LM-Polygraph: Uncertainty Estimation for Language Models

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.Comment: Accepted at EMNLP-202

Optimizing DSO Requests Management Flexibility for Home Appliances Using CBCC-RDG3

This article covers a case study with homes equipped with multiple appliances for energy consumption. The central goal is to provide for aggregators’ flexibility in distribution networks by building an optimal schedule that takes advantage of load flexibility resources. This, in turn, allows for the re-scheduling of shifting/real-time home appliances to provision a request from a distribution system operator (DSO). The paper concludes with the consideration of the CBCC-RDG3, HyDE-DF, and genetic algorithms, which were used to find the best schedule that would be highly efficient and meet all the constraints associated with the problem that successfully demonstrate the effectiveness of this particular approach

Optimizing DSO Requests Management Flexibility for Home Appliances Using CBCC-RDG3

This article covers a case study with homes equipped with multiple appliances for energy consumption. The central goal is to provide for aggregators’ flexibility in distribution networks by building an optimal schedule that takes advantage of load flexibility resources. This, in turn, allows for the re-scheduling of shifting/real-time home appliances to provision a request from a distribution system operator (DSO). The paper concludes with the consideration of the CBCC-RDG3, HyDE-DF, and genetic algorithms, which were used to find the best schedule that would be highly efficient and meet all the constraints associated with the problem that successfully demonstrate the effectiveness of this particular approach