Combining Extraction and Generation for Constructing Belief-Consequence Causal Links (2022)

Abstract: In this paper, we introduce and justify a new task—causal link extraction based on beliefs—and do a qualitative analysis of the ability of a large language model—InstructGPT-3—to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.

## introduction natural language processing can successfully capture the causal dynamics present in many complex systems. this type of automated extraction is particularly useful for computational modelers, who may be faced with a large and complex domain literature that cannot be easily summarized by humans. information extraction systems like eidos @xcite can help modelers build skeleton models of causes and effects present in systems by extracting causal links that exist between entities and processes. while many causal dynamics are mechanistic, such as water level driving crop yield, other dynamics are driven by subjective factors, such as the political beliefs of a population driving their decisions to wear masks. extracting these dynamics comes with two challenges: extracting the beliefs and consequences present in the text, and inferring implicit consequences of beliefs. for example, the following sentence contains both a belief and an explicit consequence: the above sentence can be represented by a binary, directed causal link, where the first node is the belief about the rainy season and the second node is the consequence of the belief (crop sowing). however, the consequences of beliefs are frequently implied, such as in the following sentence: to a human, the obvious consequence is that the farmers will not use chemicals, but the text does not explicitly state this. a modeler wants to generate causal belief-consequence pairs from a large literature without annotating every implicit consequence; thus, methods of automating belief extraction ought to account for implicit consequences. in this paper, we address the problem of extraction of beliefs and their consequences with a novel extraction + generation approach. we first extract beliefs using an event extraction grammar; and we then use text generation with large language models (lm) to generate possible consequences of the extracted beliefs when no consequence is stated in text. we expect that given a belief and its context, there is only a limited number of possible consequences humans can infer. for the consequence generation approach to be considered successful, we want machine-generated consequences to match those produced by humans-that would be an indicator that generated beliefs are indeed relevant for the model. with this work, we make the following contributions: ## procedure we automatically extracted beliefs from a collection of documents-scientific publications and reports-related to agriculture and social norms of senegal. we then double-annotated fifty of those beliefs with whether or not their consequences were explicitly stated in one-sentence and one-paragraph ## results and discussion based on the comparison of two sets of annotations, we see that a large number of beliefs do not have associated explicitly-stated consequences: the two annotators judged an average of 72% of the 50 beliefs annotated to not have consequences explicitly stated within the same sentence and an average of 49% to not have them within the one paragraph context window. these results indicate that both extraction and generation have to be included in the approach. analyzing the 18 beliefs that both annotators agreed did not have explicitly stated consequences, we see that, as expected, annotators tend to agree condition overlap two annotators 13 (72%) gpt-3 and one annotator 12 (66%) gpt-3 and both annotators 9 (50%) on possible consequences of beliefs: for 72% of beliefs, human annotators produced potential consequences with similar content ( annotator 1: planners and officials will invest more in the development of irrigation systems. annotator 2: they should develop irrigation systems. ## future work to address the issues with gpt-3 generations discussed in the paper, in future work, we want to explore the possibility of using explicitly stated consequences as data to improve explicit consequence generation by large language models, both for fine-tuning and as few-shot examples. for extracting explicit consequences, we will be exploring expanding the current rule-based framework to cross-sentence extractions as well as using gpt-3 text generation for event extraction. with the latter, the task can be framed in the following way: given a belief and its context, the model should produce the text that matches the text span that indicates the consequence of the belief. for both these directions, we need to have a corpus of belief-consequence annotations, which means we will be conducting an annotation exercise. this will also help us support our findings regarding distributions of explicit and implicit consequences as well as the ability of human annotators to infer implicit consequences based on the belief and its context. ## conclusion in this paper, we introduce the task of causal link extraction based on beliefs. we propose an approach for the task that combines extraction and generation, and provide a small-scale, qualitative analysis of a large language model performance on the task. additionally, we outline directions of future work.