56 citations · 200 across the 25 of their papers we have counts for
25 papers
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
Bingxiang He, Ning Ding, Cheng Qian +10
Understanding alignment techniques begins with comprehending zero-shot generalization brought by instruction tuning, but little of the mechanism has been understood. Existing work…
Scaling Large Language Model-based Multi-Agent Collaboration
Chen Qian, Zihao Xie, YiFei Wang +9
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Ins…
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Tianyu Yu, Haoye Zhang, Qiming Li +13
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models. This leaves the community without foundational…
Yi: Open Foundation Models by 01.AI
01. AI, :, Alex Young +30
We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pret…
Experiential Co-Learning of Software-Developing Agents
Chen Qian, Yufan Dang, Jiahao Li +9
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario…
Empowering Private Tutoring by Chaining Large Language Models
Yulin Chen, Ning Ding, Hai-Tao Zheng +3
Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powe…
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su +5
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have…
Rational Decision-Making Agent with Internalized Utility Judgment
Yining Ye, Xin Cong, Shizuo Tian +5
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step…
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation
Hao Peng, Xiaozhi Wang, Feng Yao +5
Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes two subtasks: event detection (ED) and event argument extraction (EAE). In this paper…
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng +9
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize…
Exploring Lottery Prompts for Pre-trained Language Models
Yulin Chen, Ning Ding, Xiaobin Wang +4
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Give…
VPGTrans: Transfer Visual Prompt Generator across LLMs
Ao Zhang, Hao Fei, Yuan Yao +4
While developing a new multimodal LLM (MLLM) by pre-training on tremendous image-text pairs from scratch can be exceedingly resource-consuming, connecting an existing LLM with a co…
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder
Zihao Fu, Wai Lam, Qian Yu +4
The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence. Traditionally, most of the seq2seq task is resolved by the…
Finding Skill Neurons in Pre-trained Transformer-based Language Models
Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang +3
Transformer-based pre-trained language models have demonstrated superior performance on various natural language processing tasks. However, it remains unclear how the skills requir…
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
Xiaozhi Wang, Yulin Chen, Ning Ding +9
The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two…
Few-shot Classification with Hypersphere Modeling of Prototypes
Ning Ding, Yulin Chen, Ganqu Cui +4
Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct c…
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
Hao Peng, Xiaozhi Wang, Shengding Hu +5
Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained lang…
Effective Few-Shot Named Entity Linking by Meta-Learning
Xiuxing Li, Zhenyu Li, Zhengyan Zhang +5
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g.,…
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Wanjun Zhong, Yifan Gao, Ning Ding +6
Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skill…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
Multi-modal application: Image Memes Generation
Zhiyuan Liu, Chuanzheng Sun, Yuxin Jiang +2
Meme is an interesting word. Internet memes offer unique insights into the changes in our perception of the world, the media and our own lives. If you surf the Internet for long en…
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
Yuan Yao, Qingxiu Dong, Jian Guan +32
Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…
Program Transfer for Answering Complex Questions over Knowledge Bases
Shulin Cao, Jiaxin Shi, Zijun Yao +6
Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the fin…
Coreferential Reasoning Learning for Language Representation
Deming Ye, Yankai Lin, Jiaju Du +4
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of…