Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Large language models (LLMs) have gained widespread adoption in various natural
language processing tasks, including question answering and dialogue systems. However …

Evaluating outputs of large language models (LLMs) is challenging, requiring making—and
making sense of—many responses. Yet tools that go beyond basic prompting tend to require …

State-of-the-art Large Language Models have recently exhibited extraordinary linguistic
abilities which have surprisingly extended to reasoning. However, responses that are …

The use of Large Language Models (LLMs) for writing has sparked controversy both among
readers and writers. On one hand, writers are concerned that LLMs will deprive them of …

Abstract Incorporating Generative Artificial Intelligence (GenAI), especially Large Language
Models (LLMs), into educational settings presents valuable opportunities to boost the …

Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend
complex and ambiguous natural language prompts. However, calibrating LLM interactions is …

Large Language Models (LLMs) are notorious for blending fact with fiction and generating
non-factual content, known as hallucinations. To address this challenge, we propose an …

In our era of rapid technological advancement, the research landscape for writing assistants
has become increasingly fragmented across various research communities. We seek to …

Large language models (LLMs) are capable of generating multiple responses to a single
prompt, yet little effort has been expended to help end-users or system designers make use …