Search Results for author:
Found 15 papers, 10 papers with code
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature.
Ranked #12 on
Math Word Problem Solving
on MATH
(using extra training data)
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
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 #14 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics.
Ranked #11 on
Math Word Problem Solving
on MATH
(using extra training data)
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
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.
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
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.
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
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
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
Open-domain questions are likely to be open-ended and ambiguous, leading to multiple valid answers.
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering
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).
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text
In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods.
CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation
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.
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
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.
