95 citations · 307 across the 25 of their papers we have counts for
25 papers
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Zhihong Shao, Peiyi Wang, Qihao Zhu +8
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-…
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
DeepSeek-AI, :, Xiao Bi +85
The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions,…
Silkie: Preference Distillation for Large Visual Language Models
Lei Li, Zhihui Xie, Mukai Li +6
This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual contex…
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Peiyi Wang, Lei Li, Zhihong Shao +6
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solution…
Guiding AMR Parsing with Reverse Graph Linearization
Bofei Gao, Liang Chen, Peiyi Wang +2
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic gr…
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning
Zhe Yang, Damai Dai, Peiyi Wang +1
Large Language Models (LLMs) have recently gained the In-Context Learning (ICL) ability with the models scaling up, allowing them to quickly adapt to downstream tasks with only a f…
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…
MIT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
Lei Li, Yuwei Yin, Shicheng Li +9
Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress i…
RepCL: Exploring Effective Representation for Continual Text Classification
Yifan Song, Peiyi Wang, Dawei Zhu +3
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. In this work, we focus on continual text classificatio…
Enhancing Continual Relation Extraction via Classifier Decomposition
Heming Xia, Peiyi Wang, Tianyu Liu +3
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements hav…
Large Language Models are not Fair Evaluators
Peiyi Wang, Lei Li, Liang Chen +7
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of respo…
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.…
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) requires the model to continually learn new relations from class-incremental data streams. In this paper, we propose a Frustratingly easy but Ef…
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Runxin Xu, Peiyi Wang, Tianyu Liu +3
Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting eve…
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Liang Chen, Peiyi Wang, Runxin Xu +3
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enh…
SmartSales: Sales Script Extraction and Analysis from Sales Chatlog
Hua Liang, Tianyu Liu, Peiyi Wang +2
In modern sales applications, automatic script extraction and management greatly decrease the need for human labor to collect the winning sales scripts, which largely boost the suc…
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
Zihan Wang, Peiyi Wang, Tianyu Liu +4
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy. Recently, the pretrained language models (PLM)have…
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Zihan Wang, Peiyi Wang, Lianzhe Huang +2
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy. Existing methods encode text and label hierarchy separat…
Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
Heming Xia, Tao Ge, Peiyi Wang +3
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. Specu…
Hierarchical Curriculum Learning for AMR Parsing
Peiyi Wang, Liang Chen, Tianyu Liu +4
Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence…
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Peiyi Wang, Runxin Xu, Tianyu Liu +4
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce dom…
Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification
Peiyi Wang, Runxin Xu, Tianyu Liu +3
Few-Shot Event Classification (FSEC) aims at developing a model for event prediction, which can generalize to new event types with a limited number of annotated data. Existing FSEC…
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Peiyi Wang, Tianyu Liu, Damai Dai +3
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divi…
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Lianzhe Huang, Peiyi Wang, Sujian Li +5
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize…