Atomic Inference for NLI with Generated Facts as Atoms (2024)

Abstract: With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models are faithful, i.e. that they reflect the inner workings of the model. Atomic inference overcomes this issue, providing interpretable and faithful model decisions. This approach involves making predictions for different components (or atoms) of an instance, before using interpretable and deterministic rules to derive the overall prediction based on the individual atom-level predictions. We investigate the effectiveness of using LLM-generated facts as atoms, decomposing Natural Language Inference premises into lists of facts. While directly using generated facts in atomic inference systems can result in worse performance, with 1) a multi-stage fact generation process, and 2) a training regime that incorporates the facts, our fact-based method outperforms other approaches.

## introduction current state-of-the-art models achieve impressive performance on various natural language understanding tasks. however, predictions from these models are not interpretable, and while existing methods can suggest plausible reasons for each prediction @xcite , there are no guarantees that these reasons are faithful to the underlying decision-making process of the model @xcite . despite the importance of inherently interpretable models for high-stakes decision-making @xcite , few works on interpretability consider this type of model @xcite . motivated by this idea, we aim to introduce an inherently interpretable model that produces plausible and faithful explanations. this method involves decomposing an input into components (atoms) and making hard classification decisions independently for each atom. a sequence of interpretable and deterministic rules is then applied to derive the overall prediction based on the model decisions for these atoms. we refer to this approach as atomic inference, producing interpretable models that reveal the specific atom-level decisions responsible for each instance-level prediction. atomic inference methods are effective when underpinned by an appropriate choice of atoms, allowing models to independently make accurate predictions for each component part of an input. we investigate the effectiveness of using generated facts as our atoms. specifically, we use an llm to generate a comprehensive list of facts that summarises an input. this fact decomposition results in more atoms than a sentence segmentation, providing more fine-grained model interpretability. moreover, we show that fact-based methods can considerably outperform existing methods that use either sentences or word spans as atoms. we test our atomic inference methods on natural language inference (nli), a task that involves reasoning about the relationship between a premise and a hypothesis. this follows previous work with atomic methods, which often consider nli @xcite @xcite or tasks analogous to nli @xcite @xcite @xcite . to improve our fact-based models, we introduce different strategies to make the generated fact lists comprehensive, preventing important information from being missed during inference. we further experiment with an attention-based architecture that introduces the fact-generated atoms during training. we describe our best performing system as fglr (fact-generated logical reasoning), a method that achieves state-of-the-art results for atomic inference, while also outperforming several large-scale llms. ## related work atomic inference involves making discrete, atomlevel predictions that are used to determine instance-level predictions @xcite , highlighting the specific atoms that are responsible for each model prediction foot_1 . this contrasts with atom-based methods that require soft atom-level predictions @xcite , or methods where the predictions for each atom also have access to other parts of the input @xcite @xcite . common choices of atoms include sentences @xcite @xcite , word spans @xcite @xcite , paragraphs @xcite , propositions @xcite , or semantic triples @xcite . recent work has further considered the decomposition of texts into lists of facts, using language models to generate fact lists that itemise the information present @xcite . we consider the effectiveness of using generated facts for atomic inference, with models making hard entailment decisions about each fact. most atom-based methods either use existing nli models to make atom-level predictions @xcite @xcite , or provide additional atom-level annotations to be used for model training @xcite . we choose to take a different approach, integrating the fact-level decomposition into the model training process. following @xcite , this approach teaches models to make accurate predictions for individual facts without requiring factlevel labels during training. we provide a direct comparison of our system with the system proposed by @xcite , which segments nli hypotheses into spans based on the presence of nouns. this span-level approach requires models to be trained with the atoms inthe-loop, enabling span-level predictions during inference. however, when using generated facts as atoms, we can compare the performance from training with the atoms in-the-loop to using a standard nli model to make the atom-level predictions. unlike @xcite , we also segment the nli premise into atoms rather than the hypothesis, requiring a different framework for both training and inference. we also introduce a range of novel fact generation strategies to avoid missing information in our generated atoms, an issue that is avoided when segmenting instances into spans. ## conclusion we experiment with using llm-generated facts as atoms in atomic inference systems, decomposing each nli premise into a list of facts before making entailment predictions for each fact with the hypothesis. the instance-level predictions then depend entirely on the model's granular predictions about each fact. we find that using a standard nli model to make predictions at a fact level results in worse performance than existing methods. however, when 1) including a multi-stage fact generation process, and 2) incorporating the generated facts during model training, our fact-based approach outperforms existing atomic inference methods. our resulting fglr model makes fact-level predictions and combines them with logical rules, without requiring fact-level labels during training. this results in high-performing, interpretable models that specify exactly which facts are responsible for each model prediction.