Search Results for author:
Found 11 papers, 7 papers with code
In-Context Editing: Learning Knowledge from Self-Induced Distributions
In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded generalization, and unnatural language generation.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications.
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Heterogeneous-Agent Reinforcement Learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research.
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
To assist form designers, in this work we present FormLM to model online forms (by enhancing pre-trained language model with form structural information) and recommend form creation ideas (including question / options recommendations and block type suggestion).
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
MRCLens: an MRC Dataset Bias Detection Toolkit
Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data.
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.
Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios
Existing multilingual speech NLP works focus on a relatively small subset of languages, and thus current linguistic understanding of languages predominantly stems from classical approaches.
