52 citations · 224 across the 34 of their papers we have counts for
6 papers · 2 filters
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
Peter West, Ronan Le Bras, Taylor Sorensen +8
We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models. Compared to previous knowledge models, NovaCOMET all…
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…
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi +8
In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on pa…
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…
Continual Dialogue State Tracking via Example-Guided Question Answering
Hyundong Cho, Andrea Madotto, Zhaojiang Lin +5
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on prev…
Curriculum Script Distillation for Multilingual Visual Question Answering
Khyathi Raghavi Chandu, Alborz Geramifard
Pre-trained models with dual and cross encoders have shown remarkable success in propelling the landscape of several tasks in vision and language in Visual Question Answering (VQA)…