Cutting Edge '25

Enhancing Tip of of the Tongue IR Systems

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"Tip-of-the-Tongue (ToT) retrieval tackles the challenge of identifying known items when users recall only vague or fragmentary de tails. Conventional lexical methods struggle to bridge the semantic gap inherent in ambiguous queries. In this study, we present a compact two stage pipeline built around the all-MiniLM-L6-v2 sentence transformer. First, we fine-tune it on the TREC ToT 2023 Q&A pair dataset, achiev ing a notable increase in single-stage retrieval performance (nDCG@1000 rising from 0.0322 to 0.1437 and MRR from 0.0005 to 0.0690). Second, we apply lightweight neural re-ranking—employing both a MonoT5 point wise re-ranker and a MiniLM-based cross-encoder—and fuse their out puts via Reciprocal Rank Fusion (RRF). While individual re-rankers yielded mixed results, RRF consistently enhanced both early- and deep list metrics (nDCG@1000 up to 0.1498, MRR to 0.0861). Finally, we report a small-scale zero-shot trial of four GPT variants, observing that “mini” models outperform their full-size counterparts in top-3 accuracy. Ourfindings demonstrate that a resource-efficient, fine-tuned transformer, when coupled with strategic fusion of lightweight re-rankers, can deliver improved performance on ToT known-item retrieval tasks."

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