15 citations · 36 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning to Reason via Mixture-of-Thought for Logical Reasoning
Tong Zheng, Lichang Chen, Simeng Han +2
Human beings naturally utilize multiple reasoning modalities to learn and solve logical problems, i.e., different representational formats such as natural language, code, and symbo…
From Lists to Emojis: How Format Bias Affects Model Alignment
Xuanchang Zhang, Wei Xiong, Lichang Chen +3
In this paper, we study format biases in reinforcement learning from human feedback (RLHF). We observe that many widely-used preference models, including human evaluators, GPT-4, a…
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +4
Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the…
AlpaGasus: Training A Better Alpaca with Fewer Data
Lichang Chen, Shiyang Li, Jun Yan +8
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT data…
Backdoor Learning on Sequence to Sequence Models
Lichang Chen, Minhao Cheng, Heng Huang
Backdoor learning has become an emerging research area towards building a trustworthy machine learning system. While a lot of works have studied the hidden danger of backdoor attac…
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Lichang Chen, Heng Huang, Minhao Cheng
Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. However, the exis…