Post Hoc Explanations of Language Models Can Improve Language Models

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities. However, incorporating such rationales poses challenges in terms of scalability as this requires a high degree of human involvement. In this work, we present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges by automating the process of rationale generation. To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs. Extensive experimentation with real-world datasets demonstrates that our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks, including those where prior approaches which rely on human-annotated rationales such as Chain-of-Thought prompting fall short. Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs. Furthermore, we conduct additional empirical analyses and ablation studies to demonstrate the impact of each of the components of AMPLIFY, which, in turn, lead to critical insights for refining in-context learning.

Adaptive Adversarial Training Does Not Increase Recourse Costs

“…An increase in n for n-adversarial training increases the overall recourse costs and the corresponding relation between n and ⇠ is discussed in [14]. In comparison with an n-adversarial training, we observe the following benets from the instance adaptive adversarially training:…”

“…For the scope of this study, we explore three dierent classes [14] of recourse methods: i) one random search, ii) one gradient-based search, and iii) one manifold-based approach. We will now briey discuss each method, and we refer the readers to the original works for further implementation details.…”

“…It is comprised of 20 features which are a combination of demographic features (age, sex, racial group), as well as employment features (hours of work per week and salary), and nancial features (capital gains/losses.) In keeping with [14] and [3], we removed categorical features for ecient training and approximation of tabular adversarial examples. The target distribution is somewhat skewed, with a 76% positive label proportion.…”

“…Both Adversarial Examples and Counterfactual Explanations are formally described as constrained optimization problems where the objective is to alter a model's output by minimally perturbing input features [4,9]. Recent work [17] proved equivalence between certain adversarial attack methods and counterfactual explanation methods, and further work has demonstrated both theoretically and empirically that increasing the radius of attack during adversarial training increases the cost of the resulting recourse [14]. This inherent connection pits security at odds with expressivity and raises an important question as to how an adversarial radius ought to be selected for adversarial training.…”

“…While minimizing the changes necessary to alter a classier's decision is seen as benecial from a recourse perspective, such changes are harmful from a robustness perspective. Research [14] has demonstrated that adversarial training increases the average recourse cost, with higher adversarial training radii corresponding to higher recourse costs, which raises the concern that there may be an inherent trade-o between robustness and recourse.…”