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Found 13 papers, 10 papers with code
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
DPPA: Pruning Method for Large Language Model to Model Merging
In this paper, we introduce a dual-stage method termed Dynamic Pruning Partition Amplification (DPPA), devised to tackle the challenge of merging complex fine-tuned models.
Collaborative Large Language Model for Recommender Systems
We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics.
Knowledge Editing for Large Language Models: A Survey
Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category.
Path-Specific Counterfactual Fairness for Recommender Systems
But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.
Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines
For another, the multimodal reasoning task emphasized the prediction of future states and behaviors but often neglected the incorporation of individual personality traits.
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization
Traditional RSs estimate user interests and predict their future behaviors by utilizing correlations in the observational historical activities, their profiles, and the content of interacted items.
Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
However, since latent item variables are not modeled in UAE, it is difficult to utilize the widely available item content information when ratings are sparse.
Deep Deconfounded Content-based Tag Recommendation for UGC with Causal Intervention
We first establish a causal graph to represent the relations among uploader, UGC, and tag, where the uploaders are identified as confounders that spuriously correlate UGC and tag selections.
Deep Causal Reasoning for Recommendations
As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation.
Cross-modal Variational Auto-encoder for Content-based Micro-video Background Music Recommendation
Moreover, the multimodal information is fused by the product-of-experts (PoE) principle, where the semantic information in visual and textual modalities of the micro-video are weighted according to their variance estimations such that the modality with a lower noise level is given more weights.
Variational Bandwidth Auto-encoder for Hybrid Recommender Systems
Moreover, by considering the fusion of collaborative and feature variables as a virtual communication channel from an information-theoretic perspective, we introduce a user-dependent channel to dynamically control the information allowed to be accessed from the feature embeddings.
Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework
As an emerging type of user-generated content, micro-video drastically enriches people's entertainment experiences and social interactions.
