4 papers
ContextGuard: Structured Self-Auditing for Context Learning in Language Models
Hongbo Jin, Chi Wang, Haoran Tang +5
Recent benchmarks reveal that despite strong reasoning capabilities, large language models (LLMs) still struggle to faithfully apply complex contextual knowledge. These failures ar…
Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis
Hongbo Jin, Mingnan Zhu, Jingqi Tian +6
While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamically extract, internalize, and…
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
Xiang Li, Haoran Tang, Siyu Chen +3
We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consist…
Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
Xiang Li, Haoran Tang, Siyu Chen +3
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclu…