Yen‐Ling Kuo

Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach

2020 · arXiv (Cornell University)

We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to consider the likelihood information of a rollout up to k …

Incorporating Rich Social Interactions Into MDPs

2022 · 2022 International Conference on Robotics and Automation (ICRA)

Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology can be …

Neural Amortized Inference for Nested Multi-agent Reasoning

2023 · arXiv (Cornell University)

Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity …

CaseEdit: Enhancing Localized Commonsense Reasoning via Null-Space Constrained Knowledge Editing in Small Parameter Language Models

2025 · arXiv (Cornell University)

Large language models (LLMs) exhibit strong performance on factual recall and general reasoning but struggle to adapt to user-specific, commonsense knowledge, a challenge particularly acute in small-parameter settings where computational efficiency is prioritized. We introduce …