For large language models (LLMs), reasoning over graphs can help solve many problems. Prior work has tried to improve LLM graph reasoning through different training methods, but the merits of such approaches remain unclear and the limitations of each approach with respect to generalizability of reasoning are often not thoroughly explored. In this paper we systematically compare the ability of LLMs to learn fundamental graph tasks across a variety of training methods and their ability to generalize out of distribution across various dimensions. We highlight key tradeoffs between training methods, e.g., training specialized graph encoders and fusing their embeddings with LLMs consistently collapses in terms of generalizability; however, no single method shows clear superiority across all dimensions of generalizability, regardless of the size of the model.

Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.

Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.