Natural Language Generation (NLG) is a subfield of artificial intelligence that focuses on generating human-like text or speech based on given data or prompts. It employs algorithms and language models to automatically produce coherent and contextually appropriate narratives, summaries, or responses in natural language.
Researchers introduced an entropy-based uncertainty estimator to tackle false and unsubstantiated outputs in large language models (LLMs) like ChatGPT. This method detects confabulations by assessing meaning, improving LLM reliability in fields like law and medicine.
Meta researchers address the crucial need for fair generative large language models (LLMs), focusing on base models. The study introduces novel definitions of bias, employing a demographic parity benchmark, and expands evaluation metrics and datasets. Results reveal varying toxicity and bias rates, highlighting the importance of intersectional demographics, prompting, and ongoing dataset evolution for effective mitigation strategies in language models.
Researchers have introduced FACTCHD, a framework for detecting fact-conflicting hallucinations in large language models (LLMs). They developed a benchmark that provides interpretable data for evaluating the factual accuracy of LLM-generated responses and introduced the TRUTH-TRIANGULATOR framework to enhance hallucination detection.
Researchers introduced a framework, BioPlanner, for automatically evaluating the performance of large language models (LLMs) in planning experimental protocols in biology. They addressed the challenge of evaluating LLM-generated protocol accuracy by utilizing pseudocode representations and a dataset called BIOPROT, demonstrating the effectiveness of the approach in generating precise and executable laboratory protocols.
This article provides a comprehensive overview of the evolution of AI advertising research by analyzing literature from 1990 to 2022. It identifies key research areas, trends, and challenges in AI advertising and suggests future directions for integrating AI with marketing functions and improving ad effectiveness.
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