110 citations · 306 across the 29 of their papers we have counts for
12 papers · 1 filter
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Le Yu, Bowen Yu, Haiyang Yu +2
In this paper, we unveil that Language Models (LMs) can acquire new capabilities by assimilating parameters from homologous models without retraining or GPUs. We first introduce DA…
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Wider and Deeper LLM Networks are Fairer LLM Evaluators
Xinghua Zhang, Bowen Yu, Haiyang Yu +5
Measuring the quality of responses generated by LLMs is a challenging task, particularly when it comes to evaluating whether the response is aligned with human preference. A novel…
A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment
Yingxiu Zhao, Bowen Yu, Binyuan Hui +4
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences. Extensive research has highli…
PolyLM: An Open Source Polyglot Large Language Model
Xiangpeng Wei, Haoran Wei, Huan Lin +15
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been pr…
Unified Language Representation for Question Answering over Text, Tables, and Images
Bowen Yu, Cheng Fu, Haiyang Yu +2
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data. Previous approaches to this problem have f…
Universal Information Extraction with Meta-Pretrained Self-Retrieval
Xin Cong. Bowen Yu, Mengcheng Fang, Tingwen Liu +5
Universal Information Extraction~(Universal IE) aims to solve different extraction tasks in a uniform text-to-structure generation manner. Such a generation procedure tends to stru…
Preference Ranking Optimization for Human Alignment
Feifan Song, Bowen Yu, Minghao Li +4
Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human f…
Domain Incremental Lifelong Learning in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +3
Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL mod…
Causal Document-Grounded Dialogue Pre-training
Yingxiu Zhao, Bowen Yu, Haiyang Yu +6
The goal of document-grounded dialogue (DocGD) is to generate a response by grounding the evidence in a supporting document in accordance with the dialogue context. This process in…
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Gehang Zhang, Bowen Yu, Jiangxia Cao +4
Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/gr…
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu +6
Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (…