@inproceedings{rajpoot-etal-2024-adapting,
    title = "Adapting {LLM} to Multi-lingual {ESG} Impact and Length Prediction Using In-context Learning and Fine-Tuning with Rationale",
    author = "Rajpoot, Pawan Kumar  and
      Jindal, Ashvini  and
      Parikh, Ankur",
    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-1.30",
    pages = "274--278",
    abstract = "The prediction of Environmental, Social, and Governance (ESG) impact and duration (length) of impact from company events, as reported in news articles, hold immense significance for investors, policymakers, and various stakeholders. In this paper, we describe solutions from our team {``}Upaya{''} to ESG impact and length prediction tasks on one such dataset ML-ESG-3. ML-ESG-3 dataset was released along with shared task as a part of the Fifth Workshop on Knowledge Discovery from Unstructured Data in Financial Services, co-located with LREC-COLING 2024. We employed two different paradigms to adapt Large Language Models (LLMs) to predict both the ESG impact and length of events. In the first approach, we leverage GPT-4 within the In-context learning (ICL) framework. A learning-free dense retriever identifies top K-relevant In-context learning examples from the training data for a given test example. The second approach involves instruction-tuning Mistral (7B) LLM to predict impact and duration, supplemented with rationale generated using GPT-4. Our models secured second place in French tasks and achieved reasonable results (fifth and ninth rank) in English tasks. These results demonstrate the potential of different LLM-based paradigms for delivering valuable insights within the ESG investing landscape.",
}

The prediction of Environmental, Social, and Governance (ESG) impact and duration (length) of impact from company events, as reported in news articles, hold immense significance for investors, policymakers, and various stakeholders. In this paper, we describe solutions from our team {``}Upaya{''} to ESG impact and length prediction tasks on one such dataset ML-ESG-3. ML-ESG-3 dataset was released along with shared task as a part of the Fifth Workshop on Knowledge Discovery from Unstructured Data in Financial Services, co-located with LREC-COLING 2024. We employed two different paradigms to adapt Large Language Models (LLMs) to predict both the ESG impact and length of events. In the first approach, we leverage GPT-4 within the In-context learning (ICL) framework. A learning-free dense retriever identifies top K-relevant In-context learning examples from the training data for a given test example. The second approach involves instruction-tuning Mistral (7B) LLM to predict impact and duration, supplemented with rationale generated using GPT-4. Our models secured second place in French tasks and achieved reasonable results (fifth and ninth rank) in English tasks. These results demonstrate the potential of different LLM-based paradigms for delivering valuable insights within the ESG investing landscape.
Rajpoot, Pawan Kumar Jindal, Ashvini Parikh, Ankur
Adapting LLM to Multi-lingual ESG Impact and Length Prediction Using In-context Learning and Fine-Tuning with Rationale