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.

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental human mistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguous to humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popular language models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases. The infrastructure, datacards, and robustness evaluation results are publicly available on GitHub for the benefit of researchers working on paraphrase generation, robustness analysis, and low-resource NLP.