Fact Verification

92 papers with code • 3 benchmarks • 14 datasets

Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.

Most implemented papers

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

huggingface/transformers NeurIPS 2020

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.

ReAct: Synergizing Reasoning and Acting in Language Models

ysymyth/ReAct 6 Oct 2022

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e. g. chain-of-thought prompting) and acting (e. g. action plan generation) have primarily been studied as separate topics.

Towards Debiasing Fact Verification Models

TalSchuster/FeverSymmetric IJCNLP 2019

Fact verification requires validating a claim in the context of evidence.

KILT: a Benchmark for Knowledge Intensive Language Tasks

facebookresearch/KILT NAACL 2021

We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.

Evidence-based Factual Error Correction

j6mes/2021-acl-factual-error-correction 31 Dec 2020

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

easonnie/combine-FEVER-NSMN 16 Nov 2018

The increasing concern with misinformation has stimulated research efforts on automatic fact checking.

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification

thunlp/GEAR ACL 2019

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims.

End-to-End Bias Mitigation by Modelling Biases in Corpora

rabeehk/robust-nli ACL 2020

We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data.

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

easonnie/semanticRetrievalMRS IJCNLP 2019

In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task.

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

nguyenvo09/EMNLP2020 EMNLP 2020

The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.