Papers Table-based Fact Verification
“Table-based Fact Verification” 태그가 달린 논문 26편 · 필터 해제
ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics
This paper presents the Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics (ARTEMIS-DA), a novel framework designed to augment Large Language Models (LLMs) for solving complex…
Code GenerationInformation RetrievalQuestion AnsweringRetrieval+3TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact…
Fact VerificationQuestion AnsweringTable-based Fact VerificationNormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in parsing textual data and generating code. However, their performance in tasks involving tabular data, especially those requiring …
Semantic ParsingTable-based Fact VerificationEfficient Prompting for LLM-based Generative Internet of Things
Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due…
Prompt EngineeringQuestion AnsweringSemantic ParsingTable-based Fact VerificationTabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data. Large language models (LLMs) have shown impressive capabilities in natural language understan…
Natural Language UnderstandingQuestion AnsweringSemantic ParsingTable-based Fact Verification+2Are Large Language Models Table-based Fact-Checkers?
Table-based Fact Verification (TFV) aims to extract the entailment relation between statements and structured tables. Existing TFV methods based on small-scaled models suffer from insufficient labeled data and weak zero-…
Fact VerificationIn-Context LearningPrompt EngineeringTable-based Fact VerificationChain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, ta…
Fact VerificationIn-Context LearningQuestion AnsweringSemantic Parsing+2Heuristic Heterogeneous Graph Reasoning Networks for Fact Verification
Existing studies on table-based fact verification generally capture linguistic evidence from claim-table subgraphs or logical evidence from program-table subgraphs independently. However, there is insufficient associatio…
Fact Verificationgraph constructionTable-based Fact VerificationLarge Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, pre…
HallucinationSemantic ParsingTable-based Fact VerificationPASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While f…
Fact CheckingFact VerificationLanguage ModellingMarketing+3ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding t…
Fact VerificationQuestion AnsweringSemantic ParsingTable-based Fact Verification+2Binding Language Models in Symbolic Languages
Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework tha…
Language ModellingSemantic ParsingTable-based Fact VerificationTable-based Fact Verification with Self-labeled Keypoint Alignment
Table-based fact verification aims to verify whether a statement sentence is trusted or fake. Most existing methods rely on graph feature or data augmentation but fail to investigate evidence correlation between the stat…
AttributeContrastive LearningData AugmentationFact Verification+3Table-based Fact Verification with Self-adaptive Mixture of Experts
The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem. It inherently requires informative reasoning over natural language together with different…
Fact VerificationLogical ReasoningManagementMixture-of-Experts+1UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG task…
Few-Shot LearningQuestion AnsweringSemantic ParsingTable-based Fact Verification+1ConTFV: A Contrastive Learning Framework for Table-based Fact Verification
Table-based fact verification is a binary classification task where the challenging part lies in the table's structural parsing and symbolic reasoning. Jointly pre-training on abundant textual and tabular data has been c…
Binary ClassificationContrastive LearningFact VerificationSemantic Parsing+3Table-based Fact Verification with Self-adaptive Mixture of Experts
The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem. It inherently requires informative reasoning over natural language together with different…
Fact VerificationLogical ReasoningManagementMixture-of-Experts+1Exploring Decomposition for Table-based Fact Verification
Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a …
Fact VerificationTable-based Fact VerificationLogic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing method…
Fact VerificationRetrievalTable-based Fact VerificationTable-based Fact Verification with Salience-aware Learning
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements …
counterfactualData AugmentationFact VerificationTable-based Fact Verification