8 citations · 28 across the 29 of their papers we have counts for
6 papers · 2 filters
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
Bozhou Li, Hao Liang, Yang Li +4
During the pretraining phase, large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora. Nevertheless, in later stages such as fine-tuning and infe…
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. However, they still encounter difficulties in solving mathematical pr…
Data Proportion Detection for Optimized Data Management for Large Language Models
Hao Liang, Keshi Zhao, Yajie Yang +4
Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks and domains, with data preparation playing a critical role in achieving these re…
QAEncoder: Towards Aligned Representation Learning in Question Answering Systems
Zhengren Wang, Qinhan Yu, Shida Wei +6
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders…
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
Hao Liang, Linzhuang Sun, Jingxuan Wei +5
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently,…
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capability has become a crucial prerequisite. Consequently, ma…