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Found 6 papers, 2 papers with code
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising.
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias.
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems.
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking
Dialogue state tracking (DST) aims to extract essential information from multi-turn dialogue situations and take appropriate actions.
Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances
The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations.
