Search Results for author:
Found 13 papers, 2 papers with code
AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications.
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial.
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data
However, the large size and high computation demands of LLMs limit their practicality in many applications, especially when further fine-tuning is required.
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge.
Exploring Query Understanding for Amazon Product Search
Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model.
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems
For example, a conversational agent on a retail site answering customer questions about past orders will need to retrieve the appropriate customer order first and then the evidence relevant to the customer's question in the context of the ordered product.
Large Language Models in the Clinic: A Comprehensive Benchmark
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention.
IterAlign: Iterative Constitutional Alignment of Large Language Models
Such a constitution discovery pipeline can be run iteratively and automatically to discover new constitutions that specifically target the alignment gaps in the current LLM.
Hierarchical Query Classification in E-commerce Search
E-commerce platforms typically store and structure product information and search data in a hierarchy.
Situated Natural Language Explanations
Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation
We explore ways of incorporating bilingual dictionaries to enable semi-supervised neural machine translation.
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks.
A Big Data Analysis Framework Using Apache Spark and Deep Learning
In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), using the popular concept of Cascade Learning.
