ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech
This addresses the need for specialized evaluation in parliamentary speech generation for political and NLP researchers, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of generating parliamentary speeches with LLMs by creating ParliaBench, a benchmark combining computational metrics and LLM-as-a-judge assessments, and fine-tuning five models on a UK Parliament dataset, resulting in statistically significant improvements across most metrics and strong discriminative power for political dimensions.
Parliamentary speech generation presents specific challenges for large language models beyond standard text generation tasks. Unlike general text generation, parliamentary speeches require not only linguistic quality but also political authenticity and ideological consistency. Current language models lack specialized training for parliamentary contexts, and existing evaluation methods focus on standard NLP metrics rather than political authenticity. To address this, we present ParliaBench, a benchmark for parliamentary speech generation. We constructed a dataset of speeches from UK Parliament to enable systematic model training. We introduce an evaluation framework combining computational metrics with LLM-as-a-judge assessments for measuring generation quality across three dimensions: linguistic quality, semantic coherence, and political authenticity. We propose two novel embedding-based metrics, Political Spectrum Alignment and Party Alignment, to quantify ideological positioning. We fine-tuned five large language models (LLMs), generated 28k speeches, and evaluated them using our framework, comparing baseline and fine-tuned models. Results show that fine-tuning produces statistically significant improvements across the majority of metrics and our novel metrics demonstrate strong discriminative power for political dimensions.