23 citations · 46 across the 18 of their papers we have counts for
6 papers · 1 filter
Formally Specifying the High-Level Behavior of LLM-Based Agents
Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo +7
Autonomous, goal-driven agents powered by LLMs have recently emerged as promising tools for solving challenging problems without the need for task-specific finetuned models that ca…
Compositional Program Generation for Few-Shot Systematic Generalization
Tim Klinger, Luke Liu, Soham Dan +3
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Neural machine learning models, including the now ubiqui…
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan +9
With the growing interest in large language models, the need for evaluating the quality of machine text compared to reference (typically human-generated) text has become focal atte…
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
Maxwell Crouse, Ramon Astudillo, Tahira Naseem +4
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as…
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse +5
Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the…
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing
Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury +4
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across…