Search Results for author:
Found 14 papers, 12 papers with code
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data.
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks.
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination
We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system that can (1) effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions; (2) create and manage an interactive lecture that generates responsive interactions catering to student learning demands while regulating the interactions to follow teaching actions.
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption.
Knowledge-to-Jailbreak: One Knowledge Point Worth One Attack
To bridge this gap, we propose a new task, knowledge-to-jailbreak, which aims to generate jailbreaks from domain knowledge to evaluate the safety of LLMs when applied to those domains.
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models
In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAG workflows in conjunction with LLMs.
DICE: Detecting In-distribution Contamination in LLM's Fine-tuning Phase for Math Reasoning
To effectively detect in-distribution contamination, we propose DICE, a novel method that leverages the internal states of LLMs to locate-then-detect the contamination.
A Solution-based LLM API-using Methodology for Academic Information Seeking
Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts.
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible trace for watermark detection.
LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts
However, few MOOC platforms are providing human or virtual teaching assistants to support learning for massive online students due to the complexity of real-world online education scenarios and the lack of training data.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time
We find some stable features that stay unchanged and apply them on the detection of ChatGPT-generated texts to improve the robustness of cross-version detection.
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education.
TWAG: A Topic-Guided Wikipedia Abstract Generator
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques.
