Reinforcement Learning From Human Feedback (RLHF) has been a critical to the success
of the latest generation of generative AI models. In response to the complex nature of the …

We propose controlled decoding (CD), a novel off-policy reinforcement learning method to
control the autoregressive generation from language models towards high reward …

With the rapid advancement of large language models (LLMs), information retrieval (IR)
systems, such as search engines and recommender systems, have undergone a significant …

In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated
high text generation quality. However, in real-world applications, LLMs must meet …

Rapid advancements of large language models (LLMs) have enabled the processing,
understanding, and generation of human-like text, with increasing integration into systems …

As Large Language Models (LLMs) are deployed more widely, customization with respect to
vocabulary, style and character becomes more important. In this work we introduce model …

While most research on controllable text generation has focused on steering base
Language Models, the emerging instruction-tuning and prompting paradigm offers an …

The Natural Language Interface to Databases (NLIDB) empowers non-technical users with
database access through intuitive natural language (NL) interactions. Advanced …

As large language models become increasingly integrated into daily life, detecting implicit
toxicity across diverse contexts is crucial. To this end, we introduce LifeTox, a dataset …

In the pursuit of developing Large Language Models (LLMs) that adhere to societal
standards, it is imperative to discern the existence of toxicity in the generated text. The …