4 papers
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
Mingzi Cao, Xingwei Tan, Mahmud Elahi Akhter +4
Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. Although improving Large Language Model (LLM) reasoning has attracted sign…
Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance
Ahmed Alajrami, Xingwei Tan, Nikolaos Aletras
Instruction-tuning plays a vital role in enhancing the task-solving abilities of large language models (LLMs), improving their usability in generating helpful responses on various…
Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?
Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao +1
Chain-of-thought (CoT) prompting is a common technique for improving the reasoning abilities of large language models (LLMs). However, extended reasoning is often unnecessary and s…
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
Anthony Hughes, Vasisht Duddu, N. Asokan +2
Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference. Existing defense mechanisms su…