This paper investigates the capabilities of Large Language Models (LLMs) in the context of
understanding their knowledge and uncertainty over questions. Specifically, we focus on …

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

The rapid proliferation of large language models and natural language processing (NLP)
applications creates a crucial need for uncertainty quantification to mitigate risks such as …

Confidence estimation aiming to evaluate output trustability is crucial for the application of
large language models (LLM), especially the black-box ones. Existing confidence estimation …

The proliferation of open-source Large Language Models (LLMs) from various institutions
has highlighted the urgent need for comprehensive evaluation methods. However, current …

Using confidential information on banks' portfolios, inaccessible to market participants, we
show that banks that emphasize the environment in their disclosures extend a higher …

In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …

We present the copy suppression motif: an algorithm implemented by attention heads in
large language models that reduces loss. If i) language model components in earlier layers …

Named Entity Recognition (NER) serves as a fundamental task in natural language
understanding, bearing direct implications for web content analysis, search engines, and …