LM-Polygraph: Uncertainty Estimation for Language Models (2311.07383v1)

Abstract: Recent advancements in the capabilities of 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.

LM-Polygraph: A Framework for Uncertainty Estimation in LLMs

The paper "LM-Polygraph: Uncertainty Estimation for LLMs" presents a significant contribution to the domain of LLMing by addressing the prevalent issue of hallucination in LLMs. The conventional challenges associated with LLMs, despite their expansive capabilities, revolve around their tendency to generate plausible yet inaccurate outputs, commonly referred to as hallucinations. The authors propose LM-Polygraph, a framework dedicated to the implementation and evaluation of uncertainty estimation (UE) methods aimed at enhancing the reliability of LLM-generated text.

Core Contributions

The core contributions of the LM-Polygraph framework include:

• Comprehensive Framework: LM-Polygraph integrates a suite of state-of-the-art UE methods specifically tailored for LLMs involved in text generation tasks. The framework is designed with unified application interfaces in Python, facilitating ease of use and integration with popular LLMs from the HuggingFace library.
• Extendable Benchmark: An extendable benchmark is introduced within the framework, enabling researchers to conduct consistent evaluations of various UE techniques. The benchmark serves as a tool for standardizing performance assessments across different methodologies.
• Demo Application: A demonstration web application is developed to showcase the functionality of LM-Polygraph. This application enhances standard dialogue interfaces with confidence scores, thereby providing users with insights into the trustworthiness of model outputs.

Uncertainty Estimation Techniques

The paper effectively categorizes UE techniques into white-box and black-box methods. White-box methods leverage models' internal workings, while black-box methods utilize output-only data, providing flexibility in different application scenarios.

• White-box Methods: The framework includes traditional information-based methods such as token and sequence entropy, as well as ensemble and density-based methods like Mahalanobis Distance. These techniques, usually requiring access to model parameters and training data, are vital for capturing epistemic uncertainty.
• Black-box Methods: These methods include state-of-the-art approaches, allowing for integration with web-hosted services like ChatGPT. Black-box techniques provide a viable option when access to internal LLM structures is restricted.

Results and Implications

The experimental results suggest that white-box methods generally outperform black-box methods across various datasets. Information-theoretic concepts form the basis of many effective UE approaches, but further research is required to improve usability and robustness, especially in complex tasks such as summarizing lengthy texts or conducting open-ended question answering.

These findings have significant implications for practical deployments of LLMs, emphasizing the need for consistent uncertainty quantification. From a theoretical perspective, LM-Polygraph offers a cohesive platform for advancing research in UE, potentially stimulating innovation in developing methods that navigate the intricacies of LLM outputs.

Future Prospects

The development of LM-Polygraph paves the way for future exploration in several directions:

• Enhancing Computational Efficiency: Despite promising results, some UE methods introduce computational overheads that may limit deployment in resource-constrained environments. Ongoing optimizations and novel methodologies could alleviate these barriers.
• Broader Applicability: Extending the framework's UE techniques to encompass multi-lingual models and varying domains would enhance the versatility and impact of LM-Polygraph on a global scale.
• Fine-Tuning Calibration Methods: Developing more sophisticated calibration techniques for translating uncertainty estimates into intuitive confidence metrics could provide a better user experience and improve decision-making processes in critical applications.

In conclusion, the LM-Polygraph framework represents a substantial step forward in addressing the inherent unreliability of LLM outputs by systematically employing uncertainty estimation techniques. Its contributions potentially foster safer and more dependable applications of LLMs across diverse fields. The framework's open-ended design invites further research and development, encouraging the community to refine existing methods and innovate new solutions within this challenging domain.