Search Results for author: Zhihong Shao

Found 13 papers, 9 papers with code

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

1 code implementation5 Feb 2024 Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y. K. Li, Y. Wu, Daya Guo

Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature.

Ranked #11 on Math Word Problem Solving on MATH (using extra training data)

Arithmetic Reasoning Math +1

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

1 code implementation14 Dec 2023 Peiyi Wang, Lei LI, Zhihong Shao, R. X. Xu, Damai Dai, Yifei Li, Deli Chen, Y. Wu, Zhifang Sui

In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions.

Ranked #13 on Arithmetic Reasoning on GSM8K (using extra training data)

Arithmetic Reasoning GSM8K +2

ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

1 code implementation29 Sep 2023 Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, Weizhu Chen

Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics.

Ranked #10 on Math Word Problem Solving on MATH (using extra training data)

Arithmetic Reasoning Computational Efficiency +3

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

no code implementations24 May 2023 Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

In this paper, we show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner.

Fact Verification Multi-hop Question Answering +2

CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

2 code implementations19 May 2023 Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, Weizhu Chen

Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging.

Fact Checking Natural Questions +4

Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models

no code implementations1 Feb 2023 Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

However, the quality of the prompts depends on the demonstrations given to the models, and creating many of them by hand is costly.

Chaining Simultaneous Thoughts for Numerical Reasoning

no code implementations29 Nov 2022 Zhihong Shao, Fei Huang, Minlie Huang

Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning over text should be an essential skill of AI systems.

Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework

1 code implementation ACL 2022 Zhihong Shao, Minlie Huang

Open-domain questions are likely to be open-ended and ambiguous, leading to multiple valid answers.

valid

A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering

1 code implementation ACL 2021 Zhihong Shao, Lifeng Shang, Qun Liu, Minlie Huang

This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e. g., producing wrong solutions or answers).

Question Answering

AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

no code implementations18 Dec 2020 Zhihong Shao, Zitao Liu, Jiyong Zhang, Zhongqin Wu, Minlie Huang

In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods.

Text Matching

CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation

1 code implementation3 Feb 2020 Fei Huang, Dazhen Wan, Zhihong Shao, Pei Ke, Jian Guan, Yilin Niu, Xiaoyan Zhu, Minlie Huang

In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions.

Text Generation

Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

2 code implementations IJCNLP 2019 Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, Xiaoyan Zhu

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions.

Data-to-Text Generation Sentence

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