mlsanityData to 14 Oct 2026

Towards Trustable Language Models: Investigating Information Quality of Large Language Models

arXiv 2401.13086 ↗First posted 23 Jan 2024cs.CL cs.AI cs.LGcited by 6

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Abstract (arXiv)

Large language models (LLM) are generating information at a rapid pace, requiring users to increasingly rely and trust the data. Despite remarkable advances of LLM, Information generated by LLM is not completely trustworthy, due to challenges in information quality. Specifically, integrity of Information quality decreases due to unreliable, biased, tokenization during pre-training of LLM. Moreover, due to decreased information quality issues, has led towards hallucination, fabricated information. Unreliable information can lead towards flawed decisions in businesses, which impacts economic activity. In this work, we introduce novel mathematical information quality evaluation of LLM, we furthermore analyze and highlight information quality challenges, scaling laws to systematically scale language models.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 16 of its 17 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

16 of 17 references.

References · 16

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4 more
  1. 2023EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Modelscited by 98
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  4. 2017How much is Wikipedia Lagging Behind News?cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Human Decision-making is Susceptible to AI-driven Manipulationcited by 19
  2. 2025MedArabiQ: Benchmarking Large Language Models on Arabic Medical Taskscited by 15
  3. 2026KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibrationcited by 4
  4. 2026MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmarkcited by 3
  5. 2024Distilling Desired Comments for Enhanced Code Review with Large Language Modelscited by 2
  6. 2026Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMscited by 0