Winning Amazon KDD Cup'24
This paper describes the winning solution of all 5 tasks for the Amazon KDD Cup 2024 Multi Task Online Shopping Challenge for LLMs. The challenge was to build a useful assistant, answering questions in the domain of online shopping. The competition contained 57 diverse tasks, covering 5 different task types (e.g. multiple choice) and across 4 different tracks (e.g. multi-lingual). Our solution is a single model per track. We fine-tune Qwen2-72B-Instruct on our own training dataset. As the competition released only 96 example questions, we developed our own training dataset by processing multiple public datasets or using Large Language Models for data augmentation and synthetic data generation. We apply wise-ft to account for distribution shifts and ensemble multiple LoRA adapters in one model. We employed Logits Processors to constrain the model output on relevant tokens for the tasks. AWQ 4-bit Quantization and vLLM are used during inference to predict the test dataset in the time constraints of 20 to 140 minutes depending on the track. Our solution achieved the first place in each individual track and is the first place overall of Amazons KDD Cup 2024.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationMultiple-choiceQuantizationSynthetic Data GenerationSimilar Papers 제목 키워드 기반
The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Existing auto-bidding algorithms in digital advertising often treat the value of an ad opportunity as the revenue obtained when an ad is shown and/or clicked, and bid accordingly. This can lead to wasteful spending becau…
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized r…
Product RecommendationSession-Based RecommendationsText GenerationShopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, fallin…
Few-Shot LearningMMLUSemantic Segmentation from Limited Training Data
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of …
ARCBoundary DetectionMetric LearningSegmentation+1MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophisticatio…
BIG-bench Machine LearningBinary ClassificationGeneral Classification