Claude Opus 4.6 fabricates more answers on Global North AI contexts than Global South ones, creating an exploitable vulnerability in AI control monitors.
Survey of hallucination in natural language generation
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation; and (3) hallucinations in large language models (LLMs). This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.
representative citing papers
Large vision-language models exhibit severe object hallucination that varies with training instructions, and the proposed POPE polling method evaluates it more stably and flexibly than prior approaches.
AI-native games require runtime generative AI as a non-substitutable core-loop mechanism; a 53-game corpus clusters in language-forward narrative and epistemic designs.
HLER architecture with pre-commitment, deterministic computation, and three human decision gates reduces LLM-assisted research failure rates from 72% to 16% in a pre-specified 2x4 factorial experiment on four datasets.
SkillCenter provides 216,938 source-grounded skills across 24 domains via an automated LLM-based pipeline, with a downstream evaluation showing skills help agents only when the task exceeds the model's knowledge and retrieval surfaces the right skill.
PAPERCLAW is a multi-agent system for end-to-end autonomous research paper generation from literature to output, with human refinement and LLM-judge evaluation showing strong results.
Galactica, a science-specialized LLM, reports higher scores than GPT-3, Chinchilla, and PaLM on LaTeX knowledge, mathematical reasoning, and medical QA benchmarks while outperforming general models on BIG-bench.
A survey-style reference book mapping the full agentic-AI stack from transformer internals to production deployment, with no new research result.
citing papers explorer
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Geographic Blind Spots in AI Control Monitors: A Cross-National Audit of Claude Opus 4.6
Claude Opus 4.6 fabricates more answers on Global North AI contexts than Global South ones, creating an exploitable vulnerability in AI control monitors.
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Evaluating Object Hallucination in Large Vision-Language Models
Large vision-language models exhibit severe object hallucination that varies with training instructions, and the proposed POPE polling method evaluates it more stably and flexibly than prior approaches.
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AI Native Games: A Survey and Roadmap
AI-native games require runtime generative AI as a non-substitutable core-loop mechanism; a 53-game corpus clusters in language-forward narrative and epistemic designs.
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(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable
HLER architecture with pre-commitment, deterministic computation, and three human decision gates reduces LLM-assisted research failure rates from 72% to 16% in a pre-specified 2x4 factorial experiment on four datasets.
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SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
SkillCenter provides 216,938 source-grounded skills across 24 domains via an automated LLM-based pipeline, with a downstream evaluation showing skills help agents only when the task exceeds the model's knowledge and retrieval surfaces the right skill.
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PaperClaw: Harnessing Agents for Autonomous Research and Human-in-the-Loop Refinement
PAPERCLAW is a multi-agent system for end-to-end autonomous research paper generation from literature to output, with human refinement and LLM-judge evaluation showing strong results.
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Galactica: A Large Language Model for Science
Galactica, a science-specialized LLM, reports higher scores than GPT-3, Chinchilla, and PaLM on LaTeX knowledge, mathematical reasoning, and medical QA benchmarks while outperforming general models on BIG-bench.
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The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
A survey-style reference book mapping the full agentic-AI stack from transformer internals to production deployment, with no new research result.