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Found 10 papers, 7 papers with code
PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion
The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in real-world situations.
PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs.
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation
In this paper, we propose a new framework called Evaluation-guided Iterative Plan Extraction for long-form narrative text generation (EIPE-text), which extracts plans from the corpus of narratives and utilizes the extracted plans to construct a better planner.
Class Incremental Learning via Likelihood Ratio Based Task Prediction
An emerging theory-guided approach (called TIL+OOD) is to train a task-specific model for each task in a shared network for all tasks based on a task-incremental learning (TIL) method to deal with catastrophic forgetting.
Class-Incremental Learning based on Label Generation
Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF).
Dealing with Cross-Task Class Discrimination in Online Continual Learning
A novel optimization objective with a gradient-based adaptive method is proposed to dynamically deal with the problem in the online CL process.
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast
Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages.
Learning to Plan with Natural Language
To obtain it, we propose the Learning to Plan method, which involves two phases: (1) In the first learning task plan phase, it iteratively updates the task plan with new step-by-step solutions and behavioral instructions, which are obtained by prompting LLMs to derive from training error feedback.
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92. 5% accuracy on the English test of the Chinese national college entrance exam.
Gradient Imbalance and solution in Online Continual learning
Most existing techniques for online continual learning are based on experience-replay.
