Preslav Nakov | Web IR NLP Group @ NUS

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

Wed, 01 Jul 2026 00:00:00 +0000

MuSLR: Multimodal Symbolic Logical Reasoning

Mon, 01 Dec 2025 00:00:00 +0000

TART – An Open-Source Tool-Augmented Framework for Explainable Table-Based Reasoning

Wed, 01 Jan 2025 00:00:00 +0000

TART is designed to overcome the limitations of LLMs in table reasoning by combining structured table processing, precise tool execution, and explainability. The key components of TART are:

Table Formatter: Prepares tables by formatting, aligning, and cleaning the data for structured input.
Tool Maker: Dynamically generates tools (Python functions) tailored for complex table operations.
Explanation Generator: Integrates structured reasoning steps and tool outputs into human-readable explanations.

TART is evaluated on nine table-based reasoning benchmarks, including SCITAB, TabFact, FinQA, and HybridQA. The framework consistently outperforms Chain-of-Thought (CoT) prompting by integrating structured tool-assisted reasoning.

TART has received the Best Paper Runner-Up Award at the 3rd Table Representation Learning Workshop at NeurIPS 2024 and has been accepted at NAACL 2025. Its code and dataset are publicly available for further research. Researchers working on LLM explainability, table reasoning, and AI-assisted fact-checking are encouraged to contribute.

SCITAB – A Benchmark for Scientific Table-Based Fact-Checking

Wed, 06 Dec 2023 00:00:00 +0000

SCITAB was developed to fill the gap in scientific fact-checking benchmarks by incorporating claims derived from actual scientific papers rather than crowd-sourced ones. These claims require multi-step reasoning to verify against evidence in tables. The dataset was created using a human–model collaboration approach, where real-world claims were filtered, counterclaims were generated using InstructGPT, and all claims were manually verified by expert annotators.

The dataset has been extensively evaluated with state-of-the-art models, including TAPAS, TAPEX, Vicuna, and GPT-4, revealing that existing table-based models struggle with SCITAB. Only GPT-4 shows notable improvements, highlighting the dataset’s challenge and potential for advancing research in table-based reasoning.

SCITAB was presented at EMNLP 2023, and the dataset is publicly available for further research. Researchers interested in compositional reasoning, table fact-checking, and model robustness are encouraged to explore SCITAB and contribute to its future developments.

On the Risk of Misinformation Pollution with Large Language Models

Fri, 01 Dec 2023 00:00:00 +0000

QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking

Fri, 01 Dec 2023 00:00:00 +0000

SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables

Fri, 01 Dec 2023 00:00:00 +0000

Fact-Checking Complex Claims with Program-Guided Reasoning

Sat, 01 Jul 2023 00:00:00 +0000

FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

Wed, 01 Jan 2020 00:00:00 +0000

A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages

Fri, 01 Oct 2010 00:00:00 +0000