Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Large language models (LLMs) have demonstrated remarkable performance across a wide
array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent …

The alignment tuning process of large language models (LLMs) typically involves instruction
learning through supervised fine-tuning (SFT) and preference tuning via reinforcement …

While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a
pivotal methodology for transferring advanced capabilities from leading proprietary LLMs …

Widely used learned metrics for machine translation evaluation, such as Comet and Bleurt,
estimate the quality of a translation hypothesis by providing a single sentence-level score …

Abstract Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement learning from human …

Generative large language models (LLMs), eg, ChatGPT, have demonstrated remarkable
proficiency across several NLP tasks, such as machine translation, text summarization …

Evaluating natural language generation (NLG) is a vital but challenging problem in artificial
intelligence. Traditional evaluation metrics mainly capturing content (eg n-gram) overlap …

Self-correction is an approach to improving responses from large language models (LLMs)
by refining the responses using LLMs during inference. Prior work has proposed various self …