1 citations · 1 across the 4 of their papers we have counts for
7 papers
The Reward Model Selection Crisis in Personalized Alignment
Fady Rezk, Yuangang Pan, Chuan-Sheng Foo +4
Personalized alignment from preference data has focused primarily on improving personal reward model (RM) accuracy, with the implicit assumption that better preference ranking tran…
Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task
Brady Bhalla, Honglu Fan, Nancy Chen +1
We investigate how embedding dimension affects the emergence of an internal "world model" in a transformer trained with reinforcement learning to perform bubble-sort-style adjacent…
LOCOST: State-Space Models for Long Document Abstractive Summarization
Florian Le Bronnec, Song Duong, Mathieu Ravaut +6
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architec…
Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input
Minh Nguyen, Nancy F. Chen
NLP models excel on tasks with clean inputs, but are less accurate with noisy inputs. In particular, character-level noise such as human-written typos and adversarially-engineered…
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards
Litton J Kurisinkel, Nancy F chen
Memory-efficient large language models are good at refining text input for better readability. However, controllability is a matter of concern when it comes to text generation task…
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction
Litton J Kurisinkel, Nancy F. Chen
Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0.4 among DUC-2004 reference summari…