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The paper introduces DEDUCE, a three‑stage framework that turns large language models into proactive error correctors by detecting input fact errors, devising correction strategies, and delivering reliable answers. It also presents MisFactQA, a dataset of factual errors, and new metrics for robustness evaluation. Experiments on TruthfulQA, FalseQA, and MisFactQA show significant gains in accuracy and error correction across Qwen, LLaMA, and Gemma models.
arXiv:2609.15561v1 Announce Type: new Abstract: Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy?...
arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces proposes DDRG, a framework that uses graph alignment to detect claim-level disagreements in reasoning traces and selectively corrects them with a verifier. DDRG builds source‑grounded claim graphs, sends localized conflicts to an answer‑blind verifier, and applies a focused repair only when the verifier supports the majority answer’s path. Experiments on five benchmarks show that zero‑shot DDRG improves macro‑average accuracy by 8.65–15.83 percentage points over direct answering and outperforms CoT, GoT, GoV, and SC on this metric.
ReliableRAG is a new framework for Retrieval-Augmented Generation that tackles misinformation in multi‑hop question answering. It extracts structured triples from retrieved documents, evaluates each triple’s reliability by combining semantic relevance to the query with credibility, and keeps only the top‑K reliable, non‑redundant triples. Using these refined triples, the system builds robust reasoning chains that filter out deceptive misinformation and produce accurate, trustworthy answers.
The paper presents an empirical study of factual errors in human-written text, focusing on corrections in newspaper articles to build a taxonomy of common mistakes such as kanji misconversions and unit errors. It evaluates large language models’ ability to detect these errors, finding that even advanced models like GPT‑5.4 achieve only a 52% word‑level F1 score on synthetic data, underscoring the difficulty of the task. The work highlights the gap in research on factual error detection in human writing compared to LLM hallucinations.