3 citations · 4 across the 4 of their papers we have counts for
4 papers
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data
Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh +1
We investigate improving the retrieval effectiveness of embedding models through the lens of corpus-specific fine-tuning. Prior work has shown that fine-tuning with queries generat…
Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh +1
While the current state-of-the-art dense retrieval models exhibit strong out-of-domain generalization, they might fail to capture nuanced domain-specific knowledge. In principle, f…
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
Forrest Sheng Bao, Miaoran Li, Renyi Qu +13
Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing eva…
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
Leonid Boytsov, Preksha Patel, Vivek Sourabh +4
We carried out a reproducibility study of InPars, which is a method for unsupervised training of neural rankers (Bonifacio et al., 2022). As a by-product, we developed InPars-light…