4 citations · 12 across the 7 of their papers we have counts for
7 papers
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam +6
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can dim…
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Avirup Sil, Jaydeep Sen, Bhavani Iyer +12
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets wit…
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Keshav Santhanam, Jon Saad-Falcon, Martin Franz +7
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some di…
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval
Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz +2
We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including name…
Learning Cross-Lingual IR from an English Retriever
Yulong Li, Martin Franz, Md Arafat Sultan +3
We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage know…
Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan +4
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However,…
The TechQA Dataset
Vittorio Castelli, Rishav Chakravarti, Saswati Dana +18
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer…