88,830 research outputs found
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
Fact checking aims to predict claim veracity by reasoning over multiple
evidence pieces. It usually involves evidence retrieval and veracity reasoning.
In this paper, we focus on the latter, reasoning over unstructured text and
structured table information. Previous works have primarily relied on
fine-tuning pretrained language models or training homogeneous-graph-based
models. Despite their effectiveness, we argue that they fail to explore the
rich semantic information underlying the evidence with different structures. To
address this, we propose a novel word-level Heterogeneous-graph-based model for
Fact Checking over unstructured and structured information, namely HeterFC. Our
approach leverages a heterogeneous evidence graph, with words as nodes and
thoughtfully designed edges representing different evidence properties. We
perform information propagation via a relational graph neural network,
facilitating interactions between claims and evidence. An attention-based
method is utilized to integrate information, combined with a language model for
generating predictions. We introduce a multitask loss function to account for
potential inaccuracies in evidence retrieval. Comprehensive experiments on the
large fact checking dataset FEVEROUS demonstrate the effectiveness of HeterFC.
Code will be released at: https://github.com/Deno-V/HeterFC.Comment: Accepted by 38th Association for the Advancement of Artificial
Intelligence, AAA
FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking
Automatic fact-checking plays a crucial role in combating the spread of
misinformation. Large Language Models (LLMs) and Instruction-Following
variants, such as InstructGPT and Alpaca, have shown remarkable performance in
various natural language processing tasks. However, their knowledge may not
always be up-to-date or sufficient, potentially leading to inaccuracies in
fact-checking. To address this limitation, we propose combining the power of
instruction-following language models with external evidence retrieval to
enhance fact-checking performance. Our approach involves leveraging search
engines to retrieve relevant evidence for a given input claim. This external
evidence serves as valuable supplementary information to augment the knowledge
of the pretrained language model. Then, we instruct-tune an open-sourced
language model, called LLaMA, using this evidence, enabling it to predict the
veracity of the input claim more accurately. To evaluate our method, we
conducted experiments on two widely used fact-checking datasets: RAWFC and
LIAR. The results demonstrate that our approach achieves state-of-the-art
performance in fact-checking tasks. By integrating external evidence, we bridge
the gap between the model's knowledge and the most up-to-date and sufficient
context available, leading to improved fact-checking outcomes. Our findings
have implications for combating misinformation and promoting the dissemination
of accurate information on online platforms. Our released materials are
accessible at: https://thcheung.github.io/factllama.Comment: Accepted in APSIPA ASC 202
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
Although Large Language Models (LLMs) have demonstrated extraordinary
capabilities in many domains, they still have a tendency to hallucinate and
generate fictitious responses to user requests. This problem can be alleviated
by augmenting LLMs with information retrieval (IR) systems (also known as
retrieval-augmented LLMs). Applying this strategy, LLMs can generate more
factual texts in response to user input according to the relevant content
retrieved by IR systems from external corpora as references. In addition, by
incorporating external knowledge, retrieval-augmented LLMs can answer in-domain
questions that cannot be answered by solely relying on the world knowledge
stored in parameters. To support research in this area and facilitate the
development of retrieval-augmented LLM systems, we develop RETA-LLM, a
{RET}reival-{A}ugmented LLM toolkit. In RETA-LLM, we create a complete pipeline
to help researchers and users build their customized in-domain LLM-based
systems. Compared with previous retrieval-augmented LLM systems, RETA-LLM
provides more plug-and-play modules to support better interaction between IR
systems and LLMs, including {request rewriting, document retrieval, passage
extraction, answer generation, and fact checking} modules. Our toolkit is
publicly available at https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM.Comment: Technical Report for RETA-LL
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification
The increasing rate at which scientific knowledge is discovered and health
claims shared online has highlighted the importance of developing efficient
fact-checking systems for scientific claims. The usual setting for this task in
the literature assumes that the documents containing the evidence for claims
are already provided and annotated or contained in a limited corpus. This
renders the systems unrealistic for real-world settings where knowledge sources
with potentially millions of documents need to be queried to find relevant
evidence. In this paper, we perform an array of experiments to test the
performance of open-domain claim verification systems. We test the final
verdict prediction of systems on four datasets of biomedical and health claims
in different settings. While keeping the pipeline's evidence selection and
verdict prediction parts constant, document retrieval is performed over three
common knowledge sources (PubMed, Wikipedia, Google) and using two different
information retrieval techniques. We show that PubMed works better with
specialized biomedical claims, while Wikipedia is more suited for everyday
health concerns. Likewise, BM25 excels in retrieval precision, while semantic
search in recall of relevant evidence. We discuss the results, outline frequent
retrieval patterns and challenges, and provide promising future directions.Comment: Accepted to EACL 202
Automated Fact Checking in the News Room
Fact checking is an essential task in journalism; its importance has been
highlighted due to recently increased concerns and efforts in combating
misinformation. In this paper, we present an automated fact-checking platform
which given a claim, it retrieves relevant textual evidence from a document
collection, predicts whether each piece of evidence supports or refutes the
claim, and returns a final verdict. We describe the architecture of the system
and the user interface, focusing on the choices made to improve its
user-friendliness and transparency. We conduct a user study of the
fact-checking platform in a journalistic setting: we integrated it with a
collection of news articles and provide an evaluation of the platform using
feedback from journalists in their workflow. We found that the predictions of
our platform were correct 58\% of the time, and 59\% of the returned evidence
was relevant
Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
Large Language Models (LLMs) are increasingly used for accessing information
on the web. Their truthfulness and factuality are thus of great interest. To
help users make the right decisions about the information they're getting, LLMs
should not only provide but also help users fact-check information. In this
paper, we conduct experiments with 80 crowdworkers in total to compare language
models with search engines (information retrieval systems) at facilitating
fact-checking by human users. We prompt LLMs to validate a given claim and
provide corresponding explanations. Users reading LLM explanations are
significantly more efficient than using search engines with similar accuracy.
However, they tend to over-rely the LLMs when the explanation is wrong. To
reduce over-reliance on LLMs, we ask LLMs to provide contrastive information -
explain both why the claim is true and false, and then we present both sides of
the explanation to users. This contrastive explanation mitigates users'
over-reliance on LLMs, but cannot significantly outperform search engines.
However, showing both search engine results and LLM explanations offers no
complementary benefits as compared to search engines alone. Taken together,
natural language explanations by LLMs may not be a reliable replacement for
reading the retrieved passages yet, especially in high-stakes settings where
over-relying on wrong AI explanations could lead to critical consequences.Comment: preprin
Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies
Efficiently querying Description Logic (DL) ontologies is becoming a vital
task in various data-intensive DL applications. Considered as a basic service
for answering object queries over DL ontologies, instance checking can be
realized by using the most specific concept (MSC) method, which converts
instance checking into subsumption problems. This method, however, loses its
simplicity and efficiency when applied to large and complex ontologies, as it
tends to generate very large MSC's that could lead to intractable reasoning. In
this paper, we propose a revision to this MSC method for DL SHI, allowing it to
generate much simpler and smaller concepts that are specific-enough to answer a
given query. With independence between computed MSC's, scalability for query
answering can also be achieved by distributing and parallelizing the
computations. An empirical evaluation shows the efficacy of our revised MSC
method and the significant efficiency achieved when using it for answering
object queries
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