3 citations · 8 across the 7 of their papers we have counts for
7 papers
LLMs Can Compensate for Deficiencies in Visual Representations
Sho Takishita, Jay Gala, Abdelrahman Mohamed +2
Many vision-language models (VLMs) that prove very effective at a range of multimodal task, build on CLIP-based vision encoders, which are known to have various limitations. We inv…
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation
Quang P. M. Pham, Khoi T. N. Nguyen, Nhi H. Doan +5
Efficient path planning in robotics, particularly within large-scale, complex environments, remains a significant hurdle. While Large Language Models (LLMs) offer strong reasoning…
How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study
Rendi Chevi, Kentaro Inui, Thamar Solorio +1
What makes an interaction with the LLM more preferable for the user? While it is intuitive to assume that information accuracy in the LLM's responses would be one of the influentia…
Syntactic Learnability of Echo State Neural Language Models at Scale
Ryo Ueda, Tatsuki Kuribayashi, Shunsuke Kando +1
What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability? To explore such a simple but sufficient neural language model, w…
Rectifying Belief Space via Unlearning to Harness LLMs' Reasoning
Ayana Niwa, Masahiro Kaneko, Kentaro Inui
Large language models (LLMs) can exhibit advanced reasoning yet still generate incorrect answers. We hypothesize that such errors frequently stem from spurious beliefs, proposition…
Large Language Models Are Human-Like Internally
Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb +2
Recent cognitive modeling studies have reported that larger language models (LMs) exhibit a poorer fit to human reading behavior (Oh and Schuler, 2023b; Shain et al., 2024; Kuribay…
LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +5
This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning. Specifically, with a particular inter…