6 papers
One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
Neeraj Anand, Payel Santra, Partha Basuchowdhuri +2
Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query complexity and i…
Latent Diffusion Pretraining for Crystal Property Prediction
Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2
Fast and accurate prediction of crystal properties is a central challenge in new materials design. Graph neural networks and Transformer-based models have emerged as powerful tools…
Mask-to-Correct: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction
Payel Santra, Lavisha Sharma, Madhusudan Ghosh +1
The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely on supervised learning from ma…
Breaking Flat: A Generalised Query Performance Prediction Evaluation Framework
Payel Santra, Partha Basuchowdhuri, Debasis Ganguly
The traditional use-case of query performance prediction (QPP) is to identify which queries perform well and which perform poorly for a given ranking model. A more fine-grained and…
Beyond Correlations: A Downstream Evaluation Framework for Query Performance Prediction
Payel Santra, Partha Basuchowdhuri, Debasis Ganguly
The standard practice of query performance prediction (QPP) evaluation is to measure a set-level correlation between the estimated retrieval qualities and the true ones. However, n…
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
Payel Santra, Madhusudan Ghosh, Debasis Ganguly +2
Leveraging both labeled (input-output associations) and unlabeled data (wider contextual grounding) may provide complementary benefits in retrieval augmented generation (RAG). Howe…