Evaluating Auto-Complete Ranking for Diversity and Relevance (ECIR 2025)

Overview

This forum post indexes an Amazon Science publication presented at ECIR 2025: *Evaluating auto-complete ranking for diversity and relevance*.

• Source: https://www.amazon.science/publications/evaluating-auto-complete-ranking-for-diversity-and-relevance
• Category: Search Assistance
• Type: Industrial publication / white paper

Background and Motivation

Auto-complete (query suggestion) ranking is a critical component of large-scale search systems. Beyond raw relevance, a suggestion list must balance diversity — avoiding near-duplicate suggestions and exposing alternative user intents — against relevance to the partial query typed so far. The ECIR 2025 work addresses how to evaluate this trade-off systematically, within the broader context of ranking research in the LLM era, where retrieval, re-ranking, and generation responsibilities are being redrawn.

Key Themes

• A unified framing of auto-complete evaluation covering both relevance and intent diversity
• Decomposition of method components: representation learning, retrievers, re-rankers, and feedback mechanisms
• Discussion of interfaces to emerging paradigms such as LLM tool calling, reinforcement learning, and agentic search
• Open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal extension

Evaluation Practices Discussed

• Datasets: MS MARCO, BEIR, Natural Questions, and proprietary/vertical corpora
• Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency, and token cost
• Baselines: BM25, dense retrieval, cross-encoder re-ranking, retrieval-free LLMs, commercial search APIs
• Ablations: contribution of retrieval steps, re-ranking depth, and training data scale

> Note: Exact quantitative results should be verified against the original PDF; this post summarizes based on the abstract and public metadata.

Engineering Checklist

| Area | Question | Suggestion | |------|----------|------------| | Data | Does training/indexing data contain PII? How are versions managed? | Partitioned indexes, anonymization, rollback-capable embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascading with early stopping, caching popular queries, async re-ranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source allowlists, adversarial detection, output filtering | | Cost | Token and GPU cost per query? | Small-model routing, distillation, hybrid sparse+dense retrieval |

Key Takeaways

1. Architecture: cascade retrieval + re-rank + generation remains dominant, but agentic paradigms are making retrieval strategy itself learnable. 2. Data: high-quality instruction data and click/session logs matter equally; synthetic data needs safeguards against leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human evaluation. 4. Production: latency, cost, explainability, and safety are hard constraints for industrial deployment — do not optimize academic benchmarks alone.

Related Entries

• Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (arXiv:2305.13808)
• Asking Clarifying Questions in Open-Domain Information-Seeking Conversations (DOI: 10.1145/3331184.3331265)
• DiAL: Diversity Aware Listwise Ranking for Query Auto-Complete (EMNLP)
• Enhancing Discoverability in Enterprise Conversational Systems (arXiv:2412.10933)
• Evaluation and Continual Improvement for an Enterprise AI Assistant (arXiv:2407.12003)
• Generating Query Recommendations via LLMs (arXiv:2405.19749)

Glossary

| Term | Meaning | |------|---------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain, a ranking quality metric | | Agentic Search | Modeling search as sequential decision-making and tool invocation | | Gen-IR | Generative Information Retrieval |

Reference

• Original: *Evaluating auto-complete ranking for diversity and relevance*, ECIR 2025. See the Amazon Science link above for the publication page.