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Found 10 papers, 9 papers with code
Robustness Testing of Language Understanding in Task-Oriented Dialog
Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution.
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
Applying this approach, we construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus.
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Endowing the protagonist with a specific personality is essential for writing an engaging story.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability.
Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training
However, the pre-trained dialogue model's ability to utilize long-range context is limited due to the scarcity of long-turn dialogue sessions.
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
In this paper, we propose a method named Ethicist for targeted training data extraction through loss smoothed soft prompting and calibrated confidence estimation, investigating how to recover the suffix in the training data when given a prefix.
Unveiling the Implicit Toxicity in Large Language Models
While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting.
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation
In this paper, we aim to control the protagonist’s persona in story generation, i. e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality through a coherent event sequence.
