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Found 25 papers, 16 papers with code
Demystifying Domain-adaptive Post-training for Financial LLMs
Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance.
Continual Learning Using Only Large Language Model Prompting
We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box.
In-context Continual Learning Assisted by an External Continual Learner
However, incrementally learning each new task in ICL necessitates adding training examples from each class of the task to the prompt, which hampers scalability as the prompt length increases.
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"
Ensuring faithfulness to context in large language models (LLMs) and retrieval-augmented generation (RAG) systems is crucial for reliable deployment in real-world applications, as incorrect or unsupported information can erode user trust.
SFR-RAG: Towards Contextually Faithful LLMs
Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI.
Bridging the Preference Gap between Retrievers and LLMs
Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM.
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks
To our knowledge, only one method has been proposed to learn a sequence of mixed tasks.
Parameter-Level Soft-Masking for Continual Learning
Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i. e., as more tasks are learned, the performance deteriorates.
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
The paper then proves that the theory can be generalized or extended to open-world CIL, which is the proposed open-world continual learning, that can perform CIL in the open world and detect future or open-world OOD data.
Continual Pre-training of Language Models
A novel proxy is also proposed to preserve the general knowledge in the original LM.
Ranked #1 on
Continual Pretraining
on ACL-ARC
Adapting a Language Model While Preserving its General Knowledge
This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.
Continual Learning of Natural Language Processing Tasks: A Survey
Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the learned knowledge to help learn new tasks better.
A Theoretical Study on Solving Continual Learning
Continual learning (CL) learns a sequence of tasks incrementally.
Continual Training of Language Models for Few-Shot Learning
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Ranked #1 on
Continual Pretraining
on AG News
A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
Instead of using the saved samples in memory to update the network for previous tasks/classes in the existing approach, MORE leverages the saved samples to build a task specific classifier (adding a new classification head) without updating the network learned for previous tasks/classes.
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data
In many real-world machine learning applications, samples belong to a set of domains e. g., for product reviews each review belongs to a product category.
Continual Learning with Knowledge Transfer for Sentiment Classification
In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain.
Ranked #4 on
Continual Learning
on DSC (10 tasks)
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
To the best of our knowledge, no technique has been proposed to learn a sequence of mixed similar and dissimilar tasks that can deal with forgetting and also transfer knowledge forward and backward.
Ranked #1 on
Continual Learning
on F-CelebA (10 tasks)
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks.
Ranked #3 on
Continual Learning
on ASC (19 tasks)
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge.
Ranked #1 on
Continual Learning
on DSC (10 tasks)
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing.
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning
The goal of this work is to endow such systems with the additional ability to transfer knowledge among tasks when the tasks are similar and have shared knowledge to achieve higher accuracy.
Continual Learning Using Pseudo-Replay via Latent Space Sampling
PLS is not only simple and efficient but also does not invade data privacy due to the fact that it works in the latent feature space.
Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays
While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality.
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays
While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research.
