Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of
Generative Artificial Intelligence, showcasing remarkable prowess in natural language …

As large language models (LLMs) continue to evolve, understanding and quantifying the
uncertainty in their predictions is critical for enhancing application credibility. However, the …

In the finance domain, nested named entities recognition has become a hot topic in named
entity recognition tasks. Traditional nested entity recognition methods easily ignore the …

Cyber-attack attribution is an important process that allows experts to put in place attacker-
oriented countermeasures and legal actions. The analysts mainly perform attribution …

Recognizing Chinese entities in low-resource settings is a challenging but promising task,
which extracts structured pre-defined entities and corresponding types from unstructured …

Abstract Recently, few-shot Named Entity Recognition (NER) has attracted significant
attention due to the high cost of obtaining high-quality labeled data. Decomposition-based …

Effective query-item relevance modeling is pivotal for enhancing user experience and
safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the …

Detecting music entities such as song titles or artist names is a useful application to help use
cases like processing music search queries or analyzing music consumption on the web …

This technical report introduces a Named Clinical Entity Recognition Benchmark for
evaluating language models in healthcare, addressing the crucial natural language …