mlsanityData to 14 Oct 2026

EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization

arXiv 2408.02970 ↗First posted 6 Aug 2024cs.CLcited by 1

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Abstract (arXiv)

Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instruction tuning and quantization. E-commerce, with its complex tasks and extensive product-user interactions, presents a promising application area for LLMs. However, the domain-specific concepts and knowledge inherent in e-commerce pose significant challenges for adapting general LLMs. To address this issue, we developed EC-Guide hrefhttps://github.com/fzp0424/EC-Guide-KDDUP-2024, a comprehensive e-commerce guide for instruction tuning and quantization of LLMs. We also heuristically integrated Chain-of-Thought (CoT) during inference to enhance arithmetic performance. Our approach achieved the 2nd place in Track 2 and 5th place in Track 5 at the Amazon KDD Cup'24 hrefhttps://www.aicrowd.com/challenges/amazon-kdd-cup-2024-multi-task-online-shopping-challenge-for-llms. Additionally, our solution is model-agnostic, enabling effective scalability across larger systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 17 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

15 of 17 references.

References · 15

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  5. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  6. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  7. 2024Yi: Open Foundation Models by 01.AIcited by 719
  8. 2015Image-based Recommendations on Styles and Substitutescited by 706
  9. 2024Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoderscited by 224
  10. 2022Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Searchcited by 65
  11. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 46
  12. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
3 more
  1. 2015Addressing Complex and Subjective Product-Related Queries with Customer Reviewscited by 29
  2. 2024IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commercecited by 11
  3. 2023Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commercecited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2025Performance Trade-offs of Optimizing Small Language Models for E-Commerce
  2. 2024Dual Instruction Tuning with Large Language Models for Mathematical Reasoning
  3. 2023FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
  4. 2024MAmmoTH2: Scaling Instructions from the Web
  5. 2024Exploring Advanced Large Language Models with LLMsuite
  6. 2026Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain
  7. 2025Slimming Down LLMs Without Losing Their Minds
  8. 2024Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance