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

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arxiv 2401.13086 v1 pith:SUVAAGIY submitted 2024-01-23 cs.CL cs.AIcs.LG

Rick Rejeleene , Xiaowei Xu , John Talburt This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords informationqualitylanguagemodelstowardschallengeslargeunreliable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

    cs.CL 2026-02 conditional novelty 5.0 of 10

    A reusable per-topic knowledge graph, built once from Wikipedia, lets an LLM generate multi-hop multiple-choice questions whose difficulty is set by path depth, with human-audited quality and model rankings that track MMLU.

  2. Distilling Desired Comments for Enhanced Code Review with Large Language Models

    cs.SE 2024-12 conditional novelty 5.0 of 10

    Desiview identifies desired review comments from code review datasets using the perplexity difference of the actual fix with and without each comment, and the distilled data improves LLaMA-based code review models.

  3. LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models

    cs.CL 2024-11 reject novelty 4.0 of 10

    This paper presents a CPU-only data curation pipeline for LLMs, but the central claim of high-quality output is not supported by any training or quality evaluation.

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