Search Results for author:
Found 15 papers, 10 papers with code
Enhancing Relevance of Embedding-based Retrieval at Walmart
In addition, we present the techniques to increase the performance of our EBR model, such as typo-aware training, and semi-positive generation.
Relevance Filtering for Embedding-based Retrieval
This issue is prominent in product search, where the number of relevant products is often small.
Doc2Token: Bridging Vocabulary Gap by Predicting Missing Tokens for E-commerce Search
Doc2Token is demonstrated to be superior to Doc2Query in terms of novel ROUGE score and diversity of predictions.
Large Language Models for Relevance Judgment in Product Search
High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search, yet measuring relevance of items to queries is one of the most challenging tasks in product information retrieval, and quality of product search is highly influenced by the precision and scale of available relevance-labelled data.
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks.
Holistic Evaluation of Text-To-Image Models
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #45 on
Code Generation
on MBPP
Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs
Large language models (LLMs) power many state-of-the-art systems in natural language processing.
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Do Question Answering Modeling Improvements Hold Across Benchmarks?
Do question answering (QA) modeling improvements (e. g., choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks?
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning Environments
In the post-industrial world, data science and analytics have gained paramount importance regarding digital data privacy.
