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Found 21 papers, 9 papers with code
Dataset Quantization
Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.
Thespian: Multi-Character Text Role-Playing Game Agents
Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents.
Ambient Adventures: Teaching ChatGPT on Developing Complex Stories
Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way.
Dialogue Shaping: Empowering Agents through NPC Interaction
One major challenge in reinforcement learning (RL) is the large amount of steps for the RL agent needs to converge in the training process and learn the optimal policy, especially in text-based game environments where the action space is extensive.
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
To solve this problem, we propose \textbf{InfoBatch}, a novel framework aiming to achieve lossless training acceleration by unbiased dynamic data pruning.
Story Shaping: Teaching Agents Human-like Behavior with Stories
We introduce a technique, Story Shaping, in which a reinforcement learning agent infers tacit knowledge from an exemplar story of how to accomplish a task and intrinsically rewards itself for performing actions that make its current environment adhere to that of the inferred story world.
Neuro-Symbolic World Models for Adapting to Open World Novelty
Additionally, WorldCloner augments the policy learning process using imagination-based adaptation, where the world model simulates transitions of the post-novelty environment to help the policy adapt.
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning
Through this way, SESoM inherits the superior generalization of model ensemble approaches and simultaneously captures the sample-specific competence of each source prompt.
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain.
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE.
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well.
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
We provide an ontology of for novelties most relevant to sequential decision making, which distinguishes between novelties that affect objects versus actions, unary properties versus non-unary relations, and the distribution of solutions to a task.
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
XFBoost: Improving Text Generation with Controllable Decoders
Multimodal conditionality in transformer-based natural language models has demonstrated state-of-the-art performance in the task of product description generation.
Crafting Better Contrastive Views for Siamese Representation Learning
For high performance Siamese representation learning, one of the keys is to design good contrastive pairs.
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.
Inherently Explainable Reinforcement Learning in Natural Language
We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud while performing a task and analyzing entire trajectories post-hoc to produce causal explanations.
Detecting and Adapting to Novelty in Games
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules".
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results.
Automatic Story Generation: Challenges and Attempts
The scope of this survey paper is to explore the challenges in automatic story generation.
Reducing Non-Normative Text Generation from Language Models
Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
