Model Name: <ParliaBench - unsloth/Qwen2-7B-bnb-4bit>

1. Model Summary

This is a fine-tuned model developed for the research paper "ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech".

The model is trained to:

  • Generate UK-style parliamentary debates
  • Produce party-conditioned political arguments
  • Model parliamentary dialogue structures
  • Perform topic-aware generation using 21 EuroVoc topic categories

2. Base Model

Base model: "unsloth/Qwen2-7B-bnb-4bit"

This repository contains only LoRA adapter weights.
The base model must be loaded separately.

3. Training Data

The model was trained using:

  • ParlaMint-GB from Clarin
  • Preprocessed into:
    • Structured debates
    • Speaker metadata
    • Section headers
    • 21-class topic labels

Dataset links:

4. Training Procedure

Hyperparameters

  • Batch Size 64
  • Learning Rate 2e-4
  • Max Steps 11194 (2 epochs)
  • Warmup Steps 336 (10% of max steps for stability)
  • Optimizer adamw
  • Weight Decay 0.01
  • Max Sequence Length 1024
  • Scheduler linear

Hardware

GPU A100 AWS resources were provided by the National Infrastructures for Research and Technology GRNET and funded by the EU Recovery and Resiliency Facility.

  • LoRA Rank (r) 16
  • LoRA Alpha 16
  • Target Modules 7 layers
  • LoRA Dropout 0
  • Bias Configuration none
  • Random State 3407

5. Intended Uses

This model is designed for:

  • Political debate simulation
  • Parliamentary dialogue generation
  • Academic NLP research
  • Social behavior modeling

6. Limitations

  • May reproduce political biases present in the corpus
  • Not suitable for real political advice or predictions
  • Model outputs are synthetic and not factual

7. Ethical Considerations

  • Avoid using for political persuasion
  • Use strictly in academic and research contexts
  • Outputs may reflect UK political biases from the dataset

8. Citation

@misc{ParliaBench2025, title={ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech}, author={Marios Koniaris and Argyro Tsipi and Panayiotis Tsanakas}, year={2025}, eprint={2511.08247}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2511.08247}, }

9. Authors

Marios Koniaris, Argyro Tsipi, Panayiotis Tsanakas ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech.

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