Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
(2403.02738)Abstract
Despite the significant achievements of existing prompting methods such as in-context learning and chain-of-thought for LLMs, they still face challenges of various biases. Traditional debiasing methods primarily focus on the model training stage, including data augmentation-based and reweight-based approaches, with the limitations of addressing the complex biases of LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate the bias of LLMs. In specific, causal intervention is implemented by designing the prompts without accessing the parameters and logits of LLMs.The chain-of-thoughts generated by LLMs are employed as the mediator variable and the causal effect between the input prompt and the output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to obtain the representation of the samples precisely and estimate the causal effect more accurately, contrastive learning is used to fine-tune the encoder of the samples by aligning the space of the encoder with the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance on 3 natural language processing datasets on both open-source and closed-source LLMs.
Overview
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This paper introduces 'Causal Prompting', a novel method to debias LLMs using causal inference, specifically front-door adjustment, during the prompt design process.
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Causal Prompting employs a two-stage process involving estimation of causal effects using chain-of-thoughts (CoT) as mediator variables and leveraging NWGM approximation and contrastive learning to fine-tune the process.
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The method has been experimentally shown to improve performance across various NLP tasks and LLM architectures, indicating its promise as a scalable, model-agnostic debiasing strategy.
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The paper suggests future directions for refining Causal Prompting and explores its application to a broader spectrum of tasks, architectures, and the incorporation of additional causal inference techniques.
Causal Prompting: A New Debiasing Method for Large Language Model Prompts Using Front-Door Adjustment
Introduction
The influence of bias in LLMs has shown to be a significant challenge, impacting the reliability of outputs across various NLP tasks. Traditional efforts to debias LLMs during the model training phase via data augmentation or reweighting strategies have faced limitations, particularly in handling the complex, multifaceted nature of bias within these models. This paper proposes a novel approach named "Causal Prompting" that utilizes causal inference—specifically front-door adjustment—to mitigate bias by intervening in the prompt design process without requiring direct access to LLM parameters or output logits.
Debiasing Through Causal Inference
Causal inference offers a robust framework for understanding the relationships between variables within a system. Specifically, this approach leverages the concept of front-door adjustment, enabling the estimation of the causal effect between an input prompt (treatment) and the model's output (outcome) without necessitating the manipulation or direct measurement of confounding variables (unobservable biases in this context). By identifying and utilizing chain-of-thoughts (CoT) generated by LLMs as a mediator variable, Causal Prompting provides a structured method to estimate and mitigate the biasing effects of unobserved confounders.
Methodology
The Causal Prompting approach encompasses two critical stages to estimate and adjust for biased causal effects in prompting LLMs:
- Estimation of $P(r|do(X))$: This phase aims to estimate the causal effect between the input prompt and the CoT. It employs self-consistency and a clustering algorithm where multiple CoTs generated by the LLM are clustered. The center of each cluster acts as the representative CoT with its probability estimated based on cluster size. This step effectively factors in the variations within the CoTs to select those most reflective of unbiased reasoning paths.
- Estimation of $P(A|do(r))$: The second stage calculates the causal effect between the CoT and the final answer. By leveraging an NWGM (normalized weighted geometric mean) approximation, ICL (In-Context Learning) demonstrations are selected based on their relevance to the CoT, serving as a proxy for rigorous counterfactual analysis. This approximation seeks to represent the entire data distribution and, in turn, guide the LLM towards generating unbiased answers.
Furthermore, the methodology incorporates contrastive learning to fine-tune the encoder, aligning the representation space of samples with that of LLMs. This alignment is crucial for accurately estimating causal effects and enhancing the overall debiasing process.
Experimental Results
The efficacy of Causal Prompting was evaluated across three distinct NLP tasks (Aspect-based Sentiment Analysis, Natural Language Inference, and Fact Verification) using both open-source and closed-source LLMs. The approach not only showed significant improvements in performance across adversarial datasets but also demonstrated its applicability across different model architectures.
Implications and Future Directions
The introduction of Causal Prompting presents a scalable, model-agnostic strategy for debiasing LLMs, potentially revolutionizing the way biases are addressed in AI systems. The method's reliance on causal inference, particularly front-door adjustment, fills a critical gap in current debiasing practices, moving beyond the limitations of direct manipulation of training data or model parameters.
Future work may explore the application of Causal Prompting across a wider range of tasks, LLM architectures, and languages. Additionally, further refinement of the methodology, including optimization of the NWGM approximation and clustering mechanisms, could enhance its effectiveness and efficiency. The exploration of other causal inference techniques within the prompting context also presents an exciting avenue for research, potentially unveiling new strategies for mitigating bias in AI.
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