Summary Continual Learning for Large Language Models Survey arxiv.org
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One Line
Continual learning is essential for Large Language Models to acquire knowledge, values, and linguistic patterns despite challenges in implementation.
Slides
Slide Presentation (8 slides)
Key Points
- Continual learning is crucial for Large Language Models (LLMs) to stay updated with evolving human knowledge, values, and linguistic patterns.
- Continual learning for LLMs involves multi-staged approach including continual pretraining, instruction tuning, and alignment.
- Continual learning updates in LLMs are categorized based on learning stages and information types for effective implementation.
- Continual pretraining in LLMs updates facts, domains, and languages to keep models relevant and effective.
- Expanding the range of languages understood by LLMs is essential for broader accessibility.
- Continual Instruction Tuning (CIT) involves fine-tuning LLMs for following instructions and transferring knowledge for future tasks.
- CIT is divided into task-incremental, domain-incremental, and tool-incremental categories for specific tuning purposes.
Summaries
17 word summary
Continual learning crucial for Large Language Models to gain knowledge, values, linguistic patterns, with challenges in implementation.
47 word summary
Continual learning is essential for Large Language Models (LLMs) to develop knowledge, values, and linguistic patterns. Challenges in implementation include continual pretraining, instruction tuning, and alignment stages. LLMs like ChatGPT and LLaMa excel in tasks through these stages, enhancing linguistic and reasoning capabilities distinct from smaller models.
112 word summary
Continual learning is crucial for Large Language Models (LLMs) to evolve with knowledge, values, and linguistic patterns. The survey highlights challenges in implementing continual learning for LLMs, involving continual pretraining, instruction tuning, and alignment stages. LLMs like ChatGPT and LLaMa excel in tasks through these stages, enhancing linguistic and reasoning capabilities distinct from smaller models. Continual pretraining updates facts, domains, and languages with real-time data assimilation. Instruction tuning fine-tunes LLMs for task-specific instructions and domain adaptation. Continual Alignment ensures outputs align with societal values. The framework categorizes research on continual learning for LLMs, focusing on expanding language understanding, improving response to user commands, and adapting to changing world developments and user needs.
314 word summary
Continual learning is essential for Large Language Models (LLMs) to stay updated with evolving knowledge, values, and linguistic patterns. The paper surveys recent works on continual learning for LLMs, emphasizing the unique challenges of applying continual learning to these massive models. It involves a multi-staged approach, including continual pretraining, instruction tuning, and alignment. The categorization of continual learning updates based on learning stages and information types offers a comprehensive understanding of implementing continual learning effectively in LLMs.
LLMs like ChatGPT and LLaMa excel in various tasks through pre-training, instruction tuning, and alignment stages. Continual learning for LLMs aims to enhance linguistic and reasoning capabilities, distinct from adaptation methods for smaller models. The three stages of continual learning for LLMs are continual pretraining, instruction tuning, and alignment, focusing on expanding language understanding, improving response to user commands, and ensuring outputs align with societal values.
Continual pretraining in LLMs updates facts, domains, and languages to keep them relevant and effective. It involves real-time data assimilation and automated systems for verifying newly acquired data. Domain-incremental pre-training accumulates knowledge across domains, while domain-specific continual learning trains models on domain-specific datasets to enhance expertise. Expanding language understanding includes processing regional dialects, contemporary slangs, and programming languages.
Continual Instruction Tuning (CIT) fine-tunes LLMs to follow instructions and transfer knowledge for future tasks. Task-incremental CIT focuses on task-specific instructions, while domain-incremental CIT adapts LLMs to different domains. Tool-incremental CIT integrates LLMs with tools like calculators and search engines. Approaches include data selection strategies, memory buffer rehearsal, and tool embeddings for efficient tool mastery.
Continual Alignment ensures LLM outputs adhere to societal values by reflecting shifts in values and integrating new demographic groups. The paper introduces a framework for continual learning in LLMs, categorizing research in this area. Continual learning updates models with the latest information, enhances coding capabilities, expands linguistic range, and ensures adaptability to changing world developments and user needs.
591 word summary
Continual learning for Large Language Models (LLMs) is crucial for keeping them updated with evolving human knowledge, values, and linguistic patterns. This paper surveys recent works on continual learning for LLMs, highlighting the unique challenges and requirements of applying continual learning to these massive models. Continual learning for LLMs involves a multi-staged approach, including continual pretraining, instruction tuning, and alignment. The paper categorizes continual learning updates based on learning stages and information types, offering a comprehensive understanding of how to effectively implement continual learning in LLMs.
Large language models (LLMs) like ChatGPT and LLaMa have shown superior performance in various tasks. They undergo multiple training stages, including pre-training, instruction tuning, and alignment. Continual learning for LLMs aims to enhance their overall linguistic and reasoning capabilities, differentiating it from simpler adaptation methods used in smaller models. The paper categorizes continual learning for LLMs into three stages: continual pretraining to expand fundamental language understanding, continual instruction tuning to improve response to user commands, and continual alignment to ensure outputs adhere to values and societal norms.
Continual learning for LLMs differs from its use in smaller models due to their vast size and complexity. The paper introduces a framework for continual learning in LLMs, categorizing research in this area. Continual pretraining updates facts, domains, and languages, while continual instruction tuning focuses on task, domain, and tool incremental tuning. Continual alignment incorporates value and preference alignment to reflect shifts in societal values and integrate new demographic groups or value types into existing LLMs.
Continual pretraining in LLMs is essential for keeping them relevant and effective by updating models with the latest information, adapting them to specialized domains, enhancing coding capabilities, and expanding linguistic range. Continual pretraining ensures LLMs remain adaptable and responsive to changing world developments and user needs. Strategies include using dynamic datasets for real-time data assimilation and implementing automated systems for verifying newly acquired data.
Continual pretraining also updates domain knowledge through domain-incremental pre-training and domain-specific continual learning. Domain-incremental pre-training accumulates knowledge across multiple domains, while domain-specific continual learning evolves a general model into a domain expert by training on domain-specific datasets and tasks. These approaches enhance model adaptability and expertise across various domains.
Expanding the range of languages that LLMs can understand is essential for ensuring broader accessibility. This includes mastering natural languages and understanding programming languages. Research explores methods for continually updating LLMs to process regional dialects, contemporary slangs, and library-oriented code generation. These developments highlight the potential of LLMs to transform both natural and programming language processing.
Continual Instruction Tuning (CIT) involves continually fine-tuning LLMs to follow instructions and transfer knowledge for future tasks. CIT is divided into task-incremental CIT, domain-incremental CIT, and tool-incremental CIT. Task-incremental CIT focuses on continuously finetuning LLMs on task-specific instructions to solve novel tasks. Methods include data selection strategies, rehearsal with a memory buffer, contrastive rationale replay, and dynamic instruction replay.
Domain-incremental CIT aims to continually finetune LLMs on domain-specific instructions to solve tasks in novel domains. Approaches include adaptively tuning LLMs on domain-specific data, applying continual learning methods using PET and dynamic replay strategy, adapting LLMs to different domains through reading comprehension tasks, using plug-in memory to store domain knowledge, and designing an adapt-retrieve-revise process for adapting LLMs to new domains.
Tool-incremental CIT aims to continually fine-tune LLMs to interact with the real world and integrate with tools like calculators, search engines, and databases. Approaches include tuning LLMs on datasets with math-related text and code, representing tools as new tokens during instruction tuning, and using tool embeddings to efficiently master tools.
Continual Alignment focuses