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The survey discusses the current state and future improvements of in-context learning for natural language processing.
Slides
Slide Presentation (9 slides)
Key Points
- In-context learning (ICL) is a new paradigm for natural language processing that involves the ability of large language models (LLMs) to make predictions based on contexts.
- The survey focuses on the training and inference stages of ICL, highlighting the challenges and potential directions for improvement.
- Various strategies such as supervised in-context finetuning, symbol tuning, and instruction tuning have been proposed to enhance the ICL capability of language models.
- Mutual information is a valuable selection metric for selecting demonstrations in ICL.
- Researchers have focused on demonstration formatting and instruction formatting as two main aspects of ICL.
- Different approaches like Self-Ask, iCAP, and Least-to-Most Prompting aim to improve language model performance in ICL.
- Factors such as domain source and pretraining on related corpora influence the performance of ICL.
- Intermediate tuning and tailored pretraining objectives have shown promising performance improvements in bridging the gap between pretraining and ICL.
Summaries
25 word summary
This survey summarizes the progress and challenges of in-context learning for natural language processing, focusing on training and inference stages and potential directions for improvement.
43 word summary
This survey on in-context learning aims to summarize the progress and challenges of this new paradigm for natural language processing. It focuses on the training and inference stages of in-context learning (ICL) and highlights the challenges and potential directions for improvement. Researchers have
596 word summary
In the survey on in-context learning, the authors aim to summarize the progress and challenges of this new paradigm for natural language processing. In-context learning (ICL) is the ability of large language models (LLMs) to make predictions based on contexts
The survey aims to provide an overview of in-context learning (ICL) and its current progress. It focuses on the training and inference stages of ICL, highlighting the challenges and potential directions for improvement. The survey does not cover the details of pre
Researchers have proposed various strategies to enhance the in-context learning (ICL) capability of language models (LLMs) through supervised in-context finetuning, symbol tuning, and instruction tuning. Model warmup, which adjusts LLMs before ICL
Mutual information is a valuable selection metric that does not require labeled examples or specific LLMs. Different methods have been proposed for selecting demonstrations, including choosing prompts with low perplexity, considering the diversity of demonstrations, generating demonstrations from LLMs,
In the field of in-context learning (ICL), researchers have focused on two main aspects: demonstration formatting and instruction formatting. In demonstration formatting, the goal is to find the best way to present examples to aid learning. One approach is to concatenate examples
Self-Ask, iCAP, and Least-to-Most Prompting are three different approaches to in-context learning (ICL) that aim to improve language model performance. Self-Ask allows language models to generate follow-up questions for the input, i
Factors that influence in-context learning (ICL) performance are discussed. In the pre-training stage, the domain source is found to be more important than corpus size. Multiple corpora may enhance ICL ability, but pretraining on corpora related to
Akyurek et al. (2022) found that Transformer-based in-context learners implicitly implement standard finetuning algorithms. Li et al. (2023e) discovered that self-attention-only Transformers exhibit similarity to models learned by gradient descent
In a survey on in-context learning (ICL), researchers have explored its application in various domains, including multilingual reasoning abilities and visual in-context learning. They found that existing ICL methods on language and vision tasks show poor performance in evaluating reasoning abilities
Researchers have developed a TTS framework with strong in-context learning capability using audio codec codes. They have also extended this idea to multi-lingual scenarios, demonstrating superior performance in zero-shot cross-lingual text-to-speech synthesis and speech-to-s
Researchers have proposed intermediate tuning as a way to bridge the gap between pretraining objectives and in-context learning (ICL), showing promising performance improvements. Tailored pretraining objectives and metrics for ICL have the potential to enhance language models (LLMs)
This document is a survey on in-context learning, and it includes a list of references from various papers and studies related to the topic. The references cover a range of subjects, including the impact of in-context examples on compositional generalization, visual prompting
This summary is a list of citations from various articles related to in-context learning. The citations include the authors' names, the titles of the articles, and the publication information.
This survey on in-context learning includes various research papers and preprints on the topic. The papers cover a range of subjects, including the use of transformers as algorithms, visual instruction tuning, prompting methods in natural language processing, few-shot prompt order sensitivity,
This document is a survey on in-context learning, featuring various studies and research papers related to the topic. The included papers cover a range of subjects, including prompt engineering, language models, visual in-context learning, multilingual models, dialog applications, few
This survey examines the topic of in-context learning and its applications in language models. It covers various research papers and preprints that discuss different aspects of in-context learning, including few-shot learning, symbol tuning, self-instruct alignment, larger language models,