4 papers
Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks
Fahmida Alam, Ellen Riloff
Existing Natural Language Processing (NLP) resources often lack the task-specific information required for real-world problems and provide limited coverage of lesser-known or newly…
Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion
Fahmida Alam, Mihai Surdeanu, Ellen Riloff
Large language models (LLMs) struggle with relation completion (RC), both with and without retrieval-augmented generation (RAG), particularly when the required information is rare…
Memorization in In-Context Learning
Shahriar Golchin, Mihai Surdeanu, Steven Bethard +2
In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mecha…
Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation
Haris Riaz, Ellen Riloff, Mihai Surdeanu
We propose a simple, unsupervised method that injects pragmatic principles in retrieval-augmented generation (RAG) frameworks such as Dense Passage Retrieval to enhance the utility…