Labadain-ZSRunS: Sparse and Zero-Shot Dense Retrieval Runs with LLM-Generated Summaries for Tetun Ad-Hoc Text Retrieval

The models used are DPR, mDPR, Contriever, mContriever, ColBERTv2, and ColBERT-X. **2.2. LLM-Generated Document Summaries** Summaries were automatically generated using Claude Haiku 3, based on the full content of each Tetun document. **3. Dataset Structure** All files are organized under the root folder of Labadain-ZSRunS. **3.1.

Run Files** Run files are stored in CSV format, comprising qid, docno, score, and rank columns. - Sparse retrieval models: Each sparse model (BM25, DFR BM25, Hiemstra LM) has its own run file. All sparse model run files are located within the sparse-baseline-run-files/[files]. - Dense retrieval models: Each dense model (e.g., DPR, mDPR, Contriever) includes two run files: one using title embeddings and the other using contextual (title + summary) embeddings.

All dense model run files are located within the dense-zero-shot-run-files/[folders]. **3.2. LLM-Generated Summaries** The LLM-generated summaries file is provided in CSV format with docno and contextual_document columns and located within the llm-generated-summaries/[file]. - The docno column represents the document identifier as used in the Labadain-Avaliadór document collection. - The contextual_document column contains a concatenation of the document's title and its LLM-generated summary, separated by a newline character (\n).