Parameter settings for in-processing models¶
(Default values are in ~/recommendation/In-processing.yaml)
The benchmark provides several arguments for describing:
Basic setting of the parameters
See below for the details:
In-processing required parameters¶
Base model set ups¶
model (str)
: The base model name.data_type (str)
: The base model training style, choosen from [‘point’, ‘pair’, ‘sequential’]. Note not all model support all types.
In-processing model set ups¶
fair-rank (bool)
: Bool variable for determining whether add fairness- and diversity-aware modules into base models.rank_model (str)
: The fairness- and diversity-aware model name.
LLM setups¶
use_llm (bool)
: Bool variable for determining whether to use LLM for fairness ranking.llm_type (str)
: Variables that determine the method of calling the large model, choosen from [‘api’, ‘local’, ‘vllm’].llm_name (str)
: The name of the LLM to be called, which must match the key in ‘llm_path_dict’ in ~/recommendation/properties/models/LLM.yaml.grounding_model (str)
: The name of the grounding model which is used to ground the LLM generated answer to real items. It should match the key in ‘llm_path_dict’ in ~/recommendation/properties/models/LLM.yaml.saved_embs_filename (str)
: The file name which store the item names as embeddings using the grounding model. Input None if you don’t need to save it.fair_prompt (str)
: Fairness prompts which is input to the large model to generate different types of fairness-aware recommendations.
Log set ups¶
log_name (str)
: The running log name, which will create a new dictionary ~recommendationloglog_nameand store the test results and all parameters into it.
For evaluation settings, please reference to parameters in evaluation.rst