Search Results for author: Zi Lin

Found 20 papers, 10 papers with code

Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing

no code implementations CL (ACL) 2021 Junjie Cao, Zi Lin, Weiwei Sun, Xiaojun Wan

Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models.

Semantic Parsing

Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty

no code implementations Findings (ACL) 2022 Zi Lin, Jeremiah Zhe Liu, Jingbo Shang

Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations.

Semantic Parsing

ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

no code implementations26 Oct 2023 Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, Jingbo Shang

This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content.


Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

no code implementations18 Oct 2023 Yongqi Tong, Yifan Wang, Dawei Li, Sizhe Wang, Zi Lin, Simeng Han, Jingbo Shang

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic.

Natural Language Inference

Critique Ability of Large Language Models

no code implementations7 Oct 2023 Liangchen Luo, Zi Lin, Yinxiao Liu, Lei Shu, Yun Zhu, Jingbo Shang, Lei Meng

In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.

Code Completion Decision Making +3

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

1 code implementation21 Sep 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.

Chatbot Instruction Following

Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis

no code implementations17 Aug 2023 Yuguang Duan, Zi Lin, Weiwei Sun

The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena.

Semantic Role Labeling

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

5 code implementations NeurIPS 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.

Chatbot Language Modelling +2

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification

1 code implementation26 Jan 2023 Zi Lin, Jeremiah Liu, Jingbo Shang

Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples.

Semantic Parsing Uncertainty Quantification

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness

2 code implementations1 May 2022 Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles.

Data Augmentation Probabilistic Deep Learning +1

Large-Scale Generative Data-Free Distillation

no code implementations10 Dec 2020 Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard

Knowledge distillation is one of the most popular and effective techniques for knowledge transfer, model compression and semi-supervised learning.

Knowledge Distillation Model Compression +1

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior

1 code implementation Findings of the Association for Computational Linguistics 2020 Zi Lin, Jeremiah Zhe Liu, Zi Yang, Nan Hua, Dan Roth

Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero.

Fast Structured Decoding for Sequence Models

1 code implementation NeurIPS 2019 Zhiqing Sun, Zhuohan Li, Haoqing Wang, Zi Lin, Di He, Zhi-Hong Deng

However, these models assume that the decoding process of each token is conditionally independent of others.

Machine Translation Sentence +1

Hint-Based Training for Non-Autoregressive Machine Translation

1 code implementation IJCNLP 2019 Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao Qin, Li-Wei Wang, Tie-Yan Liu

Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency.

Machine Translation Translation

Parsing Meaning Representations: Is Easier Always Better?

no code implementations WS 2019 Zi Lin, Nianwen Xue

The parsing accuracy varies a great deal for different meaning representations.

A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers

no code implementations4 Jul 2019 Junjie Cao, Zi Lin, Weiwei Sun, Xiaojun Wan

We present a phenomenon-oriented comparative analysis of the two dominant approaches in task-independent semantic parsing: classic, knowledge-intensive and neural, data-intensive models.

Semantic Parsing

Implanting Rational Knowledge into Distributed Representation at Morpheme Level

no code implementations26 Nov 2018 Zi Lin, Yang Liu

Previously, researchers paid no attention to the creation of unambiguous morpheme embeddings independent from the corpus, while such information plays an important role in expressing the exact meanings of words for parataxis languages like Chinese.

Word Similarity

Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data

1 code implementation EMNLP 2018 Zi Lin, Yuguang Duan, Yuan-Yuan Zhao, Weiwei Sun, Xiaojun Wan

This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language.

Semantic Parsing Semantic Role Labeling +1

Cannot find the paper you are looking for? You can Submit a new open access paper.