6 papers
Can LLMs Take Retrieved Information with a Grain of Salt?
Behzad Shayegh, Mohamed Osama Ahmed, Fred Tung +1
Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to appropriately adapt responses t…
Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation
Behzad Shayegh, Jan-Thorsten Peter, David Vilar +4
We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fal…
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing
Behzad Shayegh, Hobie H. -B. Lee, Xiaodan Zhu +2
We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe t…
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
Yuqiao Wen, Behzad Shayegh, Chenyang Huang +2
The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alt…
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing
Behzad Shayegh, Yuqiao Wen, Lili Mou
We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature. We propose to build an…
Ensemble Distillation for Unsupervised Constituency Parsing
Behzad Shayegh, Yanshuai Cao, Xiaodan Zhu +2
We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data…