REVIEW 1 cited by
Towards Trustable Language Models: Investigating Information Quality of Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Towards Trustable Language Models: Investigating Information Quality of Large Language Models
read the original abstract
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.
Forward citations
Cited by 1 Pith paper
-
KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.