activity
20242026
most citedKimi K2.5: Visual Agentic Intelligence

2 citations · 3 across the 17 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

From Simple to Professional: A Combinatorial Controllable Image Captioning Agent

Xinran Wang, Muxi Diao, Baoteng Li +3

The Controllable Image Captioning Agent (CapAgent) is an innovative system designed to bridge the gap between user simplicity and professional-level outputs in image captioning tas…

cs.SE2024★ 1 cited

How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data

Yejie Wang, Keqing He, Dayuan Fu +11

Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit hi…

cs.CL2024

SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models

Muxi Diao, Rumei Li, Shiyang Liu +4

As large language models (LLMs) continue to advance in capability and influence, ensuring their security and preventing harmful outputs has become crucial. A promising approach to…

cs.AI2024

We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Runqi Qiao, Qiuna Tan, Guanting Dong +15

Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks,…

cs.CL2024

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

Xiaoshuai Song, Muxi Diao, Guanting Dong +13

Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on b…

cs.CL2024

Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models

Pei Wang, Yejie Wang, Muxi Diao +3

In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model predictions. However, existing methods often fa…

cs.CL2024

DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning

Yejie Wang, Keqing He, Guanting Dong +8

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning approaches have been proposed to boost the code g…