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Found 10 papers, 3 papers with code
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner.
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales
Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literature analyzing such biases in children stories.
EnDex: Evaluation of Dialogue Engagingness at Scale
We propose EnDex, the first human-reaction based model to evaluate dialogue engagingness.
NECE: Narrative Event Chain Extraction Toolkit
To understand a narrative, it is essential to comprehend the temporal event flows, especially those associated with main characters; however, this can be challenging with lengthy and unstructured narrative texts.
Non-Parallel Text Style Transfer with Self-Parallel Supervision
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained.
Can Model Compression Improve NLP Fairness
Model compression techniques are receiving increasing attention; however, the effect of compression on model fairness is still under explored.
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings, with focuses on context-sensitive unsafety, which is under-explored in prior works.
Mitigating Political Bias in Language Models Through Reinforced Calibration
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings.
Enhanced Offensive Language Detection Through Data Augmentation
In this work, we present Dager (Data Augmenter), a generation-based data augmentation method, that improves the performance of classification on imbalanced and low-resource data such as the offensive language dataset.
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
For instance, Data Boost improves F1 for the three tasks by 8. 7% on average when given only 10% of the whole data for training.
