Papers Task Arithmetic
“Task Arithmetic” 태그가 달린 논문 61편 · 필터 해제
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling
Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vector, characterises the direction and stri…
Task ArithmeticTest-time AdaptationTransfer LearningMetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic
The advent of large language models (LLMs) like GPT-4 has catalyzed the exploration of multi-task learning (MTL), in which a single model demonstrates proficiency across diverse tasks. Task arithmetic has emerged as a co…
Computational EfficiencyMulti-Task LearningTask ArithmeticOn Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
Efficient fine-tuning of large language models for task-specific applications is imperative, yet the vast number of parameters in these models makes their training increasingly challenging. Despite numerous proposals for…
In-Context LearningTask ArithmeticTransfer LearningTask Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition
Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, existing methods suffer in performance wh…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain Adaptationspeech-recognition+4HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task
Head pose estimation (HPE) requires a sophisticated understanding of 3D spatial relationships to generate precise yaw, pitch, and roll angles. Previous HPE models, primarily CNN-based, rely on cropped close-up human head…
Head Pose EstimationLanguage Modellingobject-detectionObject Detection+3Localizing Task Information for Improved Model Merging and Compression
Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduced by significant performance loss. Previ…
Task ArithmeticTo Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models
LLMs have been found to memorize training textual sequences and regurgitate verbatim said sequences during text generation time. This fact is known to be the cause of privacy and related (e.g., copyright) problems. Unlea…
Adversarial AttackMemorizationTask ArithmeticText GenerationNo Train but Gain: Language Arithmetic for training-free Language Adapters enhancement
Modular deep learning is the state-of-the-art solution for lifting the curse of multilinguality, preventing the impact of negative interference and enabling cross-lingual performance in Multilingual Pre-trained Language …
Task ArithmeticTransfer LearningHave You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e.g., GPUs) or require the collection of specific training data. Instead, it involves editing differ…
Language ModelingLanguage ModellingLarge Language Modelmodel+2Ethos: Rectifying Language Models in Orthogonal Parameter Space
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private infor…
MemorizationTask ArithmeticLanguage Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic
Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment. RESTA stands for REst…
Instruction FollowingMathTask ArithmeticMerging Multi-Task Models via Weight-Ensembling Mixture of Experts
Merging various task-specific Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified by task arithmetic, have been proven to …
Mixture-of-ExpertsTask ArithmeticConcrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well a…
Meta-LearningTask ArithmeticLanguage and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization
Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there are 7000 languages in the world and many of …
Cross-Lingual Transferparameter-efficient fine-tuningTask ArithmeticText Generation+1Task Arithmetic with LoRA for Continual Learning
Continual learning refers to the problem where the training data is available in sequential chunks, termed "tasks". The majority of progress in continual learning has been stunted by the problem of catastrophic forgettin…
Continual LearningTask ArithmeticModel Merging by Uncertainty-Based Gradient Matching
Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradie…
modelTask ArithmeticParameter Efficient Multi-task Model Fusion with Partial Linearization
Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven to be powerful and scalable to incorpora…
parameter-efficient fine-tuningTask ArithmeticAdaMerging: Adaptive Model Merging for Multi-Task Learning
Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be direct…
modelMulti-Task LearningTask ArithmeticTask Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be impr…
DisentanglementImage ClassificationTask ArithmeticAn Empirical Study of Multimodal Model Merging
Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the model…
modelRetrievalTask ArithmeticVisual Question Answering (VQA)