activity
20172025
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 892 across the 17 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

cs.CL2022

Rank-One Editing of Encoder-Decoder Models

Vikas Raunak, Arul Menezes

Large sequence to sequence models for tasks such as Neural Machine Translation (NMT) are usually trained over hundreds of millions of samples. However, training is just the origin…

cs.HC2022

Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models

Vikas Raunak, Matt Post, Arul Menezes

In this work, we present some recommendations on the evaluation of state-of-the-art generative models for constrained generation tasks. The progress on generative models has been r…

cs.CL2022

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…

cs.CL2022

Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks

Vikas Raunak, Arul Menezes

Memorization presents a challenge for several constrained Natural Language Generation (NLG) tasks such as Neural Machine Translation (NMT), wherein the proclivity of neural models…

cs.CL2022★ 3 cited

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…

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…

cs.CL2022★ 7 cited

SALTED: A Framework for SAlient Long-Tail Translation Error Detection

Vikas Raunak, Matt Post, Arul Menezes

Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT. Examples include t…