Search Results for author: Yuchen Eleanor Jiang

Found 13 papers, 10 papers with code

AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning

1 code implementation10 Jan 2024 Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, Huajun Chen

To this end, we introduce AutoAct, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models (e. g., GPT-4).

Question Answering

Agents: An Open-source Framework for Autonomous Language Agents

1 code implementation14 Sep 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu, Shuai Wang, Shiding Zhu, Jiyu Chen, Wentao Zhang, Xiangru Tang, Ningyu Zhang, Huajun Chen, Peng Cui, Mrinmaya Sachan

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

2 code implementations24 May 2023 Tianyi Tang, Hongyuan Lu, Yuchen Eleanor Jiang, Haoyang Huang, Dongdong Zhang, Wayne Xin Zhao, Tom Kocmi, Furu Wei

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.

Machine Translation nlg evaluation +3

RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

2 code implementations22 May 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui, Tiannan Wang, Zhenxin Xiao, Yifan Hou, Ryan Cotterell, Mrinmaya Sachan

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.

Language Modelling Large Language Model

Efficient Prompting via Dynamic In-Context Learning

no code implementations18 May 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Ryan Cotterell, Mrinmaya Sachan

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.

In-Context Learning

Controlled Text Generation with Natural Language Instructions

1 code implementation27 Apr 2023 Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, Mrinmaya Sachan

Large language models generate fluent texts and can follow natural language instructions to solve a wide range of tasks without task-specific training.

In-Context Learning Language Modelling +1

A Bilingual Parallel Corpus with Discourse Annotations

1 code implementation26 Oct 2022 Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Mrinmaya Sachan, Ryan Cotterell

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.

Document Level Machine Translation Machine Translation +2

Investigating the Role of Centering Theory in the Context of Neural Coreference Resolution Systems

no code implementations26 Oct 2022 Yuchen Eleanor Jiang, Ryan Cotterell, Mrinmaya Sachan

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.

coreference-resolution World Knowledge

A Structured Span Selector

1 code implementation NAACL 2022 Tianyu Liu, Yuchen Eleanor Jiang, Ryan Cotterell, Mrinmaya Sachan

Many natural language processing tasks, e. g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them.

coreference-resolution Inductive Bias +1

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