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Found 9 papers, 2 papers with code
Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach
Despite their great success, the knowledge provided by the retrieval process is not always useful for improving the model prediction, since in some samples LLMs may already be quite knowledgeable and thus be able to answer the question correctly without retrieval.
Creating Suspenseful Stories: Iterative Planning with Large Language Models
To the best of our knowledge, this paper is the first attempt at suspenseful story generation with LLMs.
Foundation Models for Recommender Systems: A Survey and New Perspectives
Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes
We show that decomposition is an effective form of probing QA systems as well as a promising approach to explanation generation.
Guiding Neural Story Generation with Reader Models
In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress.
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.
Towards Universal Dialogue State Tracking
Dialogue state tracking is the core part of a spoken dialogue system.
Cost-Sensitive Active Learning for Dialogue State Tracking
Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data.
