Search Results for author:
Found 12 papers, 10 papers with code
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations.
Attention Heads of Large Language Models: A Survey
Our survey aims to shed light on the internal reasoning processes of LLMs by concentrating on the underlying mechanisms of attention heads.
Controllable Text Generation for Large Language Models: A Survey
This paper systematically reviews the latest advancements in CTG for LLMs, offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality.
Internal Consistency and Self-Feedback in Large Language Models: A Survey
In this paper, we use a unified perspective of internal consistency, offering explanations for reasoning deficiencies and hallucinations.
$\text{Memory}^3$: Language Modeling with Explicit Memory
The model is named $\text{Memory}^3$, since explicit memory is the third form of memory in LLMs after implicit memory (model parameters) and working memory (context key-values).
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability
HealthRCN is the largest known dataset of Chinese health information rumors to date.
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.
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).
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.
