39 citations · 105 across the 16 of their papers we have counts for
9 papers · 1 filter
A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, Ashutosh Baheti, Jonathan Chang +13
Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise…
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation
Jiachen Zhao, Wenlong Zhao, Andrew Drozdov +5
We study semi-supervised sequence generation tasks, where the few labeled examples are too scarce to finetune a model, and meanwhile, few-shot prompted large language models (LLMs)…
KNN-LM Does Not Improve Open-ended Text Generation
Shufan Wang, Yixiao Song, Andrew Drozdov +3
In this paper, we study the generation quality of interpolation-based retrieval-augmented language models (LMs). These methods, best exemplified by the KNN-LM, interpolate the LM's…
You can't pick your neighbors, or can you? When and how to rely on retrieval in the NN-LM
Andrew Drozdov, Shufan Wang, Razieh Rahimi +3
Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvement…
Compositional Semantic Parsing with Large Language Models
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek +5
Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artifici…
Inducing and Using Alignments for Transition-based AMR Parsing
Andrew Drozdov, Jiawei Zhou, Radu Florian +4
Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a compl…
Improved Latent Tree Induction with Distant Supervision via Span Constraints
Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee +6
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems stil…
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Andrew Drozdov, Pat Verga, Mohit Yadav +2
We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents with…
Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov, Samuel R. Bowman
Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the se…