LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users (2406.17737v1)

Abstract: While state-of-the-art LLMs have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as hallucinations and bias. In this work, we investigate how the quality of LLM responses changes in terms of information accuracy, truthfulness, and refusals depending on three user traits: English proficiency, education level, and country of origin. We present extensive experimentation on three state-of-the-art LLMs and two different datasets targeting truthfulness and factuality. Our findings suggest that undesirable behaviors in state-of-the-art LLMs occur disproportionately more for users with lower English proficiency, of lower education status, and originating from outside the US, rendering these models unreliable sources of information towards their most vulnerable users.

Targeted Underperformance in LLMs and Its Disproportionate Impact on Vulnerable Users

The paper "LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users" investigates the variability in the performance of LLMs based on user traits such as English proficiency, education level, and country of origin. This research emerges from the extended examination of known pitfalls of LLMs, such as hallucinations and bias, and introduces a nuanced analysis of how these undesirable behaviors manifest unevenly across different demographics.

Methods and Experimental Design

The authors conducted experiments using three state-of-the-art LLMs: GPT-4, Claude Opus, and Llama 3-8B, evaluating their responses using datasets aimed at truthfulness (TruthfulQA) and factuality (SciQ). The experimental design focused on analyzing responses in terms of accuracy, truthfulness, and refusal to answer, influenced by the user's English proficiency, educational background, and nationality.

Multiple user profiles were synthesized, either LLM-generated or adapted from real-world examples, capturing high and low education levels, native and non-native English speakers, and different countries of origin (specifically the USA, Iran, and China). Detailed and controlled prompts simulated user interactions to elicit model responses under varied conditions.

Key Findings

The paper's results are significant and multifaceted:

1. Education Level and Truthfulness: A pronounced decline in model performance was observed with less educated users across all LLMs. The models exhibited a statistically significant underperformance in both factuality and truthfulness, especially in answering adversarial questions designed to test the limits of truthfulness.
2. English Proficiency: Non-native English speakers consistently received less accurate and truthful responses compared to native speakers. Notably, there were significant drops in accuracy on both datasets for GPT-4 and Claude.
3. Country of Origin: Specific national biases were evident, particularly for users from Iran, where Claude exhibited significantly reduced performance. Fewer discrepancies were noted for users from China, and minimal impact was found for those from the US.
4. Refusal Rates and Condescending Responses: Claude uniquely demonstrated a higher rate of refusing to answer questions for certain demographics, coupled with condescending language towards lower-educated and foreign users. This behavior underscores an ingrained bias within the model’s response generation.

Implications of Research

The paper's findings have broad implications on both theoretical and practical levels. Theoretically, it highlights the limitations of current LLM alignment strategies, particularly Reinforcement Learning with Human Feedback (RLHF), in adequately addressing bias. The disparity in model performance underscores the human sociocognitive biases that can permeate even advanced AI systems, emphasizing the need for more robust bias mitigation techniques during both model training and deployment phases.

Practically, the outcomes bring into question the reliability of LLMs as universal tools for information dissemination and user interaction, especially for those from marginalized groups. The identified underperformance means that LLM-driven applications in education, customer service, or any personalization-heavy domains must be carefully scrutinized and refined to avoid propagating inherent biases. Tools like ChatGPT’s memory feature, which personalizes interactions based on user specifics, risk exacerbating these biases if not rigorously evaluated.

Speculation on Future Developments

Future research may focus on developing methods to monitor and dynamically adjust LLM responses to ensure equitable treatment across demographics. This could involve innovative alignment frameworks that go beyond RLHF to include diverse evaluator pools and more sophisticated corrective mechanisms. Moreover, introducing transparency in AI decision-making processes and response generation could offer pathways to better understand and counteract bias.

Given the widespread application of LLMs and their potential to democratize access to information, it is crucial that AI deployment strategies include comprehensive bias assessment protocols. The integration of multi-lingual training data sets, culturally sensitive evaluation metrics, and continuous performance audits across varied demographic interactions are necessary steps towards more inclusive AI.

Conclusion

In conclusion, the paper evidences critical shortcomings in state-of-the-art LLMs that differentially impact vulnerable users based on education, language proficiency, and nationality. As LLMs integrate deeper into societal and educational frameworks, ensuring equity and reliability across all user demographics becomes paramount. This work pioneers a comprehensive assessment framework that lays the groundwork for future studies and practical implementations of bias-mitigated AI systems.