@inproceedings{keles-bayrakli-2024-llama,
    title = "{LL}a{MA}-2-Econ: Enhancing Title Generation, Abstract Classification, and Academic {Q}{\&}{A} in Economic Research",
    author = "Keles, Onur  and
      Bayrakl{\i}, Omer Turan",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.21",
    pages = "212--218",
    abstract = "Using Quantized Low Rank Adaptation and Parameter Efficient Fine Tuning, we fine-tuned Meta AI{'}s LLaMA-2-7B large language model as a research assistant in the field of economics for three different types of tasks: title generation, abstract classification, and question and answer. The model was fine-tuned on economics paper abstracts and syntheticically created question-answer dialogues based on the abstracts. For the title generation, the results of the experiment demonstrated that LLaMA-2-Econ (the fine-tuned model) surpassed the base model (7B and 13B) with few shot learning, and comparable models of similar size like Mistral-7B and Bloom-7B in the BLEU and ROUGE metrics. For abstract categorization, LLaMA-2-Econ outperformed different machine and deep learning algorithms in addition to state-of-the-art models like GPT 3.5 and GPT 4 with both single and representative few shot learning. We tested the fine-tuned Q{\&}A model by comparing its output with the base LLaMA-2-7B-chat with a Retrieval Augmented Generation (RAG) pipeline with semantic search and dense vector indexing, and found that LLaMA-2 performed on a par with the base model with RAG.",
}

Using Quantized Low Rank Adaptation and Parameter Efficient Fine Tuning, we fine-tuned Meta AI{'}s LLaMA-2-7B large language model as a research assistant in the field of economics for three different types of tasks: title generation, abstract classification, and question and answer. The model was fine-tuned on economics paper abstracts and syntheticically created question-answer dialogues based on the abstracts. For the title generation, the results of the experiment demonstrated that LLaMA-2-Econ (the fine-tuned model) surpassed the base model (7B and 13B) with few shot learning, and comparable models of similar size like Mistral-7B and Bloom-7B in the BLEU and ROUGE metrics. For abstract categorization, LLaMA-2-Econ outperformed different machine and deep learning algorithms in addition to state-of-the-art models like GPT 3.5 and GPT 4 with both single and representative few shot learning. We tested the fine-tuned Q{\&}A model by comparing its output with the base LLaMA-2-7B-chat with a Retrieval Augmented Generation (RAG) pipeline with semantic search and dense vector indexing, and found that LLaMA-2 performed on a par with the base model with RAG.
Keles, Onur Bayrakl{\i}, Omer Turan
LLaMA-2-Econ: Enhancing Title Generation, Abstract Classification, and Academic Q&A in Economic Research