103 citations · 106 across the 7 of their papers we have counts for
7 papers
Same Voice, Different Lab: On the Homogenization of Frontier LLM Personalities
Avinash Krishna, Kalyana Chadalavada, Unso Eun Seo Jo
LLM assistant personalities play a critical role in user experience and perceived response quality. We present a large-scale experiment of frontier LLM personalities using external…
Are LLMs Overkill for Databases?: A Study on the Finiteness of SQL
Yue Li, David Mimno, Unso Eun Seo Jo
Translating natural language to SQL for data retrieval has become more accessible thanks to code generation LLMs. But how hard is it to generate SQL code? While databases can becom…
Agent Bain vs. Agent McKinsey: A New Text-to-SQL Benchmark for the Business Domain
Yue Li, Ran Tao, Derek Hommel +4
Text-to-SQL benchmarks have traditionally only tested simple data access as a translation task of natural language to SQL queries. But in reality, users tend to ask diverse questio…
Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning
Yusuf Denizay Dönder, Derek Hommel, Andrea W Wen-Yi +2
LLMs are effective at code generation tasks like text-to-SQL, but is it worth the cost? Many state-of-the-art approaches use non-task-specific LLM techniques including Chain-of-Tho…
Do Chinese models speak Chinese languages?
Andrea W Wen-Yi, Unso Eun Seo Jo, David Mimno
The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they support…
How Chinese are Chinese Language Models? The Puzzling Lack of Language Policy in China's LLMs
Andrea W Wen-Yi, Unso Eun Seo Jo, Lu Jia Lin +1
Contemporary language models are increasingly multilingual, but Chinese LLM developers must navigate complex political and business considerations of language diversity. Language p…
Efficient Few-Shot Learning Without Prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo +4
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, t…