72 citations · 492 across the 44 of their papers we have counts for
15 papers · 1 filter
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Zhuosheng Zhang, Yao Yao, Aston Zhang +8
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of…
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Xiangru Tang, Anni Zou, Zhuosheng Zhang +5
Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare. This field faces unique c…
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Xiangru Tang, Yuliang Liu, Zefan Cai +21
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming…
Investigating Data Contamination in Modern Benchmarks for Large Language Models
Chunyuan Deng, Yilun Zhao, Xiangru Tang +2
Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs, raising concerns about potential contamination of evaluat…
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…
Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models
Anni Zou, Zhuosheng Zhang, Hai Zhao +1
Large language models (LLMs) have unveiled remarkable reasoning capabilities by exploiting chain-of-thought (CoT) prompting, which generates intermediate reasoning chains to serve…
Agents: An Open-source Framework for Autonomous Language Agents
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +15
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with e…
BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models
Xiangru Tang, Bill Qian, Rick Gao +3
Pre-trained large language models (LLMs) have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate…
OctoPack: Instruction Tuning Code Large Language Models
Niklas Muennighoff, Qian Liu, Armel Zebaze +7
Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the nat…
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye +16
Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to…
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Yilun Zhao, Chen Zhao, Linyong Nan +5
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task…
GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning
Xiangru Tang, Andrew Tran, Jeffrey Tan +1
This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared task, encompassing both subtask A and subtask B. We approach the task as a dialogue summarization probl…
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
Yilun Zhao, Haowei Zhang, Shengyun Si +3
Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The ad…
QTSumm: Query-Focused Summarization over Tabular Data
Yilun Zhao, Zhenting Qi, Linyong Nan +9
People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' informat…
RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Anthony +31
Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence leng…