Rationales for Sequential Predictions (2109.06387v2)

Abstract: Sequence models are a critical component of modern NLP systems, but their predictions are difficult to explain. We consider model explanations though rationales, subsets of context that can explain individual model predictions. We find sequential rationales by solving a combinatorial optimization: the best rationale is the smallest subset of input tokens that would predict the same output as the full sequence. Enumerating all subsets is intractable, so we propose an efficient greedy algorithm to approximate this objective. The algorithm, which is called greedy rationalization, applies to any model. For this approach to be effective, the model should form compatible conditional distributions when making predictions on incomplete subsets of the context. This condition can be enforced with a short fine-tuning step. We study greedy rationalization on LLMing and machine translation. Compared to existing baselines, greedy rationalization is best at optimizing the combinatorial objective and provides the most faithful rationales. On a new dataset of annotated sequential rationales, greedy rationales are most similar to human rationales.

Insights into Rationales for Sequential Predictions

The paper "Rationales for Sequential Predictions" presents a novel approach to explaining the predictions of sequence models in NLP by employing rationales, defined as subsets of input tokens instrumental in making predictions. This research addresses the challenge of interpreting complex sequence models, such as those used for LLMing and machine translation, which are notoriously opaque in their decision-making processes.

Introduction to Rationales

Sequence models are vital in various NLP applications, yet they often lack transparency in how they make individual predictions. The paper proposes interpreting model predictions through rationales, which aim to elucidate the essential subset of input tokens that lead to a particular model output. Rationales offer model explanations critical for debugging, validating decisions, and detecting biases.

Combinatorial Optimization for Sequential Rationales

The paper frames the problem of discovering rationales as a combinatorial optimization task, seeking the smallest subset of input tokens that yield the same model prediction as the complete input sequence. It introduces a greedy algorithm—greedy rationalization—to approximate the combinatorial objective efficiently. This greedy algorithm iteratively extends rationales, selecting the context token that most enhances the prediction probability until the rationale is sufficient.

Compatibility and Fine-Tuning

A pivotal assumption for greedy rationalization is that models provide compatible predictions on incomplete context subsets. The authors introduce a fine-tuning method to ensure models can handle incomplete inputs smartly. During fine-tuning, models are exposed to randomly sampled subsets of context, learning to form conditional distributions compatible with predictions on full contexts.

Empirical Evaluations

The paper extensively evaluates greedy rationalization against various gradient- and attention-based explanation methods in LLMing and machine translation tasks. Greedy rationalization consistently offers the most faithful and succinct rationales, aligning closely with human-annotated rationales on newly collected datasets. Importantly, this method outperforms baseline techniques in capturing essential long-range dependencies and minimizing rationale sizes.

Implications and Future Work

The introduction and empirical validation of greedy rationalization for sequence models pave the way for more interpretable NLP systems. Rationales can significantly enhance the transparency of sequence models, making them more reliable for applications requiring justification. This research lays groundwork for further exploration into efficient and effective model interpretability techniques and suggests potential advancements in AI by enhancing model understanding, which could lead to more robust applications in real-world scenarios. Future work may extend this framework to other complex prediction tasks across machine learning disciplines, potentially incorporating advanced optimization strategies to improve rationale accuracy and efficiency further.

In conclusion, "Rationales for Sequential Predictions" provides essential insights into sequence model interpretability, offering promising directions for improving the transparency and trustworthiness of NLP applications.