Papers by Sarfaroz Yunusov
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are used to create personalized “mirror stories” that reflect and resonate with individual readers’ identities. |
| Approach: | They propose to use Large Language Models to create personalized “mirror stories” that reflect and resonate with individual readers’ identities. |
| Outcome: | The proposed models outperform generic human-written and LLM-generated narratives on all metrics of engagement and textual diversity while preserving the intended moral. |
Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into everyday workflows . a recent study found that LLMs exhibit distinct personality-like traits that affect user engagement . |
| Approach: | They evaluated 32 LLM users for four collaborative tasks and found significant preferences . they found that rationalists preferred GPT-4, while idealists favored Claude 3.5 . |
| Outcome: | The results show that users with different personality traits prefer certain LLMs over others. |
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models (2024.acl-long)
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| Challenge: | Representative bias is a tendency of Large Language Models to generate outputs that mirror the experiences of certain identity groups, and affinity bias is an evaluative preference for specific narratives. |
| Approach: | They propose two new metrics to measure representative bias and affinity bias within large language models and present a new set of tasks designed with customized rubrics to detect these biases. |
| Outcome: | The proposed model identifies representative biases in prominent LLMs, with a preference for identities associated with being white, straight, and men. |