Atomic Inference for NLI with Generated Facts as Atoms (2305.13214v2)

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.

Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms: An Overview

The paper, "Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms," introduces a novel framework designed to improve Natural Language Inference (NLI) by leveraging logical reasoning based on fact generation. This model-agnostic method notably emphasizes interpretability while maintaining or enhancing predictive accuracy, especially across challenging NLI datasets like the Adversarial Natural Language Inference (ANLI) dataset.

Problem Statement and Context

State-of-the-art neural models have demonstrated human-parity performance on various Natural Language Understanding (NLU) tasks. However, these models frequently base their performance on dataset artefacts and biases rather than genuine understanding of the task. Although current interpretability methods can determine influential features, they lack guarantees ensuring these features are responsible for the model's decisions. To resolve this, the authors introduce a logical reasoning framework that can attribute model decisions to specific input information reliably. This approach segments the input data into individual logical units termed "facts" and applies a set of logical rules to achieve inference.

Methodology

The framework deconstructs complex NLI observations into individual logical atoms using a generation-based approach. These generated facts are used as the basis for logical reasoning within the model. The architecture applies a neural model to process each fact separately and assign entailment or contradiction status relative to the hypothesis. The logical reasoning framework aggregates these predictions to make comprehensive decisions on the class of the entire observation.

1. Fact Generation: The system employs LLMs like GPT-3 to extract and compile lists of facts from given premises. These facts represent atomic components of information that are critical in deducing entailment relationships.
2. Logical Framework: The logical component of the proposed model uses fact-level predictions to derive observation-level decisions. Decisions are driven by specific rules: contradictions in any logical atom trigger a contradiction label for the entire premise; likewise, entailment in any atom results in an entailment label, whereas neutral decisions adopt a default response.
3. Performance Evaluation: The framework's efficacy was validated on the ANLI dataset. The model applies particularly well to instances requiring complex reasoning such as causality and commonsense.

Results and Implications

The authors report performance improvements across baseline models including BERT-base, DeBERTa-base, and DeBERTa-large, notably achieving a new state-of-the-art on the ANLI round 3 test set—known for its difficulty. Highlights include:

• A significant performance boost on the ANLI dataset's hardest examples (Round 3), suggesting the logical decomposition is particularly effective on complex inference tasks.
• Superior performance in reduced-data settings, indicating the framework's capability to generalize from minimal training data, a critical aspect for models applied in data-sparse environments.
• Human-aligned fact predictions, even without fact-level training labels, demonstrate the coherent decomposition of facts achieved via the model's reasoning process.

Discussion

FGLR's principal strengths rest in offering interpretability without compromising model performance. The decomposition of premises into logical atoms aligns with human reasoning processes, enabling further insights into model decisions. These findings promote interpretability and reliability across inference tasks, key in applications where decisions must be transparent and trustworthy.

Future Developments

While the current implementation focuses on factual entailment, extending logical reasoning frameworks to abstract and high-dimensional inferences remains unexplored. Further research could integrate reinforcement learning methodologies with logical frameworks, leading to adaptive systems capable of dynamically evolving their inference strategies based on environment data. Additionally, potential expansions could investigate the deployment of logical reasoning across other domains of NLP, such as dialogue systems and question-answering frameworks, where integrating factual verification can enhance outcome validity.

The introduction of FGLR emphasizes the critical balance between interpretability and performance, paving the path for future work that seeks not only to optimize machine intelligence but also to align it closely with human cognitive processes.