5 papers
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…
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
Zhaowei Wang, Wenhao Yu, Xiyu Ren +9
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are capable of handling hundreds of ima…
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…