18 citations · 22 across the 12 of their papers we have counts for
16 papers
Syntactic Control of Language Models by Posterior Inference
Vicky Xefteri, Tim Vieira, Ryan Cotterell +1
Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability, yet it remains…
Better Estimation of the Kullback--Leibler Divergence Between Language Models
Afra Amini, Tim Vieira, Ryan Cotterell
Estimating the Kullback--Leibler (KL) divergence between language models has many applications, e.g., reinforcement learning from human feedback (RLHF), interpretability, and knowl…
Reverse-Engineering the Reader
Samuel Kiegeland, Ethan Gotlieb Wilcox, Afra Amini +2
Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper…
Variational Best-of-N Alignment
Afra Amini, Tim Vieira, Elliott Ash +1
Best-of-N (BoN) is a popular and effective algorithm for aligning language models to human preferences. The algorithm works as follows: at inference time, N samples are drawn from…
The Role of -gram Smoothing in the Age of Neural Networks
Luca Malagutti, Andrius Buinovskij, Anej Svete +3
For nearly three decades, language models derived from the -gram assumption held the state of the art on the task. The key to their success lay in the application of various smo…
Direct Preference Optimization with an Offset
Afra Amini, Tim Vieira, Ryan Cotterell
Direct preference optimization (DPO) is a successful fine-tuning strategy for aligning large language models with human preferences without the need to train a reward model or empl…
Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Li Du, Afra Amini, Lucas Torroba Hennigen +4
Recent papers have demonstrated the possibility of energy-based text generation by adapting gradient-based sampling algorithms, a paradigm of MCMC algorithms that promises fast con…
Assessing Large Language Models on Climate Information
Jannis Bulian, Mike S. Schäfer, Afra Amini +8
As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grou…
Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals
Robin Chan, Afra Amini, Mennatallah El-Assady
We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions. The dashboard enables users to generate diverse…
Hexatagging: Projective Dependency Parsing as Tagging
Afra Amini, Tianyu Liu, Ryan Cotterell
We introduce a novel dependency parser, the hexatagger, that constructs dependency trees by tagging the words in a sentence with elements from a finite set of possible tags. In con…
Structured Voronoi Sampling
Afra Amini, Li Du, Ryan Cotterell
Gradient-based sampling algorithms have demonstrated their effectiveness in text generation, especially in the context of controlled text generation. However, there exists a lack o…
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective
Tianyu Liu, Afra Amini, Mrinmaya Sachan +1
Tasks that model the relation between pairs of tokens in a string are a vital part of understanding natural language. Such tasks, in general, require exhaustive pair-wise compariso…
In-Context Probing: Toward Building Robust Classifiers via Probing Large Language Models
Afra Amini, Massimiliano Ciaramita
Large language models are able to learn new tasks in context, where they are provided with instructions and a few annotated examples. However, the effectiveness of in-context learn…
On Parsing as Tagging
Afra Amini, Ryan Cotterell
There have been many proposals to reduce constituency parsing to tagging in the literature. To better understand what these approaches have in common, we cast several existing prop…
Naturalistic Causal Probing for Morpho-Syntax
Afra Amini, Tiago Pimentel, Clara Meister +1
Probing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limi…
Conditional Poisson Stochastic Beam Search
Clara Meister, Afra Amini, Tim Vieira +1
Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the dis…