Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents
- Paper: https://arxiv.org/abs/2503.04830
- Authors: Jingying Zeng, Hui Liu, Zhenwei Dai, Xianfeng Tang, Chen Luo, Samarth Varshney, et al. (8 authors)
- Date: March 2025
- Venue: arXiv preprint
- Conversational agents in commerce answer questions about products, availability, compatibility, and policies; unsupported claims translate directly into customer complaints and lost trust.
- Hallucination risk is amplified by long product catalogs, sparse user signals, and mixed sources of truth.
- Citation of evidence gives both users and downstream systems a mechanism to audit and verify responses.
- Classic search stacks (recall → ranking → generation) are evolving into agentic pipelines where the number and strategy of retrieval steps are themselves learned decisions.
- Evaluation is shifting from static metrics (nDCG, MRR, Recall@k) toward process-level measures such as citation accuracy, task success rate, and multi-hop reasoning chain integrity.
- Industrial deployment adds hard constraints on latency (p99 budgets), cost per query, safety filtering of retrieved content, and explainability that pure academic benchmarks often ignore.
- Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization
- Automated Query-Product Relevance Labeling using Large Language Models
- An Interpretable Ensemble of Graph and Language Models for Search
Overview
This paper targets a core reliability problem in e-commerce conversational LLM agents: responses generated during shopping-assistant interactions must be grounded in the provided context (retrieved product information, session history, knowledge sources) rather than invented by the model. The central idea conveyed by the title is that the agent should identify and cite the supporting contextual evidence before producing its answer, making each response traceable to verifiable sources.
Why Grounding Matters in E-commerce
Positioning in the Broader Landscape
The work sits at the intersection of agentic search, retrieval-augmented generation (RAG), and industrial search/recommendation systems. Relevant context from the surrounding literature discussed on this forum:
Practical Checklist for Practitioners
| Concern | Recommendation | |---------|----------------| | Latency | Cascade retrieval with early stopping; cache frequent queries | | Quality | Cross-validate offline gains with interleaved online experiments and human audits | | Safety | Whitelist retrieval sources; filter outputs against poisoning and bias | | Cost | Route easy queries to smaller models; mix sparse and dense retrieval |
Key Takeaway
For e-commerce conversational agents, making citation of contextual evidence an explicit, first-class part of the response generation process — rather than an after-the-fact verification step — is a practical route to more trustworthy, auditable LLM assistants. Readers citing quantitative results should consult the original PDF at arXiv:2503.04830.