Summary Ambiguity-Aware In-Context Learning with Large Language Models arxiv.org
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The study proposes a method for selecting demonstrations based on semantic similarity to the test example in order to explore ambiguity-aware in-context learning with large language models.
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Key Points
- Ambiguity-aware in-context learning (ICL) with large language models (LLMs) is explored in this study.
- The selection of good demonstrations for ICL is crucial as LLMs are sensitive to the choice of prompts.
- The proposed method for selecting ICL demonstrations involves three steps: ranking training data based on semantic similarity, identifying ambiguous label sets, and obtaining retriever-based baselines.
- Ambiguity-Aware In-Context Learning (AICL) is a method that improves the performance of large language models by selecting demonstrations based on ambiguous labels and mis-classifications.
- The document contains a list of references to various research papers and conference proceedings related to language models and in-context learning.
Summaries
30 word summary
The study explores ambiguity-aware in-context learning with large language models. The authors suggest a method for selecting demonstrations involving ranking training data based on semantic similarity to the test example.
38 word summary
The study focuses on ambiguity-aware in-context learning (ICL) with large language models (LLMs). The authors propose a method for selecting demonstrations for ICL, involving three steps: ranking training data based on semantic similarity to the test example, identifying
419 word summary
In this study, the authors explore ambiguity-aware in-context learning (ICL) with large language models (LLMs). They focus on the selection of good demonstrations for ICL, as LLMs are sensitive to the choice of prompts. One strategy
Our proposed method for selecting in-context learning (ICL) demonstrations involves three steps. First, we use a retriever to rank training data based on semantic similarity to the test example. We also identify the ambiguous label set for the test example and obtain
The authors propose a method for ambiguity-aware in-context learning (ICL) with large language models. They first identify the ambiguous label set for a test example by constructing a prompt and scoring each output label based on the model's log-likelihood. They
The document discusses the use of large language models for in-context learning. It presents different methods for selecting demonstrations and evaluates their performance. The methods include selecting the most frequent label, using zero-shot in-context learning, using static N-shot in-context learning,
The study proposes a method called Ambiguity-Aware In-Context Learning (AICL) to improve the performance of large language models. AICL selects demonstrations based on ambiguous labels and mis-classifications. It is found that adding a few
Our proposed methods in ambiguity-aware in-context learning with large language models aim to understand fine-grained nuances across label space and make accurate predictions. By introducing constraints, we sacrifice semantic similarity to the test input but still outperform retriever-based baselines that
This excerpt contains a list of references to various technical reports, conference papers, and research studies related to language models and in-context learning. The references include authors' names, publication titles, and other relevant details.
This document contains a list of references to various research papers and conference proceedings related to in-context learning, few-shot learning, and natural language processing. The references include papers from conferences such as EMNLP, ACL, and SemEval, as well as
This text excerpt includes a list of references to various research papers and articles related to computational linguistics and language models. The references cover a range of topics such as sentiment analysis, text classification, face recognition, and learning behavior. The authors of these works
In this document, several references are cited, including papers by Wei et al. (2023), Sang et al. (2021), Xue et al. (2021), Yoo et al. (2022), Zhang et al.
The document discusses the classification of language based on different categories such as threats, prejudice, animosity, and derogation. It also mentions specific tasks like sentiment classification and emotion classification. The confusion matrices and accuracy, precision, and recall tables are presented for