8 citations · 11 across the 2 of their papers we have counts for
11 papers · 1 filter
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
Alisia Lupidi, Carlos Gemmell, Nicola Cancedda +5
Synthetic data generation has recently emerged as a promising approach for enhancing the capabilities of large language models (LLMs) without the need for expensive human annotatio…
Self-Taught Evaluators
Tianlu Wang, Ilia Kulikov, Olga Golovneva +7
Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the s…
FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations
Jane Dwivedi-Yu, Raaz Dwivedi, Timo Schick
The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have…
TOOLVERIFIER: Generalization to New Tools via Self-Verification
Dheeraj Mekala, Jason Weston, Jack Lanchantin +4
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learn…
MultiContrievers: Analysis of Dense Retrieval Representations
Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu +1
Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affec…