Explaining black-box model behavior with natural language has achieved
impressive results in various NLP tasks. Recent research has explored the
utilization of subsequences from the input text as a rationale, providing users
with evidence to support the model decision. Although existing frameworks excel
in generating high-quality rationales while achieving high task performance,
they neglect to account for the unreliable link between the generated rationale
and model decision. In simpler terms, a model may make correct decisions while
attributing wrong rationales, or make poor decisions while attributing correct
rationales. To mitigate this issue, we propose a unified two-stage framework
known as Self-Attribution and Decision-Making (SADM). Through extensive
experiments on five reasoning datasets from the ERASER benchmark, we
demonstrate that our framework not only establishes a more reliable link
between the generated rationale and model decision but also achieves
competitive results in task performance and the quality of rationale.
Furthermore, we explore the potential of our framework in semi-supervised
scenarios.

自我归因与决策 (SADM) 是一个统一的两阶段框架，它不仅建立了生成的解释与模型决策之间更可靠的联系，而且在任务性能和解释质量方面取得了竞争性结果。

令您的决策有说服力！一个统一的两阶段框架：自我归属和决策

Make Your Decision Convincing! A Unified Two-Stage Framework:  Self-Attribution and Decision-Making

Counterfactual explanations have emerged as a promising method for
elucidating the behavior of opaque black-box models. Recently, several works
leveraged pixel-space diffusion models for counterfactual generation. To handle
noisy, adversarial gradients during counterfactual generation -- causing
unrealistic artifacts or mere adversarial perturbations -- they required either
auxiliary adversarially robust models or computationally intensive guidance
schemes. However, such requirements limit their applicability, e.g., in
scenarios with restricted access to the model's training data. To address these
limitations, we introduce Latent Diffusion Counterfactual Explanations (LDCE).
LDCE harnesses the capabilities of recent class- or text-conditional foundation
latent diffusion models to expedite counterfactual generation and focus on the
important, semantic parts of the data. Furthermore, we propose a novel
consensus guidance mechanism to filter out noisy, adversarial gradients that
are misaligned with the diffusion model's implicit classifier. We demonstrate
the versatility of LDCE across a wide spectrum of models trained on diverse
datasets with different learning paradigms. Finally, we showcase how LDCE can
provide insights into model errors, enhancing our understanding of black-box
model behavior.

基于潜在扩散模型，引入了潜在扩散反事实解释 (LDCE)，用以快速生成反事实实例，并专注于数据的重要、语义部分；通过新颖的共识引导机制过滤出与扩散模型的隐式分类器不一致的嘈杂、对抗性梯度，展示了 LDCE 在各种学习范式下的多样性和黑盒模型行为的理解。