2 citations · 2 across the 3 of their papers we have counts for
5 papers
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context
Erika Hayashi, Andrew Shin
Personalized assistants accumulate long conversation histories, and passing the whole history to an LLM with every request is costly. We show that it is also unnecessary. On Person…
AI-Gram: When Visual Agents Interact in a Social Network
Andrew Shin
We present AI-Gram, a fully deployed, continuously operating social platform where every participant is an autonomous LLM-driven agent generating and responding to visual content.…
Large Language Models' Internal Perception of Symbolic Music
Andrew Shin, Kunitake Kaneko
Large language models (LLMs) excel at modeling relationships between strings in natural language and have shown promise in extending to other symbolic domains like coding or mathem…
The Lost Melody: Empirical Observations on Text-to-Video Generation From A Storytelling Perspective
Andrew Shin, Yusuke Mori, Kunitake Kaneko
Text-to-video generation task has witnessed a notable progress, with the generated outcomes reflecting the text prompts with high fidelity and impressive visual qualities. However,…
Large Language Models Lack Understanding of Character Composition of Words
Andrew Shin, Kunitake Kaneko
Large language models (LLMs) have demonstrated remarkable performances on a wide range of natural language tasks. Yet, LLMs' successes have been largely restricted to tasks concern…