  Recent advancements in the capabilities of large language models (LLMs) have
paved the way for a myriad of groundbreaking applications in various fields.
However, a significant challenge arises as these models often "hallucinate",
i.e., fabricate facts without providing users an apparent means to discern the
veracity of their statements. Uncertainty estimation (UE) methods are one path
to safer, more responsible, and more effective use of LLMs. However, to date,
research on UE methods for LLMs has been focused primarily on theoretical
rather than engineering contributions. In this work, we tackle this issue by
introducing LM-Polygraph, a framework with implementations of a battery of
state-of-the-art UE methods for LLMs in text generation tasks, with unified
program interfaces in Python. Additionally, it introduces an extendable
benchmark for consistent evaluation of UE techniques by researchers, and a demo
web application that enriches the standard chat dialog with confidence scores,
empowering end-users to discern unreliable responses. LM-Polygraph is
compatible with the most recent LLMs, including BLOOMz, LLaMA-2, ChatGPT, and
GPT-4, and is designed to support future releases of similarly-styled LMs.
