Search Results for author:
Found 7 papers, 3 papers with code
Context Embeddings for Efficient Answer Generation in RAG
We address this challenge by presenting COCOM, an effective context compression method, reducing long contexts to only a handful of Context Embeddings speeding up the generation time by a large margin.
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve an intricate number of different configurations such as evaluation datasets, collections, metrics, retrievers, and LLMs.
Retrieval-augmented generation in multilingual settings
Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and improving LLM factuality, but is predominantly studied in English-only settings.
The Role of Complex NLP in Transformers for Text Ranking?
Even though term-based methods such as BM25 provide strong baselines in ranking, under certain conditions they are dominated by large pre-trained masked language models (MLMs) such as BERT.
How Different are Pre-trained Transformers for Text Ranking?
Our results contribute to our understanding of (black-box) neural rankers relative to (well-understood) traditional rankers, help understand the particular experimental setting of MS-Marco-based test collections.
On the Realization of Compositionality in Neural Networks
We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.
Point-less: More Abstractive Summarization with Pointer-Generator Networks
The Pointer-Generator architecture has shown to be a big improvement for abstractive summarization seq2seq models.
