Text Generation
PEFT
English
politics
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Model Card (Llama-2 13B - LoRA on speeches from members of GUE/NGL)

In this work (Chalkidis and Brandl, 2024), we adapt Llama Chat to speeches of the members of a euro party from the EU Debates dataset. To do so, we fine-tune the 13B Llama Chat model on the speeches using adapters, specifically Low-Rank Adaptation (LoRA) (Hu et al., 2022). Since we are interested in fine-tuning conversational (chat-based) models, we create instructions as pseudo-QA pairs, similar to Cheng et al. (2023) using a pseudo-QA template:

[INST] What is your opinion on T ? [/INST] U

where the instruction (question) is based on the title (topic) of the debate (T), e.g., "Immigration, the role of Frontex and cooperation among Member States", and U is a clean version of a speech (utterance) from an MEP affiliated with the party of interest.

We use a learning rate of 2e-4, and train for 10 epochs across all data points (speeches) from the party of interest. We set LoRa alpha (α) at 16, and the rank (r) at 8.

The model was developed with the code base in the following GitHub repository: https://github.com/coastalcph/eu-politics-llms/

This is the LoRA adapter for the model adapted to the speeches from MEPs affiliated with the European United Left/Nordic Green Left Party (GUE/NGL).

We have also released the following LoRA adapters:

Radar Plots

Caution / Unintended Use / Biases

The adapted models can be seen as data-driven mirrors of the parties’ ideologies, but are by no means ’perfectly’ aligned, and thus may misrepresent them. The models have been developed solely for research purposes and should not be used to generate content and share publicly. We urge the community and the public to refer to credible sources, e.g., parties’ programs, interviews, original speeches, etc., when it comes to getting political information. In some cases, models may generate text that can be considered hateful, toxic, harmful, or inappropriate. Please use them with caution.

How to use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


# Load the base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf",
                                                  device_map="auto",
                                                  torch_dtype=torch.float16,
                                                  attn_implementation="flash_attention_2")

# Load the LoRA adapter
model = PeftModel.from_pretrained(base_model,
                                  "coastalcph/Llama-2-13b-chat-hf-LoRA-eu-debates-gue-ngl",
                                  device_map="auto")


# Build pipeline
pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer
        )

Citation Information

Llama meets EU: Investigating the European political spectrum through the lens of LLMs. Ilias Chalkidis and Stephanie Brandl. In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Mexico City, Mexico, June 16–21, 2024.

@inproceedings{chalkidis-and-brandl-eu-llama-2024,
    title = "Llama meets EU: Investigating the European political spectrum through the lens of LLMs",
    author = "Chalkidis, Ilias  and Brandl, Stephanie",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
}
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Dataset used to train coastalcph/Llama-2-13b-chat-hf-LoRA-eu-debates-gue-ngl