SemEval 2026 Task 8 (MTRAGEval)

Lightweight tri-fusion retrieval and faithful generation for multi-turn RAG

SemEval-2026 Task 8: Lightweight Tri-Fusion Retrieval with Prompt-Engineered Faithful Generation for Multi-Turn RAG

With Dr. Jonathan Rusert, Purdue University Fort Wayne, 2026

I co-authored the system paper for MTRAGEval, a SemEval 2026 benchmark for multi-turn retrieval-augmented generation. The project focuses on retrieval and generation quality when conversation history accumulates across turns.

System and Results

  • Lightweight tri-fusion retrieval combining BM25, SPLADE, and Jina v4 retrieval signals
  • Prompt-engineered generation designed to remain faithful to retrieved evidence
  • Reproducible retrieval, generation, submission, and camera-ready analysis pipelines
  • Task A: nDCG@5 of 0.433, ranking 20/38
  • Task B: H-mean of 0.756, ranking 6/26
  • Task C: H-mean of 0.533, ranking 14/29

Evaluation Focus

The work connects directly to my broader research interests:

  • Builds on RASS project experience with production RAG pipelines and evaluation
  • Addresses evaluation as a first-class system requirement
  • Focuses on realistic failure modes and edge cases
  • Explores retrieval-conditioned failure patterns