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No Single Best Model for Diversity: Learning a Router for Sample Diversity
Yuhan Liu, Fangyuan Xu, Vishakh Padmakumar +2
When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide range of users. In this paper, we s…
Evaluating the Diversity and Quality of LLM Generated Content
Alexander Shypula, Shuo Li, Botong Zhang +3
Recent work suggests that preference-tuning techniques -- such as Reinforcement Learning from Human Feedback (RLHF) methods like PPO and GRPO, as well as alternatives like DPO -- r…
LiteraryTaste: A Preference Dataset for Creative Writing Personalization
John Joon Young Chung, Vishakh Padmakumar, Melissa Roemmele +7
People have different creative writing preferences, and large language models (LLMs) for these tasks can benefit from adapting to each user's preferences. However, these models are…
Intent-Aware Schema Generation And Refinement For Literature Review Tables
Vishakh Padmakumar, Joseph Chee Chang, Kyle Lo +2
The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can suppor…
Measuring LLM Novelty As The Frontier Of Original And High-Quality Output
Vishakh Padmakumar, Chen Yueh-Han, Jane Pan +2
As large language models (LLMs) are increasingly used for ideation and scientific discovery, it is important to evaluate their ability to generate novel output. Prior work evaluate…
Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas
Nishant Balepur, Vishakh Padmakumar, Fumeng Yang +3
LLMs are aligned to follow input instructions by learning which of two responses users prefer for a prompt. However, such preference data do not convey why users prefer responses t…