Search Results for author:
Found 11 papers, 10 papers with code
Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources and incorporating them into the context.
A Comprehensive Survey on Long Context Language Modeling
With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way.
Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity
Personalized tool utilization is essential for aligning large language models (LLMs) with user preference in interaction scenarios with various tools.
DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free Continual Learning
For new tasks, DATA dynamically adjusts the weights of adapters of different ranks based on their relevance and distinction from previous tasks, allowing the model to acquire new task-specific skills while effectively retaining previously learned knowledge.
Shuttle Between the Instructions and the Parameters of Large Language Models
The interaction with Large Language Models (LLMs) through instructions has been extensively investigated in the research community.
CITI: Enhancing Tool Utilizing Ability in Large Language Models without Sacrificing General Performance
However, previous works predominantly focus on improving model's tool-utilizing accuracy and the ability to generalize to new, unseen tools, excessively forcing LLMs to adjust specific tool-invoking pattern without considering the harm to model's general performance.
Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks
By decoupling general and specialized capabilities, the proposed NesyCD can achieve superior performance cost-effectively, utilizing smaller models and blending parameterized neural networks with symbolic KB.
$\textit{SKIntern}$: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models
By efficiently internalizing knowledge, $\textit{SKIntern}$ reduces computational overhead and speeds up the reasoning process by focusing solely on the question during inference.
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
Within this context, we introduce Task Adapters Generation from Instructions (TAGI), which automatically constructs the task-specific model in a parameter generation manner based on the given task instructions without retraining for unseen tasks.
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering
Retrieval-Augmented-Generation and Generation-Augmented-Generation have been proposed to enhance the knowledge required for question answering with Large Language Models (LLMs) by leveraging richer context.
Open-Domain Question Answering
Out-of-Distribution Generalization
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase.
