Measuring the quality of responses generated by LLMs is a challenging task, particularly
when it comes to evaluating whether the response is aligned with human preference. A …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

Large Language Models (LLMs) have emerged as dominant tools for various tasks,
particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns …

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a
wide range of general natural language processing (NLP) tasks. Researchers have …

Large language models (LLMs) have recently attracted considerable interest for their ability
to perform complex reasoning tasks, such as chain-of-thought (CoT) reasoning. However …

Large language models (LLMs) demonstrate their promise in tackling complicated practical
challenges by combining action-based policies with chain of thought (CoT) reasoning …

In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by
conditioning on demonstrations of question-answer pairs and it has been shown to have …

Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in
information systems. Existing methods follow two main paradigms to collect evidence:(1) …

While open Large Language Models (LLMs) have made significant progress, they still fall
short of matching the performance of their closed, proprietary counterparts, making the latter …

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language
Models (MLLMs) has expanded the scope of multimodal query resolution. However, current …