Search Results for author: Ed Chi

Found 24 papers, 7 papers with code

Can We Improve Model Robustness through Secondary Attribute Counterfactuals?

no code implementations EMNLP 2021 Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed Chi, Alex Beutel

By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods.

coreference-resolution Data Augmentation +1

Better Generalization with Semantic IDs: A case study in Ranking for Recommendations

no code implementations13 Jun 2023 Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, Maheswaran Sathiamoorthy

We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform.

Recommendation Systems

Value of Exploration: Measurements, Findings and Algorithms

no code implementations12 May 2023 Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen

We conduct extensive live experiments on a large-scale commercial recommendation platform that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.

Recommendation Systems

Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

no code implementations10 May 2023 Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, Derek Zhiyuan Cheng

In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.

Collaborative Filtering

Large Language Models Can Be Easily Distracted by Irrelevant Context

1 code implementation31 Jan 2023 Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou

We use this benchmark to measure the distractibility of cutting-edge prompting techniques for large language models, and find that the model performance is dramatically decreased when irrelevant information is included.

Arithmetic Reasoning Language Modelling

Rationale-Augmented Ensembles in Language Models

no code implementations2 Jul 2022 Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou

Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks.

Prompt Engineering Question Answering +2

Self-Consistency Improves Chain of Thought Reasoning in Language Models

no code implementations21 Mar 2022 Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou

Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks.

Ranked #21 on Arithmetic Reasoning on GSM8K (using extra training data)

Arithmetic Reasoning GSM8K +2

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

10 code implementations28 Jan 2022 Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou

We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning.

GSM8K Language Modelling

Recency Dropout for Recurrent Recommender Systems

no code implementations26 Jan 2022 Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen

Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.

Data Augmentation Recommendation Systems

Batch Reinforcement Learning Through Continuation Method

no code implementations ICLR 2021 Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen

Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.

reinforcement-learning Reinforcement Learning (RL)

What are effective labels for augmented data? Improving robustness with AutoLabel

no code implementations1 Jan 2021 Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed Chi, Alex Beutel

Despite this, most existing work simply reuses the original label from the clean data, and the choice of label accompanying the augmented data is relatively less explored.

Adversarial Robustness Data Augmentation

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

no code implementations EMNLP 2020 Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi

Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.

Adversarial Text Sentiment Analysis +2

BRPO: Batch Residual Policy Optimization

no code implementations8 Feb 2020 Sungryull Sohn, Yin-Lam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, Craig Boutilier

In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e. g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state.

reinforcement-learning Reinforcement Learning (RL)

Recommending what video to watch next: a multitask ranking system

no code implementations RecSys 2019 Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.

Top-K Off-Policy Correction for a REINFORCE Recommender System

1 code implementation6 Dec 2018 Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi

The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.

Recommendation Systems

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

9 code implementations19 Jul 2018 Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed Chi

In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.

Binary Classification Click-Through Rate Prediction +2

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