3 citations · 6 across the 7 of their papers we have counts for
7 papers
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Shunfeng Zheng, Yudi Zhang, Meng Fang +4
Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for expert-level reasoning-such as solving Olym…
MedINST: Meta Dataset of Biomedical Instructions
Wenhan Han, Meng Fang, Zihan Zhang +5
The integration of large language model (LLM) techniques in the field of medical analysis has brought about significant advancements, yet the scarcity of large, diverse, and well-a…
RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
Zihan Zhang, Meng Fang, Ling Chen
Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency…
Turn-Level Active Learning for Dialogue State Tracking
Zihan Zhang, Meng Fang, Fanghua Ye +2
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems. However, collecting a large amount of turn-by-turn annotated dialogue data is costly and in…
CITB: A Benchmark for Continual Instruction Tuning
Zihan Zhang, Meng Fang, Ling Chen +1
Continual learning (CL) is a paradigm that aims to replicate the human ability to learn and accumulate knowledge continually without forgetting previous knowledge and transferring…
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang, Meng Fang, Ling Chen +2
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing conc…
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
Zihan Zhang, Meng Fang, Ling Chen +1
Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextu…