arXiv AI By Samer Awad, Javier Conde, Carlos Arriaga, Tairan Fu, Javier Coronado-Bl\'azquez, Pedro Reviriego

Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
3d ago

How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework

The paper introduces a register-aware framework to evaluate how human-like large language models (LLMs) are, focusing on linguistic feature distributions rather than factual correctness. It uses Maximum Mean Discrepancy (MMD) and 67 Biber lexico‑grammatical features to compare LLM‑generated texts with human reference corpora across different registers. Experiments on seven instruction‑tuned, open‑source models across five English datasets show that all LLMs deviate from human baselines, with closeness to human language varying by register and not by model size.

By Bj\"orn Nieth, Marianna Gracheva, Michaela Mahlberg, Bjoern Eskofier, Emmanuelle Salin
arXiv AI
Aug 11

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation

arXiv:2510. 21891v2 Announce Type: replace-cross Abstract: To deploy large language models (LLMs) in high-stakes application domains that require substantively accurate responses to open-ended prompts, we need reliable, computationally inexpensive methods that assess the trustworthiness of long-form responses generated by LLMs.

By Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner