41 citations · 174 across the 34 of their papers we have counts for
6 papers · 1 filter
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu +5
The alignment tuning process of large language models (LLMs) typically involves instruction learning through supervised fine-tuning (SFT) and preference tuning via reinforcement le…
MacGyver: Are Large Language Models Creative Problem Solvers?
Yufei Tian, Abhilasha Ravichander, Lianhui Qin +6
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting o…
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
Da Yin, Faeze Brahman, Abhilasha Ravichander +4
Closed-source agents suffer from several issues such as a lack of affordability, transparency, and reproducibility, particularly on complex interactive tasks. This motivates the de…
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Peter West, Ximing Lu, Nouha Dziri +11
The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…
What's In My Big Data?
Yanai Elazar, Akshita Bhagia, Ian Magnusson +10
Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social fact…
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Ximing Lu, Faeze Brahman, Peter West +14
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure promptin…