A Causal Explainable Guardrails for Large Language Models
- URL: http://arxiv.org/abs/2405.04160v1
- Date: Tue, 7 May 2024 09:55:05 GMT
- Title: A Causal Explainable Guardrails for Large Language Models
- Authors: Zhixuan Chu, Yan Wang, Longfei Li, Zhibo Wang, Zhan Qin, Kui Ren,
- Abstract summary: Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases.
Existing methods for steering LLMs towards desired attributes often assume unbiased representations and rely solely on steering prompts.
We propose LLMGuardaril, a novel framework that incorporates causal analysis and adversarial learning to obtain unbiased steering representations.
- Score: 29.441292837667415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steering LLMs towards desired attributes often assume unbiased representations and rely solely on steering prompts. However, the representations learned from pre-training can introduce semantic biases that influence the steering process, leading to suboptimal results. We propose LLMGuardaril, a novel framework that incorporates causal analysis and adversarial learning to obtain unbiased steering representations in LLMs. LLMGuardaril systematically identifies and blocks the confounding effects of biases, enabling the extraction of unbiased steering representations. Additionally, it includes an explainable component that provides insights into the alignment between the generated output and the desired direction. Experiments demonstrate LLMGuardaril's effectiveness in steering LLMs towards desired attributes while mitigating biases. Our work contributes to the development of safe and reliable LLMs that align with desired attributes. We discuss the limitations and future research directions, highlighting the need for ongoing research to address the ethical implications of large language models.
Related papers
- Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
We show that prompting-based rationales align better with human-annotated rationales than attribution-based rationales.
We additionally find that the faithfulness limitations of prompting-based methods, which are identified in previous work, may be linked to their collapsed predictions.
arXiv Detail & Related papers (2024-06-28T20:06:30Z) - UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation [12.04811490937078]
We investigate how feedforward neural networks (FFNs) and attention heads result in the bias of large language models (LLMs)
To mitigate these biases, we introduce UniBias, an inference-only method that effectively identifies and eliminates biased FFN vectors and attention heads.
arXiv Detail & Related papers (2024-05-31T03:59:15Z) - The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM)
We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions.
Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
arXiv Detail & Related papers (2024-03-25T19:07:32Z) - Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing
Framework [20.753141804841]
Large language models (LLMs) can easily generate biased and discriminative responses.
This paper focuses on social bias, tackling the association between demographic information and LLM outputs.
arXiv Detail & Related papers (2024-03-13T17:46:28Z) - Debiasing Multimodal Large Language Models [61.6896704217147]
Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing.
Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image.
To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
arXiv Detail & Related papers (2024-03-08T12:35:07Z) - Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment [32.12998469814097]
A novel causal prompting method based on front-door adjustment is proposed to effectively mitigate Large Language Models (LLMs) biases.
Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets.
arXiv Detail & Related papers (2024-03-05T07:47:34Z) - Learning to Generate Explainable Stock Predictions using Self-Reflective
Large Language Models [54.21695754082441]
We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions.
A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations.
Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
arXiv Detail & Related papers (2024-02-06T03:18:58Z) - Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation.
We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process.
We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
arXiv Detail & Related papers (2023-11-15T00:02:25Z) - Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools.
Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions.
Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
arXiv Detail & Related papers (2023-09-20T09:23:46Z) - Prompting Large Language Models for Counterfactual Generation: An
Empirical Study [13.506528217009507]
Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks.
We present a comprehensive evaluation framework on various types of NLU tasks, which covers all key factors in determining LLMs' capability of generating counterfactuals.
arXiv Detail & Related papers (2023-05-24T06:44:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.