lg_e2_3q5r2l_mt_qw3 — Report Generation Task

Document collection: ['English subset', 'Arabic subset', 'Chinese subset', 'Russian subset']
Machine translation of documents: ['Yes we used the organizer-provided machine translations']
Write a short description of your retrieval process: Reciprocal ranked fusion over multilingual retrieval using plaid-x, multilingual LSR, and Qwen3 single dense vectors with ANN search.
Write a short description of your generation process: This run leverages the langraph framework. In a round, the approach produces a set of 3 queries. Similarity with the query based on Qwen3 emeddings are use to select snippets of the MT'ed documents from which the a document-based report is generated. The top 5 documents for each query are included. The snippets from documents retrieved with a single query are used to generate a partial answer. Partial answers from all queries are examined for completeness. If the answer is deemed to be incomplete, up to 3 new queries are produced to fill knowledge gaps. At the completion of at most 2 rounds, an answer is drafted and then shortened to fit the length limit. All generation is based on machine translated documents.
Which LLM(s) where used by your system?: Retrieval: XLMR and Qwen3-8B-Embedding; Generation: Qwen3-8B-Embedding, Llama-3.3-70B-Instruct for query generation, and a fine-tuned Qwen-8B model trained on report writing for drafting query-based reports and the overall report.
Open repository link: No
Assessing priority: 8