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Intervention Lens: from Representation Surgery to String Counterfactuals (2402.11355v4)

Published 17 Feb 2024 in cs.CL, cs.CY, and cs.LG

Abstract: Interventions targeting the representation space of LLMs (LMs) have emerged as an effective means to influence model behavior. Such methods are employed, for example, to eliminate or alter the encoding of demographic information such as gender within the model's representations and, in so doing, create a counterfactual representation. However, because the intervention operates within the representation space, understanding precisely what aspects of the text it modifies poses a challenge. In this paper, we give a method to convert representation counterfactuals into string counterfactuals. We demonstrate that this approach enables us to analyze the linguistic alterations corresponding to a given representation space intervention and to interpret the features utilized to encode a specific concept. Moreover, the resulting counterfactuals can be used to mitigate bias in classification through data augmentation.

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Authors (4)
  1. Matan Avitan (2 papers)
  2. Ryan Cotterell (226 papers)
  3. Yoav Goldberg (142 papers)
  4. Shauli Ravfogel (38 papers)

Summary

  • The paper introduces a novel method that generates string counterfactuals by intervening in the representation space to mitigate biases like gender.
  • It leverages iterative representation inversion using techniques such as LEACE and MiMiC to convert latent modifications into coherent text.
  • Empirical results on the BiasInBios dataset show that augmented classifiers reduce gender bias, demonstrating the method's practical utility.

Natural Language Counterfactuals through Representation Surgery

The paper "Natural Language Counterfactuals through Representation Surgery" by Avitan et al. investigates a novel technique to generate string counterfactuals from interventions in the representation space of LLMs (LMs). The primary motivation lies in the need to understand and control the behavior of LMs by intervening in their representation space, particularly to tackle and mitigate biases, such as those related to demographic information like gender.

Overview

This research addresses the challenge of translating representation space interventions into natural language string counterfactuals. Representation surgery is a suite of techniques that intervene in the encoding of specific semantic concepts within a model's representation. These interventions are often used to erase or alter the representation of certain attributes, such as gender, to mitigate biased model behavior and understand concept encoding within the model.

Methodology

The authors introduce a method to derive string counterfactuals by leveraging interventions applied in the representation space. The approach builds upon the inversion technique proposed by Morris et al. (2023), which approximates the inverse mapping from representations back to text. The method involves the following steps:

  1. Intervention in Representation Space: Use techniques such as LEACE or MiMiC to modify the representation in the model’s latent space. For instance, to achieve gender neutrality or reverse gender encodings in representations.
  2. Representation Inversion: Utilizing an iterative correction-based approach, the altered representations are incrementally projected back into the textual domain, generating counterfactual strings that reflect the changes encoded in the intervened representation.
  3. Counterfactual Generation: The resultant string counterfactuals serve twin purposes. Firstly, they offer insights into the representational changes corresponding to specific interventions, providing meta-interpretability. Secondly, they are employed for data augmentation to mitigate unfair bias in classification tasks.

Experiments and Results

The experiments focus on the BiasInBios dataset, which features short biographies annotated with gender and profession. The generated counterfactuals are evaluated for their efficacy in bias mitigation in profession classification. The classifiers trained with augmented datasets, consisting of both the original and counterfactual samples, exhibited reduced gender bias, demonstrating the practical utility of these counterfactuals in enhancing model fairness.

Qualitative and quantitative analyses of the generated counterfactuals highlight several noteworthy findings:

  • Pronoun Shift: Significant changes were observed in pronoun usage as an expected outcome of gender-oriented interventions, affecting terms like “he” vs. “she” or “his” vs. “her”.
  • Content Word Frequency: Changes in the frequency of words associated with professional or stereotypical contexts post-intervention were noted, which aligns with attempts to uncover latent gender biases encoded within the models.
  • Human and Automatic Evaluations: Human annotation indicated successful gender flips, especially for MiMiC and MiMiC+ interventions, and maintained satisfactory text quality. Automated perplexity assessment provided additional evidence of the method's robust performance in generating coherent text.

Implications and Future Directions

The theoretical implication of this work is profound as it bridges representation interventions and natural language, providing a valuable tool for bias analysis in NLP models. Practically, this advancement plays a crucial role in developing strategies to counteract inherent biases in AI systems, thus promoting fairer and more ethical AI applications.

Future research could expand beyond binary attribute values to more nuanced, multi-dimensional demographic traits, and explore the scalability and adaptability of this approach across diverse domains and languages. Enhanced methods to refine and assess the quality of counterfactual inversions can further augment its application scope, potentially providing richer explanations and interpretability within AI models used in decision-making processes.

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