Search Results for author: Yinghao Li

Found 17 papers, 13 papers with code

ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models

1 code implementation17 Mar 2024 Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang

Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER).

Attribute named-entity-recognition +2

A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction

1 code implementation20 Feb 2024 Yinghao Li, Rampi Ramprasad, Chao Zhang

It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses.

Language Modelling named-entity-recognition +4

TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance

no code implementations24 Jan 2024 Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, Chao Zhang

The teacher LLM generates problem-solving instructions and corrective principles based on the student LLM's errors.

Language Modelling

TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer

no code implementations14 Nov 2023 Huashan Sun, Yixiao Wu, Yinghao Li, Jiawei Li, Yizhe Yang, Yang Gao

In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.

Style Transfer Text Style Transfer

Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study

1 code implementation13 Nov 2023 Yinghao Li, Haorui Wang, Chao Zhang

Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific fine-tuning or prompt engineering.

Logical Reasoning Prompt Engineering

PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

1 code implementation13 Nov 2023 Jerry Junyang Cheung, Yuchen Zhuang, Yinghao Li, Pranav Shetty, Wantian Zhao, Sanjeev Grampurohit, Rampi Ramprasad, Chao Zhang

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever.

Relation Extraction

MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

no code implementations24 Oct 2023 Yizhe Yang, Huashan Sun, Jiawei Li, Runheng Liu, Yinghao Li, Yuhang Liu, Heyan Huang, Yang Gao

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.

Language Modelling Large Language Model

MUBen: Benchmarking the Uncertainty of Molecular Representation Models

2 code implementations14 Jun 2023 Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.

Benchmarking Drug Discovery +4

Extracting Shopping Interest-Related Product Types from the Web

no code implementations23 May 2023 Yinghao Li, Colin Lockard, Prashant Shiralkar, Chao Zhang

To establish such connections, we propose to extract PTs from the Web pages containing hand-crafted PT recommendations for SIs.

Node Classification

SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling

1 code implementation7 Aug 2022 Mengyang Liu, Haozheng Luo, Leonard Thong, Yinghao Li, Chao Zhang, Le Song

Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.

Denoising named-entity-recognition +3

Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition

1 code implementation27 May 2022 Yinghao Li, Le Song, Chao Zhang

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.

Named Entity Recognition Named Entity Recognition (NER) +1

WRENCH: A Comprehensive Benchmark for Weak Supervision

1 code implementation23 Sep 2021 Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

2 code implementations ACL 2021 Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song

To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.

named-entity-recognition Named Entity Recognition +2

Transformer-Based Neural Text Generation with Syntactic Guidance

1 code implementation5 Oct 2020 Yinghao Li, Rui Feng, Isaac Rehg, Chao Zhang

We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation.

Decoder Text Generation

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

1 code implementation18 Jun 2020 Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, Chao Zhang

We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.

Taxonomy Expansion

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