We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing
environment, which allows the model to search and navigate the web. By setting up the task …

Knowledge-intensive tasks, such as open-domain question answering (QA), require access
to a large amount of world or domain knowledge. A common approach for knowledge …

Recent large language models often answer factual questions correctly. But users can't trust
any given claim a model makes without fact-checking, because language models can …

Text classification in natural language processing (NLP) is evolving rapidly, particularly with
the surge in transformer-based models, including large language models (LLM). This paper …

In traditional RAG framework, the basic retrieval units are normally short. The common
retrievers like DPR normally work with 100-word Wikipedia paragraphs. Such a design …

Current Open-Domain Question Answering (ODQA) model paradigm often contains a
retrieving module and a reading module. Given an input question, the reading module …

Retrieval-augmented language models (RALMs) represent a substantial advancement in
the capabilities of large language models, notably in reducing factual hallucination by …

With the increasing of model capacity brought by pre-trained language models, there
emerges boosting needs for more knowledgeable natural language processing (NLP) …

A conversational information retrieval (CIR) system is an information retrieval (IR) system
with a conversational interface, which allows users to interact with the system to seek …

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-
domain question answering (QA), where systems take natural language questions as input …