Kevin Lin

Text2Motion: From Natural Language Instructions to Feasible Plans

2023 · arXiv (Cornell University)

We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified …

Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration

2024 · arXiv (Cornell University)

The diffusion model, a new generative modeling paradigm, has achieved significant success in generating images, audio, video, and text. It has been adapted for sequence-to-sequence text generation (Seq2Seq) through DiffuSeq, termed S2S Diffusion. Existing S2S-Diffusion …

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

2019

Oyvind Tafjord, Matt Gardner, Kevin Lin, Peter Clark. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

2023 · arXiv (Cornell University)

We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. In this paper, we define and explore a comprehensive list of advanced vision tasks …

Lost in the Middle: How Language Models Use Long Contexts

2024 · Transactions of the Association for Computational Linguistics

Abstract While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks …