35 citations · 114 across the 31 of their papers we have counts for
14 papers · 1 filter
-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
Yuhang Zhou, Yu He, Siyu Tian +8
While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural lang…
Watermarking LLMs with Weight Quantization
Linyang Li, Botian Jiang, Pengyu Wang +3
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed. It is important to protect the model weights to avoid malicio…
PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Linyang Li, Ke Ren, Yunfan Shao +2
With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts m…
SeqXGPT: Sentence-Level AI-Generated Text Detection
Pengyu Wang, Linyang Li, Ke Ren +3
Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text…
Does Correction Remain A Problem For Large Language Models?
Xiaowu Zhang, Xiaotian Zhang, Cheng Yang +2
As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist?…
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Yuhao Dan, Zhikai Lei, Yiyang Gu +13
EduChat (https://www.educhat.top/) is a large-scale language model (LLM)-based chatbot system in the education domain. Its goal is to support personalized, fair, and compassionate…
Optimizing Non-Autoregressive Transformers with Contrastive Learning
Chenxin An, Jiangtao Feng, Fei Huang +2
Non-autoregressive Transformers (NATs) reduce the inference latency of Autoregressive Transformers (ATs) by predicting words all at once rather than in sequential order. They have…
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Shimin Li, Xiaotian Zhang, Yanjun Zheng +2
Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency…
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
Mozhi Zhang, Hang Yan, Yaqian Zhou +1
Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via limited annotated samples. Recently, prototypical networks have shown promising performance…
Improving Contrastive Learning of Sentence Embeddings from AI Feedback
Qinyuan Cheng, Xiaogui Yang, Tianxiang Sun +2
Contrastive learning has become a popular approach in natural language processing, particularly for the learning of sentence embeddings. However, the discrete nature of natural lan…
Evaluating the Performance of Large Language Models on GAOKAO Benchmark
Xiaotian Zhang, Chunyang Li, Yi Zong +12
Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensively and accurately assess their p…
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve
Xiaonan Li, Xipeng Qiu
Large Language Models (LLMs) have shown impressive abilities in various tasks. However, fundamentally improving them depends on high-quality datasets or computationally expensive f…
Origin Tracing and Detecting of LLMs
Linyang Li, Pengyu Wang, Ke Ren +2
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system. More importantly, while more a…
Finding Support Examples for In-Context Learning
Xiaonan Li, Xipeng Qiu
Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propos…