Summary Large Language Models as General Pattern Machines arxiv.org
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One Line
Large language models have the ability to generate complex sequences and fill in missing patterns without the need for further training.
Slides
Slide Presentation (9 slides)
Key Points
- Large language models (LLMs) have the ability to complete complex token sequences and exhibit pattern completion proficiency.
- LLMs can serve as general sequence modelers without additional training.
- LLMs can learn and complete patterns, improving sequences through in-context sequence transformation and extrapolation.
- Token indices or embeddings can be passed directly to LLMs or token alphabets that are unlikely to be grouped by the tokenizer to use LLMs as general pattern machines.
- LLMs can be used in robotics for pattern reasoning capabilities and completing patterns.
- Calibrating language models for improved few-shot performance is discussed in a paper presented at the International Conference on Machine Learning (ICML) in 2021.
- The document provides a list of references to various research papers and conference proceedings related to the use of large language models in different applications.
- The text excerpt discusses experiments and additional details related to large language models (LLMs), including token invariance and introducing new token embedding vectors.
Summaries
28 word summary
Large language models (LLMs) can complete complex token sequences and exhibit pattern completion proficiency with randomly sampled tokens. They can serve as general sequence modelers without additional training.
42 word summary
Large language models (LLMs) have the ability to complete complex token sequences and exhibit pattern completion proficiency even with randomly sampled tokens. They can serve as general sequence modelers without additional training. LLMs can complete patterns and improve sequences by combining in
487 word summary
Large language models (LLMs) have the ability to complete complex token sequences and exhibit pattern completion proficiency even with randomly sampled tokens. This suggests that LLMs can serve as general sequence modelers without additional training. The authors investigate how these zero-shot
Large Language Models (LLMs) have the ability to complete patterns and can be applied to various tasks such as extending a wiping motion or drawing patterns. They can also improve sequences by combining in-context sequence transformation and extrapolation. LLMs can learn
This summary covers the main points of the excerpted text. The original document discusses the capabilities of large language models (LLMs) in learning and completing patterns. It explores three categories of in-context pattern learning: sequence transformation, sequence completion, and sequence
A work-around for using large language models (LLMs) as general pattern machines is to pass token indices or embeddings directly to the model or use token alphabets that are unlikely to be grouped by the tokenizer. This approach can be applied to various
This excerpt discusses the use of large language models (LLMs) as general pattern machines in robotics. The authors introduce a benchmark for evaluating the pattern reasoning capabilities of LLMs using procedurally generated transformations. They demonstrate that LLMs can complete patterns
This document is a list of references to various research papers and conference proceedings related to the use of large language models in different applications. The references cover topics such as logical reasoning, quantitative reasoning, robotic affordances, planning goals, object rearrangement, optimal
Calibrating language models for improved few-shot performance is discussed in a paper presented at the International Conference on Machine Learning (ICML) in 2021. The measure of intelligence is explored in another paper by F. Chollet from 201
The summary of the text excerpt is as follows:
This excerpt consists of a list of references to various papers and articles related to language models. The references cover topics such as the few-shot learning capabilities of language models, transfer learning, out-of-distribution
The document provides a list of references to various technical reports and research papers related to large language models and their applications. Some of the key topics covered include embodied reasoning through planning with language models, zero-shot multimodal reasoning, few-shot grounded planning for embodied
This summary includes information about various papers and articles related to large language models.
The first paper, by Zhou et al. (2023), discusses how "Least-to-Most Prompting" can enable complex reasoning in large language models.
The
The summary of the text excerpt is as follows:
In Section 4 of the main paper, the authors describe how ARC problems require reasoning about different types of pattern operations. They provide sample problems that are correctly solved by text-davinci-003 and
The text excerpt discusses various experiments and additional details related to large language models (LLMs). In the first section, the token invariance of LLMs is investigated by introducing new token embedding vectors that the model has not seen during training. The