An Empirical Analysis of Factual Errors in Human-Written Text and Its Application to Factual Error Detection

arXiv:2606.27959 · cs.CL · Submitted 2026-06-26 · Read on arXiv

cs.CL

Submitted: 2026-06-26

Updated: 2026-09-14

Comments: EMNLP 2026 Findings

Code: https://github.com/nlp-waseda/JTruthfulQA

Project page: https://taku910.github.io/mecab/14https://www.cl.ecei.tohoku.ac.jp/~m-suzuki/jawiki_vector/15https://huggingface.co/tokyotech-llm/Llama-3

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Hallucination — 23× in this paper · explained in 3 episodes
Fine-Tuning — 22× in this paper · explained in 5 episodes
F1 Score — 11× in this paper · explained in 3 episodes
Verification — 10× in this paper · explained in 2 episodes
Benchmark — 8× in this paper · explained in 2 episodes
Factual Error Correction — 6× in this paper
Factual Error Detection — 6× in this paper
Hallucination Detection — 6× in this paper · explained in 2 episodes
Adam — 3× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
Consistency — 2× in this paper · explained in 3 episodes
Transformer — 2× in this paper · explained in 2 episodes
Accuracy — 1× in this paper · explained in 1 episode
Adaptation — 1× in this paper · explained in 1 episode
Grounding — 1× in this paper · explained in 3 episodes
In-Context Learning — 1× in this paper · explained in 4 episodes
Quantization — 1× in this paper · explained in 12 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
alpha — 1× in this paper · explained in 1 episode

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