Interpretability with Contrastive Explanations in LLMs
The paper explores the interpretability of neural LLMs (LMs) by employing contrastive explanations, focusing on providing more granular insights into the decision-making process of these models, particularly in language generation tasks. Traditional interpretability approaches often collapse evidence into a unified explanation, which can be insufficient for complex LLMs. This research presents methods that elucidate why a LLM chooses one token over another, facilitating a clearer understanding of language phenomena and highlighting linguistic features used by the model in its predictions.
Key Contributions and Methodology
This paper introduces a novel approach to LLM interpretability through contrastive explanations. Building on existing interpretability techniques like gradient-based saliency maps, the paper extends these methods to compare alternative model outputs. The primary focus is on identifying salient input features that inform the model's decision to select one token instead of potential alternatives. The methodology involves several steps:
- Selection and Extension of Interpretability Methods: The research adapts three existing interpretability techniques—gradient norm, gradient × input, and input erasure—to the contrastive explanation setting. Each method targets a different aspect of the input to quantify its saliency in altering the prediction from one output token to another.
- Linguistic Phenomena Evaluation: The paper leverages the BLIMP dataset, containing minimal pair sentences to evaluate linguistic phenomena like anaphor agreement, argument structure, and subject-verb agreement. By assessing the alignment between the known evidence for these phenomena and the contrastive explanations, the paper quantifies the effectiveness of these methods.
- Human Simulatability Study: To evaluate how well these explanations facilitate understanding of model behavior, a user paper is conducted. Participants predict the LM's outputs with and without explanations, with results indicating a higher simulation accuracy when contrastive explanations are provided.
- Cluster Analysis for Model Decisions: The paper employs clustering on the explanations to discern the input features that the LLMs rely upon across different linguistic phenomena. This helps in understanding the contextual dependencies models use for various grammatical distinctions.
Results and Implications
The paper presents empirical evidence supporting the superiority of contrastive explanations over non-contrastive counterparts across various metrics. Specifically, the alignment of contrastive explanations with linguistic evidence shows higher accuracy. Moreover, contrastive explanations significantly enhance the participants’ ability to simulate model behavior, suggesting that these methods improve comprehension of complex LLM predictions.
The cluster analysis reveals that linguistic distinctions can be mapped to specific clusters of contextual cues that the models use to make predictions. For example, gender-neutral pronoun decisions are affected by gender-specific terms within the input context, illustrating a nuanced understanding of language that can be somewhat obscured by non-contrastive methods.
Theoretical and Practical Implications
The research underscores the importance of interpretability in enhancing the transparency and reliability of neural LLMs. Practically, contrastive explanations can be instrumental in refining LLMs by identifying where models rely heavily on contextually inappropriate cues, thus aiding in the development of more robust and human-like language processing systems. Theoretically, the methods provide a pathway to uncover broader linguistic patterns and model biases, potentially guiding future innovations in model architecture and interpretability.
Future Directions
The paper proposes that future research extend contrastive explanations to other machine learning models and tasks beyond LLMing, such as machine translation. Further work could refine contrastive interpretability techniques or devise complementary methods to enhance the understandability and effectiveness of LLMs across different linguistic tasks.
In conclusion, the paper presents meaningful advancements in the interpretability of LLMs through contrastive explanations, facilitating deeper insights into model behavior and aligning computational predictions more closely with human linguistic intuition.