4 papers · 1 filter
LLMs are Frequency Pattern Learners in Natural Language Inference
Liang Cheng, Zhaowei Wang, Mark Steedman
While fine-tuning LLMs on NLI corpora improves their inferential performance, the underlying mechanisms driving this improvement remain largely opaque. In this work, we conduct a s…
S2LPP: Small-to-Large Prompt Prediction across LLMs
Liang Cheng, Tianyi LI, Zhaowei Wang +1
The performance of pre-trained Large Language Models (LLMs) is often sensitive to nuances in prompt templates, requiring careful prompt engineering, adding costs in terms of comput…
Neutralizing Bias in LLM Reasoning using Entailment Graphs
Liang Cheng, Tianyi Li, Zhaowei Wang +2
LLMs are often claimed to be capable of Natural Language Inference (NLI), which is widely regarded as a cornerstone of more complex forms of reasoning. However, recent works show t…
Explicit Inductive Inference using Large Language Models
Tianyang Liu, Tianyi Li, Liang Cheng +1
Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of consideri…