activity
20222024
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 748 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2024★ 4 cited

Instruction Following without Instruction Tuning

John Hewitt, Nelson F. Liu, Percy Liang +1

Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction…

cs.CL2024★ 36 cited

BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text

Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga +8

Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameter…

cs.CL2024

Model Editing with Canonical Examples

John Hewitt, Sarah Chen, Lanruo Lora Xie +3

We introduce model editing with canonical examples, a setting in which (1) a single learning example is provided per desired behavior, (2) evaluation is performed exclusively out-o…

cs.CL2024★ 2 cited

When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…

cs.CL2023★ 10 cited

Fine-tuning Language Models for Factuality

Katherine Tian, Eric Mitchell, Huaxiu Yao +2

The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet languag…

cs.CL2023★ 3 cited

An Emulator for Fine-Tuning Large Language Models using Small Language Models

Eric Mitchell, Rafael Rafailov, Archit Sharma +2

Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-t…

cs.CL2022★ 128 cited

Holistic Evaluation of Language Models

Percy Liang, Rishi Bommasani, Tony Lee +47

Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…