HalluPeer: A Taxonomy-driven Benchmark for Detecting Hallucinations in Scientific Peer Reviews

Reducing LLM Hallucinations with Knowledge Graphs: A Survey

NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs'Capability in Paper Novelty Assessment

NovGauge is presented, a human-anchored benchmark for fine-grained novelty assessment diagnosis, and a cascading diagnostic pipeline that verifies per-dimension correctness, evidence grounding, and logical support is proposed.

Mitigating Hallucinations in Natural Language Generation through Prompt Engineering: A Mechanism- Oriented Narrative Review

Large language models (LLMs) can generate fluent and confident responses that are factually incorrect, unsupported by evidence, or inconsistent with the source material. These hallucinations reduce the reliability of LLM-based question answering, summariza tion, dialogue, and information retrieval systems, especially w...

The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked with open-domain question answering (QA) datasets containing questions and corresponding short reference answers. First, an LLM is used to generate answers to questions w...

MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models

This work introduces MISHAP-Bench, a comprehensive benchmark with 12,000 challenging open-ended question-audio pairs and a rigorous evaluation pipeline covering two hallucination categories, and proposes a groundedness judge that uses reference rubrics and judge prompts guided by human annotations.

Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection

This work investigates whether an LLM can use an alternative, low level, symbolic competence such as SQL for unsupervised hallucination detection in some high level task and finds that this approach improves on direct prediction and competes with state-of-the-art hallucination detection methods, while not requiring dom...