1k citations · 2.2k across the 22 of their papers we have counts for
25 papers
Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz +31
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Maribeth Rauh, John Mellor, Jonathan Uesato +9
Large language models produce human-like text that drive a growing number of applications. However, recent literature and, increasingly, real world observations, have demonstrated…
Competition-Level Code Generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung +23
Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more pr…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
Ethical and social risks of harm from Language Models
Laura Weidinger, John Mellor, Maribeth Rauh +20
This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understan…
Challenges in Detoxifying Language Models
Johannes Welbl, Amelia Glaese, Jonathan Uesato +7
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of saf…
Towards Robust Image Classification Using Sequential Attention Models
Daniel Zoran, Mike Chrzanowski, Po-Sen Huang +3
In this paper we propose to augment a modern neural-network architecture with an attention model inspired by human perception. Specifically, we adversarially train and analyze a ne…
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Sven Gowal, Chongli Qin, Po-Sen Huang +4
Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has b…
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang +6
Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of ge…
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang +3
This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a…
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Sven Gowal, Jonathan Uesato, Chongli Qin +3
Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be miscla…
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl +5
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversari…
Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang +3
Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets…
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham +3
Models such as Sequence-to-Sequence and Image-to-Sequence are widely used in real world applications. While the ability of these neural architectures to produce variable-length out…
Neural Phrase-to-Phrase Machine Translation
Jiangtao Feng, Lingpeng Kong, Po-Sen Huang +5
In this paper, we propose Neural Phrase-to-Phrase Machine Translation (NPMT). Our model uses a phrase attention mechanism to discover relevant input (source) segments that are…
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt +4
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to le…
Discourse-Aware Neural Rewards for Coherent Text Generation
Antoine Bosselut, Asli Celikyilmaz, Xiaodong He +3
In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn ne…
Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh +2
In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could…
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Yelong Shen, Jianshu Chen, Po-Sen Huang +2
Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be f…
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
David Golub, Po-Sen Huang, Xiaodong He +1
We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet). Given a high-performing MC model in one domain, our t…
Towards Neural Phrase-based Machine Translation
Po-Sen Huang, Chong Wang, Sitao Huang +2
In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a…
Sequence Modeling via Segmentations
Chong Wang, Yining Wang, Po-Sen Huang +3
Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segme…
Link Prediction using Embedded Knowledge Graphs
Yelong Shen, Po-Sen Huang, Ming-Wei Chang +1
Since large knowledge bases are typically incomplete, missing facts need to be inferred from observed facts in a task called knowledge base completion. The most successful approach…
ReasoNet: Learning to Stop Reading in Machine Comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao +1
Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architect…