D3 Artikkeli ammatillisessa konferenssijulkaisussa
A Deep Dive into Multi-Head Attention and Multi-Aspect Embedding
Tekijät: Teimouri, Maryam; Kanerva, Jenna; Ginter, Filip
Toimittaja: Angelova, Galia; Kunilovskaya, Maria; Escribe, Marie; Mitkov, Ruslan
Konferenssin vakiintunut nimi: International Conference on Recent Advances in Natural Language Processing
Julkaisuvuosi: 2025
Kokoomateoksen nimi: Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI era
Aloitussivu: 1263
Lopetussivu: 1270
eISBN: 978-954-452-098-4
DOI: https://doi.org/10.26615/978-954-452-098-4-146
Verkko-osoite: https://doi.org/10.26615/978-954-452-098-4-146
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/500281244
Multi-vector embedding models play an increasingly important role in retrievalaugmented generation, yet their internal behaviour lacks comprehensive analysis. We conduct a systematic, head-level study of the 32-head Semantic Feature Representation (SFR) encoder with the FineWeb corpus containing 10 billion tokens. For a set of 4,000 web documents, we pair head-specific embeddings with GPT-4o topic annotations and analyse the results using t-SNE visualisations, heat maps, and a 32-way logistic probe. The analysis shows that (i) clear semantic separation between heads emerges only at an intermediate layer, (ii) some heads align with specific topics while others capture broader corpus features, and (iii) naive pooling of head outputs can blur these distinctions, leading to frequent topic mismatches. The study offers practical guidance on where to extract embeddings, which heads may be pruned, and how to aggregate them to support more transparent and controllable retrieval pipelines.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This research was conducted as part of the EU Horizon project SEUS – Smart European Shipbuilding (Grant Agreement No. 101096224), funded by the European Union. Additional support was provided by the Human Diversity Consortium under the Profi7 program of the Research Council of Finland. Computational resources were provided by CSC – IT Center for Science.
