Exploring Value Biases: How LLMs Deviate Towards the Ideal (2402.11005v2)

Abstract: Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact. Understanding the non-deliberate(ive) mechanism of LLMs in giving responses is essential in explaining their performance and discerning their biases in real-world applications. This is analogous to human studies, where such inadvertent responses are referred to as sampling. We study this sampling of LLMs in light of value bias and show that the sampling of LLMs tends to favour high-value options. Value bias corresponds to this shift of response from the most likely towards an ideal value represented in the LLM. In fact, this effect can be reproduced even with new entities learnt via in-context prompting. We show that this bias manifests in unexpected places and has implications on relevant application scenarios, like choosing exemplars. The results show that value bias is strong in LLMs across different categories, similar to the results found in human studies.

• The paper demonstrates that LLMs inherently deviate toward ideal values, highlighting both implicit and context-learned biases.
• The study employs controlled experiments with GPT-4 to quantify how prototype evaluation shifts from statistical likelihood to ideal exemplars.
• The findings indicate that mitigating value bias is crucial for ensuring neutrality and fairness in AI-driven applications.

Exploring Value Biases in LLMs

Introduction to Value Bias in LLMs

The effectiveness and ubiquitous application of LLMs across various domains necessitate an in-depth understanding of their operational mechanisms, particularly in how they generate responses. This analysis is critical not only from a utility perspective but also to ensure these models operate within secure and unbiased frameworks. Notably, LLMs have demonstrated an inclination towards generating responses that deviate towards an ideal value, indicative of an underlying value bias. This phenomenon is akin to human cognitive biases where the likelihood of certain responses is influenced by inherent value systems. The paper comprehensively explores this value bias by investigating the response tendencies of LLMs towards an ideal value across different scenarios, and its implications on practical applications, such as prototype assessment.

Delving into LLM Value Bias

Implicit and Context-Learned Bias

The research delineates between implicit value biases inherent within LLMs and those acquired from context during the model's operation. Implicit biases are examined through the lens of how LLMs inherently prefer certain "ideal" responses over statistically likely ones, without external input. On the other hand, context-learned biases are studied through the introduction of new, hypothetical scenarios (termed "glubbing") where LLMs are seen to develop a value system based on the context provided, demonstrating a statistically significant deviation towards ideal responses.

Value Bias in Prototypes

Further investigation into the practical implications of value bias highlights its effect on how LLMs evaluate prototypes. Prototypes, in cognitive science, refer to the 'best examples' of a category, combining aspects of both averageness and idealness. The findings suggest that LLMs' judgments on prototypes are not solely based on statistical representation but are biased towards idealized examples.

Experiments and Observations

Through a series of meticulously designed experiments, the paper presents compelling evidence of value bias in LLMs. Using GPT-4 as the primary model for investigation, the studies span across analyzing implicit value biases within a wide array of concepts, evaluating biases for newly introduced contexts, and assessing the influence of value bias in prototype recognition. The results uniformly indicate a departure from purely likelihood-based responses towards responses that lean towards an abstract notion of "ideal" values, thus substantiating the hypothesis of value bias in LLMs.

The Implications of Findings

The revelation of value bias in LLMs harbors both theoretical and practical ramifications. Theoretically, it adds a new dimension to our understanding of LLM operations, suggesting that their response generation is not merely a function of likelihood but also of an embedded value system, which may or may not align with human value judgments. Practically, this bias could influence LLM application outcomes, particularly in areas requiring impartiality and objective judgment. For instance, the skewed prototype evaluation could affect information summarization tasks, leading to outputs that might inadvertently emphasize certain values over others.

Future Directions in AI and LLM Research

Acknowledging the existence and impact of value bias in LLMs paves the way for future explorations aimed at mitigating unwanted biases and aligning LLM outputs closer to desired ethical and objective standards. It opens up avenues for developing mechanisms to adjust or neutralize the inherent value systems in LLMs, especially in critical applications where neutrality and fairness are paramount. Additionally, further research is needed to fully understand the origins of these biases within the training data or the architecture of LLMs and to devise strategies to counteract them without compromising the models' utility and efficiency.

Conclusion

This paper provides a foundational look at the manifestation of value bias in LLMs, elucidating the complexities of LLM response mechanisms beyond simple probability sampling. By uncovering the nuanced ways in which LLM outputs may be influenced by implicit and learned value systems, it invites a broader discourse on the ethical considerations and potential biases inherent in AI technologies. As we continue to integrate LLMs into various sectors, understanding and addressing these biases will be crucial in ensuring that AI tools serve humanity in fair, unbiased, and beneficial ways.