Assessment and evaluation have long been critical challenges in artificial intelligence (AI)
and natural language processing (NLP). However, traditional methods, whether matching …

Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from
various LMs. However, concerns including transparency, controllability, and affordability …

Multimodal Large Language Models (MLLMs) have gained significant attention recently,
showing remarkable potential in artificial general intelligence. However, assessing the utility …

Large Language Models (LLMs) have demonstrated promising capabilities as automatic
evaluators in assessing the quality of generated natural language. However, LLMs still …

This review gives an extensive overview of evaluation methods for task-oriented dialogue
systems, paying special attention to practical applications of dialogue systems, for example …

With the proliferation of domain-specific models, model merging has emerged as a set of
techniques that combine the capabilities of multiple models into one that can multitask …

To enhance Large Language Models'(LLMs) reliability, calibration is essential--the model's
assessed confidence scores should align with the actual likelihood of its responses being …

Recent work has explored the capability of large language models (LLMs) to identify and
correct errors in LLM-generated responses. These refinement approaches frequently …

Since large language models (LLMs) achieve significant success in recent years, the
hallucination issue remains a challenge, numerous benchmarks are proposed to detect the …

Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language
Generation (NLG) tasks. However, the capabilities of LLMs in scoring NLG quality remain …