Search Results for author:
Found 8 papers, 4 papers with code
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
With the increasing demand for substantial amounts of high-quality data to train large language models (LLMs), efficiently filtering large web corpora has become a critical challenge.
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark
This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean.
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplification of problem-solving ability.
Evalverse: Unified and Accessible Library for Large Language Model Evaluation
This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework.
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core.
sDPO: Don't Use Your Data All at Once
As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important.
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
We introduce SOLAR 10. 7B, a large language model (LLM) with 10. 7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks.
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together.
