mlsanityData to 7 Oct 2026

Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

arXiv 2401.04319 ↗First posted 9 Jan 2024cs.CL cs.AIcited by 2

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. In practical scenarios, the demands of non-expert marketers are often abstract and diverse. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation. Part of our data and code can be found at https://github.com/alipay/Analogic-Reasoning-Augmented-Large-Language-Model.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 9 of its 33 references that are in the map. First posted on arXiv 9 Jan 2024.

Builds on

9 of 33 references.

References · 9

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  3. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  4. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  5. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 556
  6. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 106
  7. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  8. 2023Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translationcited by 59
  9. 2023Who Would be Interested in Services? An Entity Graph Learning System for User Targetingcited by 3

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Transferable and Forecastable User Targeting Foundation Modelcited by 4
  2. 2025Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUscited by 0

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  3. 2024Injecting Salesperson's Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning
  4. 2026EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce
  5. 2023Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
  6. 2026Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition
  7. 2026Sell More, Play Less: Benchmarking LLM Realistic Selling Skill
  8. 2023Active Preference Inference using Language Models and Probabilistic Reasoning