7 citations · 11 across the 8 of their papers we have counts for
9 papers
Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow
Polina Tsvilodub, Andreas Waldis, Linlu Qiu +2
Language models (LMs) are increasingly used for tasks that require reasoning about hidden variables from a few observations, for which Bayesian inference is the normatively correct…
ELF: Embedded Language Flows
Keya Hu, Linlu Qiu, Yiyang Lu +5
Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their success has attracted growing…
Learning User Simulators with Turing Rewards
Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu +5
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and…
DUEL: Adversarial Self-Play for Multimodal Reasoning
Lin Qiu, Hanqing Zeng, Yao Liu +3
Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-based optimization typically d…
Implicit Representations of Grammaticality in Language Models
Yingshan Susan Wang, Linlu Qiu, Zhaofeng Wu +2
Grammaticality and likelihood are distinct notions in human language. Pretrained language models (LMs), which are probabilistic models of language fitted to maximize corpus likelih…
ARC Is a Vision Problem!
Keya Hu, Ali Cy, Linlu Qiu +5
The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a…
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models
Linlu Qiu, Fei Sha, Kelsey Allen +3
Large language models (LLMs) are increasingly used as agents that interact with users and with the world. To do so successfully, LLMs must construct representations of the world an…
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Ekin Akyürek, Mehul Damani, Adam Zweiger +5
Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks even when given a small number…
Learning to Reason via Program Generation, Emulation, and Search
Nathaniel Weir, Muhammad Khalifa, Linlu Qiu +2
Program synthesis with language models (LMs) has unlocked a large set of reasoning abilities; code-tuned LMs have proven adept at generating programs that solve a wide variety of a…