2.3k citations · 3.5k across the 11 of their papers we have counts for
7 papers · 1 filter
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5
Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…
Trustworthy Social Bias Measurement
Rishi Bommasani, Percy Liang
How do we design measures of social bias that we trust? While prior work has introduced several measures, no measure has gained widespread trust: instead, mounting evidence argues…
Evaluation for Change
Rishi Bommasani
Evaluation is the central means for assessing, understanding, and communicating about NLP models. In this position paper, we argue evaluation should be more than that: it is a forc…
Evaluating Human-Language Model Interaction
Mina Lee, Megha Srivastava, Amelia Hardy +15
Many real-world applications of language models (LMs), such as writing assistance and code autocomplete, involve human-LM interaction. However, most benchmarks are non-interactive…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani +13
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictabl…
Generalized Optimal Linear Orders
Rishi Bommasani
The sequential structure of language, and the order of words in a sentence specifically, plays a central role in human language processing. Consequently, in designing computational…