Recent advances in large language models have enabled the development of viable
generative retrieval systems. Instead of a traditional document ranking, generative retrieval …

Retrieval-augmented generation (RAG) is an effective technique that enables large
language models (LLMs) to utilize external knowledge sources for generation. However …

There is an emerging trend to use large language models (LLMs) to reason about complex
goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. This …

Large Language Models (LLMs) are transforming the way people generate, explore, and
engage with content. We study how we can develop LLM applications for online social …

Managing knowledge efficiently is crucial for organizational success. In manufacturing,
operating factories has become increasing knowledge-intensive putting strain on the …

We design probes trained on the internal representations of a transformer language model
that are predictive of its hallucinatory behavior on in-context generation tasks. To facilitate …

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being
used in a wide range of academic and industrial fields. More recently, Large Language …

In the rapidly evolving field of natural language processing, dialogue systems primarily
employ a single-step dialogue paradigm. Although this paradigm is efficient, it lacks the …

This research compares large language model (LLM) fine-tuning methods, including
Quantized Low Rank Adapter (QLoRA), Retrieval Augmented fine-tuning (RAFT), and …

Abstract Knowledge-grounded dialogue prevents the model from generating meaningless
responses or outdated information by providing additional knowledge. Despite this, the …