12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

Hallucination Control

The Hallucination Control theme comprises 1,017 papers in this corpus published between 2014 and 2026. Work here is dominated by Experimental, Empirical Study, Benchmarking. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Experimental249 (24%)
  • Empirical Study244 (24%)
  • Benchmarking149 (15%)
  • Design Science94 (9%)
  • Literature Review85 (8%)
  • Conceptual64 (6%)

Research domains

  • Research Integrity730 (72%)
  • Bias & Epistemic Risk130 (13%)
  • Data Analysis65 (6%)
  • Knowledge Synthesis23 (2%)
  • Academic Writing14 (1%)
  • Literature Discovery10 (1%)

Frequent sub-topics

detection of AI-generated survey responses from large language models · 1moral consistency in LLM decision-making under prompt perturbations · 1Generative AI integrity in academic publishing; human-AI boundaries · 1wisdom of crowds approach with LLM agents for fraud detection · 1abductive reasoning evaluation in LLMs · 1algorithmic bias in AI-based clinical decision systems · 1LLM hallucination detection and mitigation via system reliability theory · 1structural biases in LLM-as-a-Judge systems · 1

Open research gaps

  • Not explicitly stated in abstract
  • The paper identifies a gap between current LLM architectures and the representational requirements for truth-bearers. It suggests that existing explanations of hallucination converge on an underlying
  • The authors identify the following gaps and future directions: (1) Limited comprehensiveness of existing benchmarks for hallucinated citation detection; (2) Lack of easily reproducible tools and analy
  • The authors identify that existing benchmarks focus on single-turn, English-centric tasks and do not address multi-turn dynamics and linguistic-regulatory nuances of non-English financial domains, spe
  • The authors identify open problems for achieving metacognitive awareness in LLMs. Specifically, they highlight that for direct interaction, "acting on uncertainty means communicating it honestly; for

Representative papers

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