hyintell/awesome-refreshing-llms

EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.

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updated Dec 12, 2023

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README

Awesome-Refreshing-LLMs

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. How can we refresh LLMs to align with the ever-changing world knowledge without expensive retraining from scratch?

llm_align_world_example
An LLM after training is static and can be quickly outdated. For example, ChatGPT has a knowledge
cutoff date of September 2021. Without web browsing, it does not know the latest information ever since.

📢 News

[2023-10] Our survey paper is now available on Arxiv: How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances.
[2023-10] Our survey paper: "How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances" has been accepted by EMNLP 2023! We will release the camera-ready version soon.
[2023-10] We create this repository to maintain a paper list on refreshing LLMs without retraining.

📃 Papers

Methods Overview

To refresh LLMs to align with the ever-changing world knowledge without retraining, we roughly categorize existing methods into Implicit and Explicit approaches. Implicit means the approaches seek to directly alter the knowledge stored in LLMs, such as parameters or weights, while Explicit means more often incorporating external resources to override internal knowledge, such as augmenting a search engine.

Please see our paper for more details.

methods taxonomy
Taxonomy of methods to align LLMs with the ever-changing world knowledge.

methods overview
A high-level comparison of different approaches.

Knowledge Editing

Knowledge editing (KE) is an arising and promising research area that aims to alter the parameters of some specific knowledge stored in pre-trained models so that the model can make new predictions on those revised instances while keeping other irrelevant knowledge unchanged. We categorize existing methods into meta-learning, hypernetwork, and locate-and-edit -based methods.

Meta-learning

Year	Venue	Paper	Link
2023	Arxiv	RECKONING: Reasoning through Dynamic Knowledge Encoding
2020	ICLR	Editable Neural Networks

Hypernetwork Editor

Year	Venue	Paper
2023	KBS	A divide and conquer framework for Knowledge Editing
2023	Arxiv	Inspecting and Editing Knowledge Representations in Language Models
2023	Arxiv	Propagating Knowledge Updates to LMs Through Distillation
2023	EACL	Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models
2022	ICLR	Fast Model Editing at Scale
2021	EMNLP	Editing Factual Knowledge in Language Models

Locate and Edit

Year	Venue	Paper
2023	Arxiv	KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
2023	Arxiv	Editing Commonsense Knowledge in GPT
2023	Arxiv	PMET: Precise Model Editing in a Transformer
2023	Arxiv	Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons
2023	Arxiv	Dissecting Recall of Factual Associations in Auto-Regressive Language Models
2023	ICLR	Mass-Editing Memory in a Transformer
2022	ACL	Knowledge Neurons in Pretrained Transformers
2022	NeurIPS	Fast Model Editing at Scale

Other

Year	Venue	Paper
2023	Arxiv	Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs
2023	Arxiv	Evaluating the Ripple Effects of Knowledge Editing in Language Models
2023	Arxiv	Cross-Lingual Knowledge Editing in Large Language Models
2023	Arxiv	Language Anisotropic Cross-Lingual Model Editing

Continual Learning

Continual learning (CL) aims to enable a model to learn from a continuous data stream across time while reducing catastrophic forgetting of previously acquired knowledge. With CL, a deployed LLM has the potential to adapt to the changing world without costly re-training from scratch. Below papers employ CL for aligning language models with the current world knowledge, including Continual Pre-training and Continual Knowledge Editing.

Continual Pre-training

Year	Venue	Paper
2023	Arxiv	KILM: Knowledge Injection into Encoder-Decoder Language Models
2023	Arxiv	Semiparametric Language Models Are Scalable Continual Learners
2023	Arxiv	Meta-Learning Online Adaptation of Language Models
2023	Arxiv	ModuleFormer: Modularity Emerges from Mixture-of-Experts
2023	Arxiv	Self Information Update for Large Language Models through Mitigating Exposure Bias
2023	Arxiv	Continual Pre-Training of Large Language Models: How to (re)warm your model?
2023	ICLR	Continual Pre-training of Language Models
2023	ICML	Lifelong Language Pretraining with Distribution-Specialized Experts
2022	ACL	ELLE: Efficient Lifelong Pre-training for Emerging Data
2022	EMNLP	Fine-tuned Language Models are Continual Learners
2022	EMNLP	Continual Training of Language Models for Few-Shot Learning
2022	EMNLP	TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
2022	ICLR	LoRA: Low-Rank Adaptation of Large Language Models
2022	ICLR	Towards Continual Knowledge Learning of Language Models
2022	NAACL	DEMix Layers: Disentangling Domains for Modular Language Modeling
2022	NAACL	Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
2022	NeurIPS	Factuality Enhanced Language Models for Open-Ended Text Generation
2022	TACL	Time-Aware Language Models as Temporal Knowledge Bases
2021	ACL	K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
2021	EACL	Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
2020	EMNLP	Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting

Continual Knowledge Editing

Year	Venue	Paper
2023	Arxiv	Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters
2023	ICLR	Transformer-Patcher: One Mistake Worth One Neuron
2022	ACL	On Continual Model Refinement in Out-of-Distribution Data Streams
2022	ACL	Plug-and-Play Adaptation for Continuously-updated QA

Memory-enhanced

Pairing a static LLM with a growing non-parametric memory enables it to capture information beyond its memorized knowledge during inference. The external memory can store a recent corpus or feedback that contains new information to guide the model generation.

