## 5 Conclusions  
The described experience presents several original contributions. First is the design of a KH to aid users in navigating a collection of documents by single topics and overlapping topics based on up-to-date LLM techniques and a fuzzy classification approach, allowing us to associate a document to more topics at the same time with distinct relevance. To this aim, the contribution focuses on the identification of the best performing LLM by carrying out a user-evaluation experiment. The second original contribution is the evaluation of different combinations of LLMs, pooling strategies, and similarity matching for the ad hoc retrieval task. As far as we know, a comparison of several \"semantic\" pretrained foundation LLMs, based on transformer architecture, to index and retrieve, in an ad hoc retrieval task, a highly heterogeneous collection of documents, with both highly variable length and variable nature and genre, was never performed before. The evaluation experiment investigated several aspects such as considering different chunk definitions and similarity metrics, and last but not least, different aggregation strategies of a varying number of the best chunks' relevance scores to compute the overall rank of documents. Determining the portion of the documents that is relevant to a query is important in the case the documents can be long, consisting of many chunks. This aspect can be dependent of personal user preferences, and indeed, the carried out experiments revealed that the engaged users considered documents as relevant when finding  
Figure 6: Mean AP (mAP) and corresponding standard deviation (st_dev_ap) for different combinations of pretrained LLMs, chunk definitions, and dot product matching function, along with a significance test against the baseline (“_ch_per_doc_1_avg_0”). Rows highlighted in green indicate performance improvements over the baseline (positive variation in the last column); red highlights indicate a performance drop (negative variation).  
just a few relevant sentences containing the search keywords. Nevertheless, depending on the tested foundation models, we could observe that the significance tests reveal that each model yields best results with a different number of considered chunks, meaning that relevance of documents does not solely depend on users' judgments but is also influenced by the model's characteristics. In this respect, a promising research direction could be to design a post hoc adaptive optimization mechanism to choose the best aggregation of the chunk relevant scores (i.e., find the best pooling strategy) for each user and collection. This application will be the objective of an evolution of the current proposal.  
Conceptualization, G.B. and P.T.A.d.; Data curation, P.T.A.d., A.M. and A.O.; Formal analysis, P.T.A.d.; Funding acquisition, L.B. and A.O.; Methodology, P.T.A.d. and G.B.; Project administration, L.B. and A.O.; Software (experiments), P.T.A.d.; Software (scraping), A.L., A.M., A.O. and P.T.A.d.; Writing--original draft, P.T.A.d. and G.B.; Writing--review and editing, P.T.A.d., G.B., L.B., A.L., A.M. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ISPRA Istituto Superiore per la Protezione e la Ricerca Ambientale, grant number prot. CNR-IREA 0003070/2022.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data used for the user-evaluation study are freely available at the url cited in [17].  
The authors declare no conflicts of interest.