Search Results for author:
Found 13 papers, 6 papers with code
Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages
(2) Are synthetic data from the same source equally effective, and which synthesis methods are most efficient?
What Are Step-Level Reward Models Rewarding? Counterintuitive Findings from MCTS-Boosted Mathematical Reasoning
Step-level reward models (SRMs) can significantly enhance mathematical reasoning performance through process supervision or step-level preference alignment based on reinforcement learning.
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding
Our work paves the way for more efficient exploitation of multi-step reasoning capabilities in LLMs across a wide range of applications.
A Declarative System for Optimizing AI Workloads
We describe the workload of AI-powered analytics tasks, the optimization methods that Palimpzest uses, and the prototype system itself.
Brain-Inspired Two-Stage Approach: Enhancing Mathematical Reasoning by Imitating Human Thought Processes
Although large language models demonstrate emergent abilities in solving math word problems, there is a challenging task in complex multi-step mathematical reasoning tasks.
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
Large language models (LLMs) are displaying emergent abilities for math reasoning tasks, and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT). In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability. Firstly, we determine the ability boundary of reasoning paths augmentation by identifying these paths' minimal optimal set. Secondly, we validate that different abilities of the model can be cumulatively enhanced by Mix of Minimal Optimal Sets of corresponding types of data, while our models MMOS achieve SOTA performance on series base models under much lower construction costs. Besides, we point out GSM-HARD is not really hard and today's LLMs no longer lack numerical robustness. Also, we provide an Auto Problem Generator for robustness testing and educational applications. Our code and data are publicly available at https://github. com/cyzhh/MMOS.
Ranked #2 on
Math Word Problem Solving
on ASDiv-A
(using extra training data)
SEED: Domain-Specific Data Curation With Large Language Models
As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e. g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples.
RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training
In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.
Lingua Manga: A Generic Large Language Model Centric System for Data Curation
Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks.
Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
DeepStruct: Pretraining of Language Models for Structure Prediction
We introduce a method for improving the structural understanding abilities of language models.
Ranked #1 on
Open Information Extraction
on Penn Treebank
Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design.
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
We cast a suite of information extraction tasks into a text-to-triple translation framework.
Ranked #1 on
Open Information Extraction
on OIE2016
(using extra training data)
