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Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases.
Language models are few-shot learners
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
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CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models
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What Makes Good In-Context Examples for GPT- 3 3 ?
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
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Counterfactual vqa: A cause-effect look at language bias
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Show your work: Scratchpads for intermediate computation with language models
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An explanation of in-context learning as implicit bayesian inference
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Scaling instruction-finetuned language models
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A survey for in-context learning
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
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Active Learning Principles for In-Context Learning with Large Language Models
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A Causal View of Entity Bias in (Large) Language Models
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Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning
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Large language models are zero-shot reasoners
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Can language models learn from explanations in context?
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What Makes Good In-Context Examples for GPT-3?
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Neuro-Symbolic Procedural Planning with Commonsense Prompting
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Counterfactual Debiasing for Fact Verification
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Multi-Relational Probabilistic Event Representation Learning via Projected Gaussian Embedding
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Mitigating language model hallucination with interactive question-knowledge alignment
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Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension
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Llama 3 Model Card
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On the Causal Nature of Sentiment Analysis
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