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cs.CL2025
GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
Kun-Peng Ning, Jia-Yu Yao, Yu-Yang Liu +2
Large Language Models (LLMs), such as GPT, are considered to learn the latent distributions within large-scale web-crawl datasets and accomplish natural language processing (NLP) t…
cs.CL2024
PiCO: Peer Review in LLMs based on the Consistency Optimization
Kun-Peng Ning, Shuo Yang, Yu-Yang Liu +5
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations…
cs.CL2023
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu +3
Large Language Models (LLMs), including GPT-3.5, LLaMA, and PaLM, seem to be knowledgeable and able to adapt to many tasks. However, we still cannot completely trust their answers,…