Generative AI (GenAI) represents a shift from AI's ability to “recognize” to its ability to
“generate” solutions for a wide range of tasks. As generated solutions and applications grow …

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation,
introducing Large Language Models (LLMs) has opened new avenues for assessing …

Offering a promising solution to the scalability challenges associated with human evaluation,
the LLM-as-a-judge paradigm is rapidly gaining traction as an approach to evaluating large …

Instruction tuning is critical to improve LLMs but usually suffers from low-quality and
redundant data. Data filtering for instruction tuning has proved important in improving both …

Accurate and consistent evaluation is crucial for decision-making across numerous fields,
yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large …

Some prior work has shown that LLMs perform well in NLG evaluation for different tasks.
However, we discover that LLMs seem to confuse different evaluation criteria, which reduces …

Transformer-based Large Language Models (LLMs) often impose limitations on the length of
the text input to ensure the generation of fluent and relevant responses. This constraint …

Evaluating the degree of reproduction of copyright-protected content by language models
(LMs) is of significant interest to the AI and legal communities. Although both literal and non …

Recently, integrating external tools with Large Language Models (LLMs) has gained
significant attention as an effective strategy to mitigate the limitations inherent in their pre …

Recently, Large Language Models (LLMs) have been widely studied by researchers for their
roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese …