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Found 8 papers, 3 papers with code
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations
Adversarial attacks curated against NLP models are increasingly becoming practical threats.
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing.
RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface
We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS).
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
TCAB: A Large-Scale Text Classification Attack Benchmark
In addition to the primary tasks of detecting and labeling attacks, TCAB can also be used for attack localization, attack target labeling, and attack characterization.
Factual and Informative Review Generation for Explainable Recommendation
Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings.
On Faithfulness and Coherence of Language Explanations for Recommendation Systems
Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance.
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack.
