200 citations · 247 across the 20 of their papers we have counts for
20 papers
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to…
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Zhihan Zhang, Tao Ge, Zhenwen Liang +5
Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses o…
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
Zhenwen Liang, Dian Yu, Wenhao Yu +4
Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats. However, real world…
Describe-then-Reason: Improving Multimodal Mathematical Reasoning through Visual Comprehension Training
Mengzhao Jia, Zhihan Zhang, Wenhao Yu +2
Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs but still struggle with complex multimodal mathematical reasoning, l…
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +4
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making…
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning
Zhihan Zhang, Dong-Ho Lee, Yuwei Fang +4
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource language…
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Zhihan Zhang, Shuohang Wang, Wenhao Yu +6
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the pe…
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions
Zhihan Zhang, Wenhao Yu, Zheng Ning +2
Contrast consistency, the ability of a model to make consistently correct predictions in the presence of perturbations, is an essential aspect in NLP. While studied in tasks such a…
Improving Language Models via Plug-and-Play Retrieval Feedback
Wenhao Yu, Zhihan Zhang, Zhenwen Liang +2
Large language models (LLMs) exhibit remarkable performance across various NLP tasks. However, they often generate incorrect or hallucinated information, which hinders their practi…
Large Language Models are Built-in Autoregressive Search Engines
Noah Ziems, Wenhao Yu, Zhihan Zhang +1
Document retrieval is a key stage of standard Web search engines. Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing…
Pre-training Language Models for Comparative Reasoning
Mengxia Yu, Zhihan Zhang, Wenhao Yu +1
Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a n…
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi +64
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase…
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
Wenhao Yu, Chenguang Zhu, Zhihan Zhang +4
A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and…
A Unified Encoder-Decoder Framework with Entity Memory
Zhihan Zhang, Wenhao Yu, Chenguang Zhu +1
Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informa…
On the Relationship Between Counterfactual Explainer and Recommender
Gang Liu, Zhihan Zhang, Zheng Ning +1
Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative…
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Zhihan Zhang, Wenhao Yu, Mengxia Yu +2
Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalitie…
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
Wenhao Yu, Chenguang Zhu, Lianhui Qin +3
Generative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text. Recent years have seen a surge of interest in improvin…
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
Zhihan Zhang, Xiubo Geng, Tao Qin +2
Procedural text describes dynamic state changes during a step-by-step natural process (e.g., photosynthesis). In this work, we focus on the task of procedural text understanding, w…
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Daheng Wang, Zhihan Zhang, Yihong Ma +4
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the no…
DCA: Diversified Co-Attention towards Informative Live Video Commenting
Zhihan Zhang, Zhiyi Yin, Shuhuai Ren +2
We focus on the task of Automatic Live Video Commenting (ALVC), which aims to generate real-time video comments with both video frames and other viewers' comments as inputs. A majo…