28 citations · 47 across the 18 of their papers we have counts for
19 papers
ZigzagAttention: Efficient Long-Context Inference with Exclusive Retrieval and Streaming Heads
Zhuorui Liu, Chen Zhang, Dawei Song
With the rapid development of large language models (LLMs), handling long context has become one of the vital abilities in LLMs. Such long-context ability is accompanied by difficu…
WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference
Youhui Zuo, Sibo Wei, Chen Zhang +3
With the advancements in long-context inference capabilities of large language models (LLMs), the KV cache has become one of the foundational components. However, its substantial G…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…
Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models
Chen Zhang, Zhuorui Liu, Dawei Song
With the increasingly giant scales of (causal) large language models (LLMs), the inference efficiency comes as one of the core concerns along the improved performance. In contrast…
On Elastic Language Models
Chen Zhang, Benyou Wang, Dawei Song
Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. Knowledge distillation offers…
Sparse Contrastive Learning of Sentence Embeddings
Ruize An, Chen Zhang, Dawei Song
Recently, SimCSE has shown the feasibility of contrastive learning in training sentence embeddings and illustrates its expressiveness in spanning an aligned and uniform embedding s…
Towards the Law of Capacity Gap in Distilling Language Models
Chen Zhang, Qiuchi Li, Dawei Song +3
Language model (LM) distillation aims at distilling the knowledge in a large teacher LM to a small student one. As a critical issue facing LM distillation, a superior student often…
Task-agnostic Distillation of Encoder-Decoder Language Models
Chen Zhang, Yang Yang, Jingang Wang +1
Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks. The intriguing task-agnostic property has driven a shifted focus from ta…
Lifting the Curse of Capacity Gap in Distilling Language Models
Chen Zhang, Yang Yang, Jiahao Liu +4
Pretrained language models (LMs) have shown compelling performance on various downstream tasks, but unfortunately they require a tremendous amount of inference compute. Knowledge d…
Modular Retrieval for Generalization and Interpretation
Juhao Liang, Chen Zhang, Zhengyang Tang +3
New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource…
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation
Hanqing Zhang, Dawei Song
Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG). However, vanilla prompt tuning tends t…
Sparse Teachers Can Be Dense with Knowledge
Yi Yang, Chen Zhang, Dawei Song
Recent advances in distilling pretrained language models have discovered that, besides the expressiveness of knowledge, the student-friendliness should be taken into consideration…
XPrompt: Exploring the Extreme of Prompt Tuning
Fang Ma, Chen Zhang, Lei Ren +5
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gr…
Structural Bias for Aspect Sentiment Triplet Extraction
Chen Zhang, Lei Ren, Fang Ma +3
Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly inco…
Aspect-specific Context Modeling for Aspect-based Sentiment Analysis
Fang Ma, Chen Zhang, Bo Zhang +1
Aspect-based sentiment analysis (ABSA) aims at predicting sentiment polarity (SC) or extracting opinion span (OE) expressed towards a given aspect. Previous work in ABSA mostly rel…
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
Yi Yang, Chen Zhang, Benyou Wang +1
Over-parameterized models, typically pretrained language models (LMs), have shown an appealing expressive power due to their small learning bias. However, the huge learning capacit…
MiniDisc: Minimal Distillation Schedule for Language Model Compression
Chen Zhang, Yang Yang, Qifan Wang +4
Recent studies have uncovered that language model distillation is less effective when facing a large capacity gap between the teacher and the student, and introduced teacher assist…
Making Pretrained Language Models Good Long-tailed Learners
Chen Zhang, Lei Ren, Jingang Wang +2
Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge. This motivates us to check the…
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models
Hanqing Zhang, Haolin Song, Shaoyu Li +2
Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation tec…