PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 15, 20240 citationsOpen Access

Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models

View Full Paper

Authors

KHKang HeThermal Power Research InstituteYLYinghan LongPurdue University West LafayetteKRKaushik RoyCommonwealth Scientific and Industrial Research Organisation

Discussion

Loading...

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

He et al. (2024) studied this question.

synapsesocial.com/papers/68e79181b6db643587702ff4https://doi.org/10.48550/arxiv.2402.10353
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment2024 · 5 citations
  2. 2Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction2024 · 2 citations
  3. 3Beyond Performance: Quantifying and Mitigating Label Bias in LLMs2024
  4. 4Evaluating the Efficacy of Prompting Techniques for Debiasing Language Model Outputs (Student Abstract)2024 · 1 citations
  5. 5Bias Mitigation in Large Language Models for Tabular Data Classification2026