Cited by 28Reinforcement Learning in RoboticsAdvanced Bandit Algorithms ResearchNatural Language Processing Techniques

Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook

Open access
Lin, Baihan·2024·Expert Systems with Applications

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2023.122254

Cites

186 of 186 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Such frameworks are crucial in ensuring AI systems are built with core emphasis on ethical considerations, fairness, and transparency, addressing bias mitigation, privacy and security, and AI ethics for accountability.

    “Such frameworks are crucial in ensuring AI systems are built with a core emphasis on ethical considerations, fairness, and transparency, as they address critical aspects such as bias mitigation for fairness, privacy and security concerns for data protection, and AI ethics for accountability, thus responding to the evolving landscape where accountability in AI is of paramount importance [214], [15]”
  • Ethical considerations and legal compliance have become increasingly crucial as AI technologies evolve and integrate into sectors, requiring focus on developing 'Ethical AI Frameworks'.

    “As AI technologies rapidly evolve and become more integrated into various sectors, ethical considerations and legal compliance have become increasingly crucial, which requires a focus on developing 'Ethical AI Frameworks', a new category in our taxonomy reflecting the trend towards responsible AI development in generative AI [214], [215], [15], [216], [217].”
  • Adoption of these ethical initiatives remains uneven, highlighting ongoing need for comprehensive and consistent ethical practices in AI development and deployment.

    “However, the adoption of these initiatives remains uneven, highlighting the ongoing need for comprehensive and consistent ethical practices in AI development and deployment [214].”
  • AI ethics focuses on fairness, accountability, and societal impact, addresses ethical challenges posed by AI's increasing complexity and potential misalignment with human values, and requires ethical governance frameworks, multidisciplinary collaborations, and technological solutions.

    “The field of AI ethics focuses on fairness, accountability, and societal impact, addresses the surge in ethical challenges posed by AI's increasing complexity and potential misalignment with human values, and requires ethical governance frameworks, multidisciplinary collaborations, and technological solutions [214], [235], [15], [236].”

Cites

Flagged first, then by citations

1 of the 187 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Lin, B.
Affiliations
Columbia University (US)
Corresponding author
Lin, Baihan
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2024-03
Volume
238
Pages
122254
ISSN
0957-4174 (ISSN-L)
OpenAlex
W4387807193
Cited by
28
References
187
Citations by year
2026: 3 · 2025: 11 · 2024: 12 · 2023: 2
FWCI
2.64
Type
journal article
Language
en
Open access
Locations
  • Expert Systems with Applications (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
reinforcement learning, multi-armed bandits, speech processing, natural language processing, large language models, deep neural network training, adaptive models, interactive model, scalable models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review