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Found 6 papers, 3 papers with code
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top item preferences in a cold-start setting is a key challenge for building effective and personalized conversational recommendation (ConvRec) systems.
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
In this work, we seek a novel KGQA dataset that supports commonsense reasoning and focuses on long-tail entities (e. g., non-mainstream and recent entities) where LLMs frequently hallucinate, and thus create the need for novel methodologies that leverage the KG for factual and attributable commonsense inference.
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering
Knowledge Graph Question Answering (KGQA) methods seek to answer Natural Language questions using the relational information stored in Knowledge Graphs (KGs).
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
Experimental results show that Late Fusion contrastive learning for Neural RIR outperforms all other contrastive IR configurations, Neural IR, and sparse retrieval baselines, thus demonstrating the power of exploiting the two-level structure in Neural RIR approaches as well as the importance of preserving the nuance of individual review content via Late Fusion methods.
Bayesian Knowledge-driven Critiquing with Indirect Evidence
This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS.
LogicRec: Recommendation with Users' Logical Requirements
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
