Search Results for author:
Found 14 papers, 10 papers with code
Improving Generalization and Convergence by Enhancing Implicit Regularization
In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence.
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs).
xFinder: Robust and Pinpoint Answer Extraction for Large Language Models
The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance.
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods.
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values.
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
For each of these CRUD categories, we have developed comprehensive datasets to evaluate the performance of RAG systems.
CAT-LLM: Prompting Large Language Models with Text Style Definition for Chinese Article-style Transfer
Text style transfer is increasingly prominent in online entertainment and social media.
Grimoire is All You Need for Enhancing Large Language Models
In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application.
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
Automating Code Review Activities by Large-Scale Pre-training
In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario.