Year	Venue	Paper
2023	Arxiv	Adaptation Approaches for Nearest Neighbor Language Models
2023	Arxiv	Semiparametric Language Models Are Scalable Continual Learners
2023	Arxiv	MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
2022	EMNLP	You can’t pick your neighbors, or can you? When and How to Rely on Retrieval in the kNN-LM
2022	EMNLP	Nearest Neighbor Zero-Shot Inference
2022	EMNLP	Memory-assisted prompt editing to improve GPT-3 after deployment
2022	EMNLP	Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement
2022	ICML	Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
2022	ICML	Memory-Based Model Editing at Scale
2022	NAACL	Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback
2021	EMNLP	Efficient Nearest Neighbor Language Models
2021	EMNLP	BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief
2020	ICLR	Generalization through Memorization: Nearest Neighbor Language Models

Retrieval-enhanced

Leveraging an off-the-shelf retriever and the in-context learning ability of LLMs, this line of work designs better retrieval strategies to incorporate world knowledge into a fixed LLM through prompting, which can be divided into single-stage and multi-stage.

single_and_multiple_stage_retrieval
Single-Stage (left) typically retrieves once, while Multi-Stage (right) involves multiple retrievals or revisions to solve complex questions

Year	Venue	Paper
2023	ACL	Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
2023	ACL	When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
2023	ACL	Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
2023	ACL	RARR: Researching and Revising What Language Models Say, Using Language Models
2023	ACL	MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
2023	Arxiv	Can We Edit Factual Knowledge by In-Context Learning?
2023	Arxiv	REPLUG: Retrieval-Augmented Black-Box Language Models
2023	Arxiv	Improving Language Models via Plug-and-Play Retrieval Feedback
2023	Arxiv	Measuring and Narrowing the Compositionality Gap in Language Models
2023	Arxiv	ART: Automatic multi-step reasoning and tool-use for large language models
2023	Arxiv	ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
2023	Arxiv	Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
2023	Arxiv	Question Answering as Programming for Solving Time-Sensitive Questions
2023	Arxiv	Active Retrieval Augmented Generation
2023	Arxiv	Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
2023	Arxiv	Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
2023	Arxiv	Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
2023	Arxiv	CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
2023	Arxiv	WikiChat: A Few-Shot LLM-Based Chatbot Grounded with Wikipedia
2023	Arxiv	Query Rewriting for Retrieval-Augmented Large Language Models
2023	Arxiv	Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs
2023	ICLR	Prompting GPT-3 To Be Reliable
2023	ICLR	Decomposed Prompting: A Modular Approach for Solving Complex Tasks
2023	ICLR	ReAct: Synergizing Reasoning and Acting in Language Models
2023	TACL	In-Context Retrieval-Augmented Language Models
2022	Arxiv	Rethinking with Retrieval: Faithful Large Language Model Inference

Internet-enhanced

A recent trend uses the whole web as the knowledge source and equips LLMs with the Internet to support real-time information seeking.

Year	Venue	Paper
2023	ACL	Large Language Models are Built-in Autoregressive Search Engines
2023	ACL	RARR: Researching and Revising What Language Models Say, Using Language Models
2023	Arxiv	Measuring and Narrowing the Compositionality Gap in Language Models
2023	Arxiv	ART: Automatic multi-step reasoning and tool-use for large language models
2023	Arxiv	TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
2023	Arxiv	MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
2023	Arxiv	Active Retrieval Augmented Generation
2023	Arxiv	Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
2023	Arxiv	CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
2023	Arxiv	Query Rewriting for Retrieval-Augmented Large Language Models
2023	ICLR	ReAct: Synergizing Reasoning and Acting in Language Models
2022	Arxiv	Internet-augmented language models through few-shot prompting for open-domain question answering

💻 Resources

Tools

LangChain: a framework for developing applications powered by language models.
ChatGPT plugins: designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services.
EasyEdit: an Easy-to-use Knowledge Editing Framework for LLMs.
FastEdit: injecting fresh and customized knowledge into large language models efficiently using one single command.
PyContinual: an Easy and Extendible Framework for Continual Learning.
Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

🚩 Citation

If our research helps you, please kindly cite our paper.

@article{zhang2023large,
  title={How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances},
  author={Zhang, Zihan and Fang, Meng and Chen, Ling and Namazi-Rad, Mohammad-Reza and Wang, Jun},
  journal={arXiv preprint arXiv:2310.07343},
  year={2023}
}

🎉 Acknowledgement & Contribution

This field is evolving very fast, and we may miss important works. Please don't hesitate to share your work. Pull requests are always welcome if you spot anything wrong (e.g., broken links, typos, etc.) or share new papers! We thank all contributors for their valuable efforts.

awesome-list

continual-learning

knowledge-editing

large-language-models

llm

llms

natural-language-processing

nlp

paper

pretrained-language-model

refreshing

retrieval-augmented-generation

review

survey

update-llm

Contributors

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hyintell/awesome-refreshing-llms

EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.

135

14 commits

updated Dec 12, 2023

See the code

README

Awesome-Refreshing-LLMs

📢 News

[2023-10] Our survey paper is now available on Arxiv: How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances.
[2023-10] Our survey paper: "How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances" has been accepted by EMNLP 2023! We will release the camera-ready version soon.
[2023-10] We create this repository to maintain a paper list on refreshing LLMs without retraining.

🔍 Table of Contents

📢 News
🔍 Table of Contents
📃 Papers
💻 Resources
- Related Survey
- Tools
🚩 Citation
🎉 Acknowledgement & Contribution