SaGE: Evaluating Moral Consistency in Large Language Models

Core Concepts

Large Language Models (LLMs) exhibit moral inconsistency, highlighting the need for improved evaluation methodologies.

Abstract

最近の進歩にもかかわらず、最先端のLLM（Large Language Models）はその生成物に道徳的な一貫性が欠けており、信頼性を疑問視しています。従来のLLM評価においては、特定のタスクでの正確さを測定するためのグラウンドトゥルースデータの開発に焦点が当てられてきました。しかし、普遍的に合意された回答が欠如する道徳的シナリオでは、モデルの応答の一貫性が信頼性にとって重要となります。この問題に対処するため、我々は「Rules of Thumb」（RoTs）と呼ばれる情報理論的尺度であるSemantic Graph Entropy（SaGE）を提案しました。これは、モデルが学習した抽象原則であり、それらの意思決定戦略を効果的に説明することができるものです。さらに、SaGEの汎用性を示すために、TruthfulQAとHellaSwagという2つの人気データセット上でLLMの一貫性を調査しました。結果から分かるように、タスク精度と一貫性は独立した問題であり、これらの問題をさらに探求する必要があることが示唆されます。

Stats

我々は50K個の道徳的な質問やそれに対するLLMs（Large Language Models）から得られた回答、およびこれらモデルが従ったRoTs（Rules of Thumb）を含むMoral Consistency Corpus（MCC）を構築しました。
我々は11種類のSOTA LLMs（State-of-the-Art Large Language Models）をMCC上で評価しました。最高得点は0.681であり、LLMsは道徳的シナリオで不一致であることを示しています。
人間アノテーション実験ではKrippendorff's αスコア0.868を観察しました。
SaGEスコアは人間判断と有意な相関関係があります。
温度変化によるサンプリング方法では一貫性へ影響しないことが示されました。
TruthfulQAおよびHellaSwag上で行われた実験ではタスク精度と一貫性間に相関関係は見られませんでした。
RoTsを使用したプロンプト付き実験では明確な一貫性向上が観察されました。

Quotes

"Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability."
"As LLMs have grown in scale and capability, the spectrum of potential social risks they present has also broadened."
"Moral consistency is widely acknowledged in psychology and ethics. However, its importance in the NLP community is yet to be established."

Key Insights Distilled From

SaGE

by Vamshi Krish... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2402.13709.pdf

Deeper Inquiries

How can the evaluation methodologies for Large Language Models (LLMs) be further improved to address moral consistency issues?

To enhance the evaluation methodologies for LLMs and tackle moral consistency issues, several key improvements can be implemented:

Diverse Dataset Construction: Ensure datasets used for evaluation cover a wide range of moral scenarios with varying degrees of complexity. Incorporate diverse perspectives and ethical frameworks to capture a comprehensive understanding of morality.

Human Annotation Validation: Implement robust human annotation processes with multiple annotators to ensure reliability and reduce bias in evaluating model responses. Use consensus-based approaches to determine ground truth labels where applicable.

Incorporation of Normative Ethics: Integrate principles from normative ethics into the evaluation process, such as deontological or consequentialist theories, to provide a structured framework for assessing moral judgments made by LLMs.

RoT Integration: Expand the use of Rules of Thumb (RoTs) beyond measuring consistency by incorporating them into training objectives for LLMs. By explicitly teaching models these fundamental guidelines during training, they may exhibit more consistent behavior in generating morally aligned responses.

Semantic Graph Analysis: Further develop semantic graph analysis techniques like Semantic Graph Entropy (SaGE) to quantify not only consistency but also depth and breadth of ethical reasoning exhibited by LLMs across different contexts.

Interdisciplinary Collaboration: Foster collaboration between experts in AI ethics, philosophy, psychology, and linguistics to design holistic evaluation frameworks that consider both technical capabilities and ethical implications of LLMs' outputs.

How can the concept of "Rules of Thumb" (RoTs) be applied beyond evaluating moral consistency in LLMs?

The concept of Rules of Thumb (RoTs) can have broader applications beyond evaluating moral consistency in Large Language Models:

Decision-Making Frameworks: RoTs can serve as decision-making heuristics guiding AI systems on various tasks beyond morality, such as problem-solving strategies or task prioritization based on predefined rules learned during training.

Bias Mitigation: RoTs could be utilized as guardrails against biased outcomes by instructing models on fair treatment criteria or promoting diversity awareness when generating content related to sensitive topics like gender identity or cultural heritage.

Explainability Enhancement: Integrating RoTs into model inference processes can improve explainability by providing transparent insights into how decisions are made based on underlying principles rather than black-box algorithms alone.

4Consistency Across Domains: Applying RoTs consistently across different domains enables models to maintain coherence in their responses regardless of input variations or contextual changes, enhancing overall reliability and trustworthiness.
5**Personalized Recommendations: Leveraging individual-specific RoTs tailored towards user preferences allows AI systems like recommendation engines to offer personalized suggestions aligned with users' values and interests while ensuring ethical considerations are met.