252 citations · 887 across the 29 of their papers we have counts for
9 papers · 1 filter
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Wenhao Yu, Hongming Zhang, Xiaoman Pan +3
Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveragin…
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…
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
Wenhao Yu, Meng Jiang, Peter Clark +1
Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult…
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…
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
Zhenwen Liang, Wenhao Yu, Tanmay Rajpurohit +3
In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our…
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…
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…