7 citations · 12 across the 11 of their papers we have counts for
11 papers
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
Bowen Ren, Heyan Huang, Yinghao Li +1
Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing expe…
How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling
Yuhang Liu, Heyan Huang, Yizhe Yang +3
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear…
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios
Bin Xu, Yu Bai, Huashan Sun +10
As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing…
Word Matters: What Influences Domain Adaptation in Summarization?
Yinghao Li, Siyu Miao, Heyan Huang +1
Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the m…
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
Heyan Huang, Yinghao Li, Huashan Sun +2
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as…
Speaker Verification in Agent-Generated Conversations
Yizhe Yang, Palakorn Achananuparp, Heyan Huang +2
The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of…
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling
Yu Bai, Xiyuan Zou, Heyan Huang +4
Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within th…
Identifying and Analyzing Performance-Critical Tokens in Large Language Models
Yu Bai, Heyan Huang, Cesare Spinoso-Di Piano +4
In-context learning (ICL) has emerged as an effective solution for few-shot learning with large language models (LLMs). However, how LLMs leverage demonstrations to specify a task…
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Yizhe Yang, Heyan Huang, Yihang Liu +1
Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source. In general, there are two forms of kno…
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Yizhe Yang, Huashan Sun, Jiawei Li +5
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. Wh…
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Xiaochen Liu, Yang Gao, Yu Bai +4
Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt…