1 citations · 1 across the 5 of their papers we have counts for
36 papers
Asymmetric Collapse in Model Merging: When Refusal Over- writes Recognition
Aarnav Choudhary, Matheus Fonseca Rocha, Jiwon Seo +2
Model merging is often used to combine capabilities from separately fine-tuned models without additional training, but it is unclear whether standard merging methods preserve multi…
Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi +3
Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narrat…
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
Aayush Aluru, Chloe Ho, Muhammad Hammouri +5
Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form…
Preference Optimization Drives Monoculture in LLM Prediction Markets
James Begin, Brendan Gho, Suman Muppavarapu +6
Prediction markets rest on the independence of participant errors. As LLM agents become active traders on platforms like Kalshi and Polymarket, we ask: does this independence hold…
Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques
Raina Gao, Alyssa Jeong, Lang Xiong +4
Sarcasm is a form of humor where expressions convey meanings opposite to their literal interpretations. Classifying and generating sarcasm using large language models is vital for…
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen +4
Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capa…