SaGE: Evaluating Moral Consistency in Large Language Models
(2402.13709)Abstract
Despite recent advancements showcasing the impressive capabilities of LLMs in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and trustworthiness in general). Prior works in LLM evaluation focus on developing ground-truth data to measure accuracy on specific tasks. However, for moral scenarios that often lack universally agreed-upon answers, consistency in model responses becomes crucial for their reliability. To address this issue, we propose an information-theoretic measure called Semantic Graph Entropy (SaGE), grounded in the concept of "Rules of Thumb" (RoTs) to measure a model's moral consistency. RoTs are abstract principles learned by a model and can help explain their decision-making strategies effectively. To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed. Furthermore, to illustrate the generalizability of SaGE, we use it to investigate LLM consistency on two popular datasets -- TruthfulQA and HellaSwag. Our results reveal that task-accuracy and consistency are independent problems, and there is a dire need to investigate these issues further.
Overview
-
The paper introduces a framework to assess the moral consistency of LLMs using a novel metric called Semantic Graph Entropy (SaGE), which quantifies their capacity to maintain non-contradictory moral values across similar situations.
-
It presents a Moral Consistency Corpus (MCC) containing 50,000 moral questions with LLM-generated responses and Rules of Thumb (RoTs) to evaluate LLM responses for moral consistency.
-
Findings show that current LLMs exhibit notable moral inconsistency, highlighting the ineffectiveness of traditional methods like temperature-based sampling in enhancing consistency.
-
The research suggests incorporating RoTs explicitly in response generation as a potential method to improve LLM consistency and advocates for the development of model architectures and training paradigms that prioritize ethical alignment.
Evaluating the Moral Consistency of LLMs with Semantic Graph Entropy
Introduction
LLMs have become integral components in AI-driven applications, offering impressive capabilities in conversational systems and beyond. However, the reliability and trustworthiness of these models are under scrutiny, especially concerning their moral consistency. It is crucial for LLMs to generate responses that are not only accurate but also consistent with moral principles across various contexts. In light of this, our discussion revolves around a novel framework intended to assess the moral consistency of LLMs. Utilizing the concept of Rules of Thumb (RoTs) and introducing an information-theoretic measure known as Semantic Graph Entropy (SaGE), this framework endeavors to quantify the ability of LLMs to maintain non-contradictory moral values in semantically similar situations.
Moral Consistency: A Crucial Evaluation Dimension
Moral consistency pertains to an entity's ability to uphold consistent moral values across differing scenarios. For LLMs, exhibiting moral inconsistency catalyzes issues concerning user trust and potential misuse. To bridge this research gap, we introduce the Moral Consistency Corpus (MCC), constituting 50,000 moral questions and the corresponding LLM-generated responses and RoTs. Furthermore, we present the Semantic Graph Entropy (SaGE) metric, which leverages the structural and semantic information within responses to assess consistency.
Semantic Graph Entropy (SaGE): Innovating Evaluation Metrics
SaGE represents an innovative step forward in the evaluation of LLMs' moral consistency. By constructing semantic graphs from RoTs and analyzing their entropy, SaGE provides a nuanced measure of consistency. Preliminary findings indicate that state-of-the-art LLMs exhibit notable moral inconsistency, underscoring a critical area for future research and model development. Interestingly, our analysis also reveals that conventional methods like temperature-based sampling are ineffective at enhancing consistency, suggesting the need for fundamentally different approaches.
Practical Implications and Future Horizons
Our examination extends beyond moral consistency to encompass other cognitive tasks, such as commonsense reasoning and truthful question-answering. A distinct lack of correlation between task accuracy and consistency emphasizes the independent nature of these challenges, advocating for more holistic evaluation frameworks. Encouragingly, preliminary investigations suggest the potential to improve LLM consistency by explicitly incorporating RoTs into response generation. This finding paves the way for more robust and ethically aligned model training methodologies.
Ethical Considerations and Limitations
The ethical dimension of this research merits careful consideration, especially in the generation and use of moral guidelines (RoTs). Our approach is descriptive, aiming to evaluate consistency without making normative judgments on the correctness of the RoTs themselves. Furthermore, the reliance on various NLP tools and models introduces inherited limitations, alongside the computational constraints that bounded our experiments to a selection of 11 LLMs and a restricted number of paraphrases.
Concluding Thoughts
The fidelity of LLMs in moral scenarios is paramount for their trustworthiness and effective real-world deployment. Our introduction of the Semantic Graph Entropy metric and the Moral Consistency Corpus establishes foundational steps toward more rigorous evaluation and development of morally consistent LLMs. Looking ahead, this research underscores the urgent need for innovative model architectures and training paradigms that inherently prioritize moral consistency, ensuring that AI technologies advance in alignment with ethical principles and human values.
Create an account to read this summary for free: