Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation

Zdeněk Kasner; Ondřej Dušek

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arXiv · 2401.10186

Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation

Zdeněk Kasner·Ondřej Dušek

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Abstract

We analyze the behaviors of open large language models (LLMs) on the task of data-to-text (D2T) generation, i.e., generating coherent and relevant text from structured data. To avoid the issue of LLM training data contamination with standard benchmarks, we design Quintd - a tool for collecting novel structured data records from public APIs. We find that open LLMs (Llama 2, Mistral, and Zephyr) can generate fluent and coherent texts in zero-shot settings from data in common formats collected with Quintd. However, we show that the semantic accuracy of the outputs is a major issue: both according to human annotators and our reference-free metric based on GPT-4, more than 80% of the outputs of open LLMs contain at least one semantic error. We publicly release the code, data, and model outputs.

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Zdeněk Kasner, Ondřej Dušek. 2024-01-18. Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation. https://arxiv.org/abs/2401.10186

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