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20212026
most citedWhat Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

15 citations · 32 across the 18 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CL2024★ 1 cited

CareBot: A Pioneering Full-Process Open-Source Medical Language Model

Lulu Zhao, Weihao Zeng, Xiaofeng Shi +1

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific…

cs.LG2024

MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning

Lulu Zhao, Weihao Zeng, Xiaofeng Shi +1

Recently, LoRA has emerged as a crucial technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. In contrast, the…

cs.AI2024★ 1 cited

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Weihao Zeng, Yuzhen Huang, Lulu Zhao +3

In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method f…

cs.CL2024★ 1 cited

Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models

Lulu Zhao, Weihao Zeng, Xiaofeng Shi +3

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific…

cs.CL2024★ 3 cited

Automatic Instruction Evolving for Large Language Models

Weihao Zeng, Can Xu, Yingxiu Zhao +2

Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging results across a wide range of tasks. However, designing effective evolving methods for in…

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

DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations

Weihao Zeng, Dayuan Fu, Keqing He +3

Language models pre-trained on general text have achieved impressive results in diverse fields. Yet, the distinct linguistic characteristics of task-oriented dialogues (TOD) compar…

cs.CL2024

BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses

Weihao Zeng, Keqing He, Yejie Wang +2

Pre-trained language models have been successful in many scenarios. However, their usefulness in task-oriented dialogues is limited due to the intrinsic linguistic differences betw…

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…