Search Results for author:
Found 22 papers, 14 papers with code
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
In this paper, we introduce PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels.
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
To investigate this question, we present the first extensive robustness evaluation of LLMs in commonsense reasoning.
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings
CodeElo benchmark is mainly based on the official CodeForces platform and tries to align with the platform as much as possible.
Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs
Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed.
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #7 on
on GPQA
Evaluating and Aligning CodeLLMs on Human Preference
Code large language models (codeLLMs) have made significant strides in code generation.
Language Models can Self-Lengthen to Generate Long Texts
The framework consists of two roles: the Generator and the Extender.
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e. g., OpenAI o1 achieves 94. 8\% on MATH dataset), indicating their inadequacy for truly challenging these models.
Qwen2.5-Coder Technical Report
In this report, we introduce the Qwen2. 5-Coder series, a significant upgrade from its predecessor, CodeQwen1. 5.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?
Furthermore, we introduce the concept of consistency score to quantitatively measure this inconsistency and analyze the potential for improvement in consistency by relative consistency score.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Transferring General Multimodal Pretrained Models to Text Recognition
This paper proposes a new method, OFA-OCR, to transfer multimodal pretrained models to text recognition.
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining.
Ranked #1 on
Zero-shot Image Retrieval
on MUGE Retrieval
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation
Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness.
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications.
Graph-based Multi-hop Reasoning for Long Text Generation
The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.
InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining
We pretrain the model with three pretraining tasks, including masked segment modeling (MSM), masked region modeling (MRM) and image-text matching (ITM); and finetune the model on a series of vision-and-language downstream tasks.
Towards Knowledge-Based Recommender Dialog System
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.
Ranked #5 on
Text Generation
on ReDial
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.
