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Found 8 papers, 5 papers with code
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines.
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Many information retrieval tasks require large labeled datasets for fine-tuning.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
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 with leaderboards, and novel algorithms for key components such as retrievers and readers.
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks.
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval
On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.
Learning Cross-Lingual IR from an English Retriever
We present DR. DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD).
Cross-Lingual Information Retrieval
Knowledge Distillation
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Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
