Hacker News: Explaining Large Language Models Decisions Using Shapley Values

Source URL: https://arxiv.org/abs/2404.01332
Source: Hacker News
Title: Explaining Large Language Models Decisions Using Shapley Values

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The paper explores the use of Shapley values to interpret decisions made by large language models (LLMs), highlighting how these models can exhibit cognitive biases and “token noise” effects. This work is significant for AI professionals, particularly those focused on improving the interpretability and reliability of LLM outputs in various applications.

Detailed Description: This research contributes to the ongoing discourse on the validity and reliability of large language models (LLMs) as stand-ins for human decision-making. Key points include:

* **Novel Approach**: The study introduces the application of Shapley values from cooperative game theory to provide a structured method to analyze the behavior of LLMs.
* **Token Noise Effects**: The authors uncover “token noise” effects, where LLM outputs are significantly swayed by prompts that contain tokens with little informative value. This insight raises important questions about the stability and applicability of LLM-generated insights.
* **Implications for Human Behavior Simulation**: Given that LLMs are utilized in assessing human behavior, the findings point out the potential misunderstandings that can arise if such models are considered reliable substitutes for real human subjects.
* **Practical Applications**: The model-agnostic nature of the proposed approach allows practitioners to apply this methodology broadly across proprietary LLMs, optimizing prompts to yield more accurate representations of human-like responses.
* **Recommendations**: The paper calls for researchers to report findings that are contingent upon specific prompt templates, advocating for more caution when drawing parallels between LLM behavior and actual human cognition.

This research highlights the importance of interpretability in AI systems, particularly for those working with LLMs in marketing or behavioral analysis. Security and compliance professionals in AI must consider these factors to ensure ethical use and accurate insights derived from AI systems.