Publications

Improving Language Plasticity via Pretraining with Active Forgetting
Yihong Chen
Kelly Marchisio
Roberta Raileanu
David Ifeoluwa Adelani
Pontus Stenetorp
Sebastian Riedel
Mikel Artetxe
Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performan… (see more)ce, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation, but also outperform standard ones in a low-data regime, particularly for languages that are distant from English. Code will be available at https://github.com/facebookresearch/language-model-plasticity.
openreview.net
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu
Mizu Nishikawa-Toomey
Jithendaraa Subramanian
Nikolay Malkin
Laurent Charlin
Yoshua Bengio
Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied … (see more)to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.
openreview.net
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Alessandro Sordoni
Eric Yuan
Xingdi Yuan
Marc-Alexandre Côté
Matheus Pereira
Adam Trischler
Ziang Xiao
Arian Hosseini
Friederike Niedtner
Nicolas Le Roux
Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can b… (see more)e seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer. By stacking two such layers and feeding the output of one layer to the next, we obtain a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). Then, we present an extension that applies to 2-layer DLNs (DLN-2), where two prompts must be learned. The key idea is to consider the output of the first layer as a latent variable, which requires inference, and prompts to be learned as the parameters of the generative distribution. We first test the effectiveness of DLN-1 in multiple reasoning and natural language understanding tasks. Then, we show that DLN-2 can reach higher performance than a single layer, showing promise that we might reach comparable performance to GPT-4, even when each LLM in the network is smaller and less powerful.
openreview.net
Language Model Alignment with Elastic Reset
Michael Noukhovitch
Samuel Lavoie
Florian Strub
Aaron Courville
Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimiz… (see more)ing against a reward model can improve on reward while degrading performance in other areas, a phenomenon known as reward hacking, alignment tax, or language drift. First, we argue that commonly-used test metrics are insufficient and instead measure how different algorithms tradeoff between reward and drift. The standard method modified the reward with a Kullback-Lieber (KL) penalty between the online and initial model. We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective. We periodically reset the online model to an exponentially moving average (EMA) of itself, then reset the EMA model to the initial model. Through the use of an EMA, our model recovers quickly after resets and achieves higher reward with less drift in the same number of steps. We demonstrate that fine-tuning language models with Elastic Reset leads to state-of-the-art performance on a small scale pivot-translation benchmark, outperforms all baselines in a medium-scale RLHF-like IMDB mock sentiment task and leads to a more performant and more aligned technical QA chatbot with LLaMA-7B. Code available at github.com/mnoukhov/elastic-reset.
openreview.net
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions
Stefano Massaroli
Michael Poli
Daniel Y Fu
Hermann Kumbong
Rom Nishijima Parnichkun
Aman Timalsina
David W. Romero
Quinn McIntyre
Beidi Chen
Atri Rudra
Ce Zhang
Christopher Re
Stefano Ermon
Yoshua Bengio
Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers… (see more). In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or caching of activations) over the input sequence for each generated token -- similarly to attention-based models. In this paper, we seek to enable
openreview.net
Learning better with Dale’s Law: A Spectral Perspective
Pingsheng Li
Jonathan Cornford
Arna Ghosh
Blake Richards
openreview.net
Learning Reliable Logical Rules with SATNet
Zhaoyu Li
Jinpei Guo
Yuhe Jiang
Xujie Si
openreview.net
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
openreview.net
Lie Point Symmetry and Physics-Informed Networks
Tara Akhound-Sadegh
Laurence Perreault-Levasseur
Johannes Brandstetter
Max Welling
Siamak Ravanbakhsh
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equiv… (see more)ariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.
openreview.net
Maximum State Entropy Exploration using Predecessor and Successor Representations
Arnav Kumar Jain
Lucas Lehnert
Irina Rish
Glen Berseth
Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misp… (see more)laced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms often learn a less efficient exploration strategy because they either condition only on the current state or simply rely on making random open-loop exploratory moves. In this work, we propose
openreview.net
Multi-Head Adapter Routing for Cross-Task Generalization
Lucas Caccia
Edoardo Ponti
Zhan Su
Matheus Pereira
Nicolas Le Roux
Alessandro Sordoni
Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists in pre-training adapters on a multi-task training set before f… (see more)ew-shot adaptation to test tasks. Polytropon [Ponti et al., 2023] (
openreview.net
Neural Graph Generation from Graph Statistics.
Kiarash Zahirnia
Yaochen Hu
Mark Coates
Oliver Schulte
openreview.net