical claims, better reflecting the diverse nature of real-world numerical information and the challenges it poses for automated fact-checking systems.

What are the potential biases in the evidence corpus collected from web searches, and how can they be mitigated?

The evidence corpus collected from web searches for the NUMTEMP dataset may be subject to several potential biases, which should be considered and mitigated:

Source Bias: The web search results may be skewed towards certain types of websites or domains, such as news media, government sources, or popular online platforms. This could lead to an overrepresentation of certain perspectives or information sources, potentially overlooking relevant evidence from less prominent or specialized websites.
Mitigation: Implement more diverse and balanced web crawling strategies, targeting a wider range of website types and domains, to ensure a more representative evidence corpus.

Temporal Bias: The publication dates of the web pages retrieved may not align with the claim's original publication, leading to potential temporal leakage, where the evidence is published after the claim was made.
Mitigation: Incorporate techniques to filter out evidence that was published after the claim, such as those proposed by Schlichtkrull et al. (2023), to ensure the evidence predates the claim.

Language and Geographical Bias: The web search results may be biased towards content in certain languages or from specific geographical regions, depending on the search engine's algorithms and the user's location.
Mitigation: Expand the web crawling to include a more diverse set of languages and geographical regions, or implement techniques to detect and mitigate language and geographical biases in the evidence corpus.

Credibility Bias: The web search results may be biased towards more popular or well-known websites, which may not necessarily be the most credible or authoritative sources of information.
Mitigation: Develop methods to assess the credibility and reliability of the evidence sources, potentially incorporating techniques from the field of web credibility assessment, and use this information to balance the evidence corpus.

Algorithmic Bias: The web search algorithms used to retrieve the evidence may be subject to inherent biases, such as personalization or optimization for certain types of queries or user preferences.
Mitigation: Experiment with different search engine APIs or techniques, and analyze the potential biases introduced by the search algorithms. Explore ways to mitigate these biases, such as using multiple search engines or implementing custom search strategies.

By acknowledging and addressing these potential biases in the evidence corpus, the NUMTEMP dataset can be further strengthened, ensuring a more diverse, representative, and reliable set of evidence to support the verification of numerical claims.

How can the numerical reasoning capabilities of language models be further improved to enhance their performance on numerical claim verification tasks?

To enhance the numerical reasoning capabilities of language models and improve their performance on numerical claim verification tasks, the following approaches can be explored:

Targeted Pre-training on Numerical Data: Existing language models can be further pre-trained on large-scale datasets that focus on numerical information, such as financial reports, scientific publications, or datasets like FINQA (Zhang and Moshfeghi, 2022). This pre-training can help the models develop a stronger understanding of numerical concepts, operations, and reasoning patterns.

Numerical Reasoning Modules: Language models can be augmented with specialized numerical reasoning modules that can be seamlessly integrated into the model architecture. These modules could be designed to handle various numerical operations, such as arithmetic, comparisons, and unit conversions, and could be trained on datasets that require explicit numerical reasoning.

Multi-task Learning: Language models can be trained on a diverse set of tasks that involve numerical reasoning, such as numerical question answering, numerical entailment, and numerical claim verification. By learning to solve these tasks simultaneously, the models can develop a more robust and generalizable understanding of numerical concepts.

Symbolic Reasoning Integration: Incorporating symbolic reasoning capabilities into language models can help improve their numerical reasoning abilities. This could involve integrating neural-symbolic approaches that combine the strengths of neural networks and symbolic reasoning systems, enabling the models to perform more precise and interpretable numerical computations.

Attention Mechanisms for Numerical Reasoning: Enhancing the attention mechanisms within language models to better focus on numerical entities, operations, and relationships can help the models better understand and reason about the numerical aspects of claims and evidence.

Numerical Prompting and In-context Learning: Developing effective prompting strategies and in-context learning techniques that explicitly guide language models to engage in numerical reasoning can help them better apply their numerical understanding to the task of claim verification.

Numerical Evaluation Benchmarks: Expanding the NUMTEMP dataset and creating additional numerical reasoning benchmarks can help drive the development of more robust and capable numerical reasoning models. These benchmarks should cover a diverse range of numerical claim types and reasoning requirements.

By pursuing these approaches, the numerical reasoning capabilities of language models can be significantly improved, leading to enhanced performance on numerical claim verification tasks and a better understanding of the complex numerical information present in real-world claims.