# FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking

arXiv: https://arxiv.org/abs/2309.00240 · HTML: https://mlsanity.com/paper/2309.00240/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [Generating Fact Checking Explanations](https://mlsanity.com/paper/2004.05773/) (2020)
- [DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning](https://mlsanity.com/paper/1809.06416/) (2018)
- [A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection](https://mlsanity.com/paper/2209.14642/) (2022)

## Built on by (most cited first, 30 of 37)

- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom](https://mlsanity.com/paper/2405.03371/) (2024)
- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Multimodal Misinformation Detection using Large Vision-Language Models](https://mlsanity.com/paper/2407.14321/) (2024)
- [Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models](https://mlsanity.com/paper/2505.19509/) (2025)
- [Research Trends for the Interplay between Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2406.08223/) (2024)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Multimodal Large Language Models to Support Real-World Fact-Checking](https://mlsanity.com/paper/2403.03627/) (2024)
- [Web Retrieval Agents for Evidence-Based Misinformation Detection](https://mlsanity.com/paper/2409.00009/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models](https://mlsanity.com/paper/2310.00259/) (2023)
- [Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies](https://mlsanity.com/paper/2412.05155/) (2024)
- [Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification](https://mlsanity.com/paper/2402.10735/) (2024)
- [Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking](https://mlsanity.com/paper/2502.15419/) (2025)
- [VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning](https://mlsanity.com/paper/2406.10289/) (2024)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- [MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation](https://mlsanity.com/paper/2403.14171/) (2024)
- [Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion](https://mlsanity.com/paper/2410.10408/) (2024)
- [Task-Oriented Automatic Fact-Checking with Frame-Semantics](https://mlsanity.com/paper/2501.13288/) (2025)
- [STRIVE: Structured Reasoning for Self-Improvement in Claim Verification](https://mlsanity.com/paper/2502.11959/) (2025)
- [Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability](https://mlsanity.com/paper/2506.03655/) (2025)
- [ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model](https://mlsanity.com/paper/2403.06765/) (2024)
- [HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering](https://mlsanity.com/paper/2512.24562/) (2025)
- [Detecting Manipulated Contents Using Knowledge-Grounded Inference](https://mlsanity.com/paper/2504.21165/) (2025)
- [Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability](https://mlsanity.com/paper/2505.15050/) (2025)
- [REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control](https://mlsanity.com/paper/2511.20233/) (2025)
- [TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection](https://mlsanity.com/paper/2602.11106/) (2026)
- [LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction](https://mlsanity.com/paper/2602.13279/) (2026)
- [Black-Box Guardrail Reverse-engineering Attack](https://mlsanity.com/paper/2511.04215/) (2025)

## Abstract (arXiv)

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.