Publications

Raidar: geneRative AI Detection viA Rewriting
Chengzhi Mao
Carl Vondrick
Hao Wang
Junfeng Yang
We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. Th… (voir plus)is tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Reasoning with Latent Diffusion in Offline Reinforcement Learning
Siddarth Venkatraman
Shivesh Khaitan
Ravi Tej Akella
John Dolan
Jeff Schneider
Glen Berseth
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Reasoning with Latent Diffusion in Offline Reinforcement Learning
Siddarth Venkatraman
Shivesh Khaitan
Ravi Tej Akella
John Dolan
Jeff Schneider
Glen Berseth
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Reward Model Ensembles Help Mitigate Overoptimization
Thomas Coste
Usman Anwar
Robert Kirk
David Scott Krueger
Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As pa… (voir plus)rt of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the “true” reward, these learned reward models are susceptible to overoptimization. Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger “gold” reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used. Using a similar setup, we conduct a systematic study to evaluate the efficacy of using ensemble-based conservative optimization objectives, specifically worst-case optimization (WCO) and uncertainty-weighted optimization (UWO), for mitigating reward model overoptimization when using two optimization methods: (a) best-of-n sampling (BoN) (b) proximal policy optimization (PPO). We additionally extend the setup of Gao et al. (2023) to include 25% label noise to better mirror real-world conditions. Both with and without label noise we find that conservative optimization practically eliminates overoptimization and improves performance by up to 70% for BoN sampling. For PPO, ensemble-based conservative optimization always reduces overoptimization and outperforms single reward model optimization. Moreover, combining it with a small KL penalty successfully prevents overoptimization at no performance cost. Overall, our results demonstrate that ensemble-based conservative optimization can effectively counter overoptimization.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare
Santiago Miret
Adriana Hugessen
Mariano Phielipp
Glen Berseth
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
Jiarui Lu
Bozitao Zhong
Zuobai Zhang
Jian Tang
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Sufficient conditions for offline reactivation in recurrent neural networks
Nanda H Krishna
Colin Bredenberg
Daniel Levenstein
Blake Richards
Guillaume Lajoie
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (voir plus)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Roman Pogodin
Jonathan Cornford
Arna Ghosh
Gauthier Gidel
Guillaume Lajoie
Blake Richards
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic … (voir plus)plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes that the distance is Euclidean, but many other distances are possible, and there is no reason that biology necessarily uses Euclidean geometry. Here, using the theoretical tools provided by mirror descent, we show that the distribution of synaptic weights will depend on the geometry of synaptic plasticity. We use these results to show that experimentally-observed log-normal weight distributions found in several brain areas are not consistent with standard gradient descent (i.e. a Euclidean geometry), but rather with non-Euclidean distances. Finally, we show that it should be possible to experimentally test for different synaptic geometries by comparing synaptic weight distributions before and after learning. Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.
2024-01-16
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (voir plus)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Tian Jin
Nolan Clement
Xin Dong
Vaishnavh Nagarajan
Michael Carbin
Jonathan Ragan-Kelley
Gintare Karolina Dziugaite
We study how down-scaling large language model (LLM) size impacts LLM capabilities. We begin by measuring the effects of weight pruning – … (voir plus)a popular technique for reducing model size – on the two abilities of LLMs: (a) recalling facts presented during pre-training and (b) processing information presented in context. Surprisingly, we find that existing pruning techniques affect these two abilities of LLMs differently. For example, pruning more than 30% of weights significantly decreases an LLM’s ability to recall facts presented during pre-training. Yet pruning 60-70% of weights largely preserves an LLM’s ability to process information in-context, ranging from retrieving answers based on information presented in context to learning parameterized functions such as a linear classifier based on a few examples. Moderate pruning impairs LLM’s ability to recall facts learnt from pre-training. However, its effect on model’s ability to process information presented in context is much less pronounced. The said disparate effects similarly arise when replacing the original model with a smaller dense one with reduced width and depth. This similarity suggests that model size reduction in general underpins the said disparity.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
The Curse of Diversity in Ensemble-Based Exploration
Zhixuan Lin
Pierluca D'Oro
Evgenii Nikishin
Aaron Courville
We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established … (voir plus)exploration strategy -- can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon *the curse of diversity*. We find that several intuitive solutions -- such as a larger replay buffer or a smaller ensemble size -- either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
On the Stability of Iterative Retraining of Generative Models on their own Data
Quentin Bertrand
Joey Bose
Alexandre Duplessis
Marco Jiralerspong
Gauthier Gidel
Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical … (voir plus)human's ability to discern the authenticity of samples. Undeniably, a key driver of this success is enabled by the massive amounts of web-scale data consumed by these models. Due to these models' striking performance and ease of availability, the web will inevitably be increasingly populated with synthetic content. Such a fact directly implies that future iterations of generative models will be trained on both clean and artificially generated data from past models. In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets---from classical training on real data to self-consuming generative models trained on purely synthetic data. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough and the proportion of clean training data (w.r.t. synthetic data) is large enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models on CIFAR10 and FFHQ.
2024-01-16
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net