4 citations · 5 across the 3 of their papers we have counts for
6 papers
Language Model Uncertainty Quantification with Attention Chain
Yinghao Li, Rushi Qiang, Lama Moukheiber +1
Accurately quantifying a large language model's (LLM) predictive uncertainty is crucial for judging the reliability of its answers. While most existing research focuses on short, d…
Ensembles of Low-Rank Expert Adapters
Yinghao Li, Vianne Gao, Chao Zhang +1
The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions…
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Yuzhao Heng, Chunyuan Deng, Yitong Li +4
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity re…
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
Yinghao Li, Rampi Ramprasad, Chao Zhang
Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsi…
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li +4
Large Language Models (LLMs) have recently showcased remarkable reasoning abilities. However, larger models often surpass their smaller counterparts in reasoning tasks, posing the…
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Yinghao Li, Haorui Wang, Chao Zhang
Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific f…