Search Results for author:
Found 24 papers, 7 papers with code
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
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.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
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.
Value of Exploration: Measurements, Findings and Algorithms
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.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
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.
Large Language Models Can Be Easily Distracted by Irrelevant Context
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.
Rationale-Augmented Ensembles in Language Models
Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks.
Ranked #28 on
Arithmetic Reasoning
on GSM8K
Self-Consistency Improves Chain of Thought Reasoning in Language Models
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)
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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.
Recency Dropout for Recurrent Recommender Systems
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #36 on
Code Generation
on HumanEval
Batch Reinforcement Learning Through Continuation Method
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.
What are effective labels for augmented data? Improving robustness with AutoLabel
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.
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
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.
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments.
BRPO: Batch Residual Policy Optimization
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.
Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.
Recommending what video to watch next: a multitask ranking system
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
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.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
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.
Efficient Training on Very Large Corpora via Gramian Estimation
We study the problem of learning similarity functions over very large corpora using neural network embedding models.
