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Xiaofei Ma

AWS AI

19 papers hereh-index 14845 citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author14
  • last author1

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.IR4
  • cs.AI1
  • cs.LG1
  • stat.ML1
affiliations
  • AWS AI
same name
  • Xiaofei Ma — 10 papers, h 8
  • Xiaofei Ma — 2 papers, h 1
  • Xiaofei Ma — 1 paper, h 4
  • Xiaofei Ma — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182024
most citedUniversal Text Representation from BERT: An Empirical Study

40 citations · 101 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CL2022

ContraCLM: Contrastive Learning For Causal Language Model

Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9

Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…

cs.IR2022

Debiasing Neural Retrieval via In-batch Balancing Regularization

Yuantong Li, Xiaokai Wei, Zijian Wang +4

People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair rankin…

cs.CL2022

Learning Dialogue Representations from Consecutive Utterances

Zhihan Zhou, Dejiao Zhang, Wei Xiao +4

Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data sc…

cs.CL2022★ 1 cited

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

Danilo Ribeiro, Shen Wang, Xiaofei Ma +8

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations,…

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