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Found 12 papers, 8 papers with code
KBAlign: Efficient Self Adaptation on Specific Knowledge Bases
Although retrieval-augmented generation (RAG) remains essential for knowledge-based question answering (KBQA), current paradigms face critical challenges under specific domains.
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Our experiments on various knowledge-intensive tasks demonstrate that DDR significantly outperforms the SFT method, particularly for LLMs with smaller-scale parameters that depend more on the retrieved knowledge.
PersLLM: A Personified Training Approach for Large Language Models
To fully unlock the potential of LLM personification, we propose PersLLM, a framework for better data construction and model tuning.
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge, enhancing their performance on knowledge-intensive tasks.
ConPET: Continual Parameter-Efficient Tuning for Large Language Models
First, Static ConPET can adapt former continual learning methods originally designed for relatively smaller models to LLMs through PET and a dynamic replay strategy, which largely reduces the tuning costs and alleviates the over-fitting and forgetting issue.
Interactive Molecular Discovery with Natural Language
Natural language is expected to be a key medium for various human-machine interactions in the era of large language models.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e. g., cold start) in real-world scenarios.
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task.
