@inproceedings{tan-etal-2024-enhancing-fact,
title = "Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning",
author = "Tan, Fiona Anting and
Desai, Jay and
Sengamedu, Srinivasan H.",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.20",
pages = "151--169",
abstract = "The ability to extract and verify factual information from free-form text is critical in an era where vast amounts of unstructured data are available, yet unreliable sources abound. This paper focuses on enhancing causal deductive reasoning, a key component of factual verification, through the lens of accident investigation, where determining the probable causes of events is paramount. Deductive reasoning refers to the task of drawing conclusions based on a premise. While some deductive reasoning benchmarks exist, none focus on causal deductive reasoning and are from real-world applications. Recently, large language models (LLMs) used with prompt engineering techniques like retrieval-augmented generation (RAG) have demonstrated remarkable performance across various natural language processing benchmarks. However, adapting these techniques to handle scenarios with no knowledge bases and to different data structures, such as graphs, remains an ongoing challenge. In our study, we introduce a novel framework leveraging LLMs{'} decent ability to detect and infer causal relations to construct a causal Knowledge Graph (KG) which represents knowledge that the LLM recognizes. Additionally, we propose a RoBERTa-based Transformer Graph Neural Network (RoTG) specifically designed to select relevant nodes within this KG. Integrating RoTG-retrieved causal chains into prompts effectively enhances LLM performance, demonstrating usefulness of our approach in advancing LLMs{'} causal deductive reasoning capabilities.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tan-etal-2024-enhancing-fact">
<titleInfo>
<title>Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Fiona</namePart>
<namePart type="given">Anting</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jay</namePart>
<namePart type="family">Desai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Srinivasan</namePart>
<namePart type="given">H</namePart>
<namePart type="family">Sengamedu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Schlichtkrull</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulong</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chenxi</namePart>
<namePart type="family">Whitehouse</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhenyun</namePart>
<namePart type="family">Deng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mubashara</namePart>
<namePart type="family">Akhtar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rami</namePart>
<namePart type="family">Aly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhijiang</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oana</namePart>
<namePart type="family">Cocarascu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arpit</namePart>
<namePart type="family">Mittal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">James</namePart>
<namePart type="family">Thorne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">Vlachos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Miami, Florida, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The ability to extract and verify factual information from free-form text is critical in an era where vast amounts of unstructured data are available, yet unreliable sources abound. This paper focuses on enhancing causal deductive reasoning, a key component of factual verification, through the lens of accident investigation, where determining the probable causes of events is paramount. Deductive reasoning refers to the task of drawing conclusions based on a premise. While some deductive reasoning benchmarks exist, none focus on causal deductive reasoning and are from real-world applications. Recently, large language models (LLMs) used with prompt engineering techniques like retrieval-augmented generation (RAG) have demonstrated remarkable performance across various natural language processing benchmarks. However, adapting these techniques to handle scenarios with no knowledge bases and to different data structures, such as graphs, remains an ongoing challenge. In our study, we introduce a novel framework leveraging LLMs’ decent ability to detect and infer causal relations to construct a causal Knowledge Graph (KG) which represents knowledge that the LLM recognizes. Additionally, we propose a RoBERTa-based Transformer Graph Neural Network (RoTG) specifically designed to select relevant nodes within this KG. Integrating RoTG-retrieved causal chains into prompts effectively enhances LLM performance, demonstrating usefulness of our approach in advancing LLMs’ causal deductive reasoning capabilities.</abstract>
<identifier type="citekey">tan-etal-2024-enhancing-fact</identifier>
<location>
<url>https://aclanthology.org/2024.fever-1.20</url>
</location>
<part>
<date>2024-11</date>
<extent unit="page">
<start>151</start>
<end>169</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning
%A Tan, Fiona Anting
%A Desai, Jay
%A Sengamedu, Srinivasan H.
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F tan-etal-2024-enhancing-fact
%X The ability to extract and verify factual information from free-form text is critical in an era where vast amounts of unstructured data are available, yet unreliable sources abound. This paper focuses on enhancing causal deductive reasoning, a key component of factual verification, through the lens of accident investigation, where determining the probable causes of events is paramount. Deductive reasoning refers to the task of drawing conclusions based on a premise. While some deductive reasoning benchmarks exist, none focus on causal deductive reasoning and are from real-world applications. Recently, large language models (LLMs) used with prompt engineering techniques like retrieval-augmented generation (RAG) have demonstrated remarkable performance across various natural language processing benchmarks. However, adapting these techniques to handle scenarios with no knowledge bases and to different data structures, such as graphs, remains an ongoing challenge. In our study, we introduce a novel framework leveraging LLMs’ decent ability to detect and infer causal relations to construct a causal Knowledge Graph (KG) which represents knowledge that the LLM recognizes. Additionally, we propose a RoBERTa-based Transformer Graph Neural Network (RoTG) specifically designed to select relevant nodes within this KG. Integrating RoTG-retrieved causal chains into prompts effectively enhances LLM performance, demonstrating usefulness of our approach in advancing LLMs’ causal deductive reasoning capabilities.
%U https://aclanthology.org/2024.fever-1.20
%P 151-169
Markdown (Informal)
[Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning](https://aclanthology.org/2024.fever-1.20) (Tan et al., FEVER 2024)
ACL