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Found 13 papers, 10 papers with code
Weaver: Foundation Models for Creative Writing
Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models.
AUTOACT: Automatic Agent Learning from Scratch via Self-Planning
Further analysis demonstrates the effectiveness of the division-of-labor strategy, with the trajectory quality generated by AutoAct significantly outperforming that of others.
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers.
Efficient Prompting via Dynamic In-Context Learning
To achieve this, we train a meta controller that predicts the number of in-context examples suitable for the generalist model to make a good prediction based on the performance-efficiency trade-off for a specific input.
Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus
Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages.
Controlled Text Generation with Natural Language Instructions
Large language models generate fluent texts and can follow natural language instructions to solve a wide range of tasks without task-specific training.
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference.
Investigating the Role of Centering Theory in the Context of Neural Coreference Resolution Systems
Our analysis further shows that contextualized embeddings contain much of the coherence information, which helps explain why CT can only provide little gains to modern neural coreference resolvers which make use of pretrained representations.
A Bilingual Parallel Corpus with Discourse Annotations
The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena.
A Structured Span Selector
Many natural language processing tasks, e. g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Standard automatic metrics, e. g. BLEU, are not reliable for document-level MT evaluation.
