35 citations · 114 across the 31 of their papers we have counts for
31 papers
BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
Xinghao Wang, Pengyu Wang, Bo Wang +3
Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enha…
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
Yuhong Sun, Zhangyue Yin, Qipeng Guo +3
Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous…
Training-Free Long-Context Scaling of Large Language Models
Chenxin An, Fei Huang, Jun Zhang +4
The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the e…
Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
Yuhang Zhou, Yuchen Ni, Yunhui Gan +7
Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e…
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
Xinghao Wang, Junliang He, Pengyu Wang +3
Contrastive-learning-based methods have dominated sentence representation learning. These methods regularize the representation space by pulling similar sentence representations cl…
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
Pengyu Wang, Dong Zhang, Linyang Li +5
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the…
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
Dong Zhang, Zhaowei Li, Pengyu Wang +3
Human communication is a complex and diverse process that not only involves multiple factors such as language, commonsense, and cultural backgrounds but also requires the participa…
-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…
Mitigating Negative Style Transfer in Hybrid Dialogue System
Shimin Li, Qinyuan Cheng, Linyang Li +1
As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growi…
Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next
Xiaotian Zhang, Yanjun Zheng, Hang Yan +1
While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous…
Word-Level Representation From Bytes For Language Modeling
Chu-Tak Lee, Qipeng Guo, Xipeng Qiu
Modern language models mostly take sub-words as input, a design that balances the trade-off between vocabulary size, number of parameters, and performance. However, sub-word tokeni…