TREC 2025 Proceedings

grilllab-larf-finetuned-10-rounds

Submission Details

Organization
grilllab
Track
Interactive Knowledge Acquisition Track
Task
Passage Ranking and Response Generation
Date
2025-07-28

Run Description

What type of manually annotated information does the system use?
automatic: system does not use any manually annotated data and relies only on the user utterance and system responses (canonical responses of previous turns)
How is conversation understanding (NLP/rewriting) performed in this run (check all that apply)?
['method uses large language models like LLaMA and GPT-x.']
What data is used for conversational query understanding in this run (check all that apply)?
['method uses iKAT 23 data']
How is ranking performed in this run (check all that apply)?
['method uses traditional unsupervised sparse retrieval (e.g.¸ QL¸ BM25¸ etc.)', 'method uses document expansion (e.g.¸ Doc2Query)', 'method performs re-ranking with a pre-trained neural language model (BERT¸ Roberta¸ T5¸ etc.) (please describe specifics in the description field below)']
What data is used to develop the ranking method in this run (check all that apply)?
['method is trained with CAsT datasets', 'method uses iKAT 23 data']
Please specify all the methods used to handle feedback or clarification responses from the user (check all that apply).
['method does not treat them specially']
Please describe the method used to generate the final conversational responses from one or more retrieved passages (check all that apply).
['method uses large language models to generate the summary.']
Please describe how you integrate the PTKBs in your run (check all that apply)
[' method uses a PTKB relevance model to detect the relevant ones', " method integrates PTKBs in the response generation method (e.g. include in the LLM's prompt)"]
Which LLM did you use to generate the final response?
[' method does not use LLMs and uses other techniques for response generation (please specify details in description below)']
Please describe the external resources used by this run, if applicable.
iKAT 2023, CAsT 2022
Please provide a short description of this run.
There are three stages to this run: - A candidate passage generation stage where the system rewrites the user's query in the context of the conversation and PTKBs, then retrieves 500 documents from the collection using BM25. - An expansion phase where the system uses existing documents to find similar documents from the collection with BM25. - A refinement phase where an LLM analyses the candidate passages and synthesises new queries to find more passages that address any gaps found Each phase consists of a passage filtering and reranking step. The output of each stage is a ranked list that is then fed to the next stage. Queries go through each stage multiple times until no new passages are found between rounds. Responses are generated with an LLM
Please provide a priority for assessing this run. (If resources do not allow all runs to be assessed, NIST will work in priority order, resolving ties arbitrarily).
2

Evaluation Files

Paper