Search Results for author:
Found 9 papers, 3 papers with code
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-specific state-of-the-art models.
Towards Unified Alignment Between Agents, Humans, and Environment
We also conduct proof-of-concept studies by introducing realistic features to WebShop, including user profiles to demonstrate intentions, personalized reranking for complex environmental dynamics, and runtime cost statistics to reflect self-constraints.
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos
Multimodal counterfactual reasoning is a vital yet challenging ability for AI systems.
Learning Action Conditions from Instructional Manuals for Instruction Understanding
The ability to infer pre- and postconditions of an action is vital for comprehending complex instructions, and is essential for applications such as autonomous instruction-guided agents and assistive AI that supports humans to perform physical tasks.
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.
Modeling Fuzzy Cluster Transitions for Topic Tracing
Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks.
Tracing Topic Transitions with Temporal Graph Clusters
Twitter serves as a data source for many Natural Language Processing (NLP) tasks.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis).
Natural Language Inference
Natural Language Understanding
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Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer
SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.
