Understanding the User: An Intent-Based Ranking Dataset (2408.17103v1)

Abstract: As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide short keyword queries without accompanying intent or descriptions, posing a challenge in comprehending the underlying information need. This paper proposes an approach to augmenting such datasets to annotate informative query descriptions, with a focus on two prominent benchmark datasets: TREC-DL-21 and TREC-DL-22. Our methodology involves utilizing state-of-the-art LLMs to analyze and comprehend the implicit intent within individual queries from benchmark datasets. By extracting key semantic elements, we construct detailed and contextually rich descriptions for these queries. To validate the generated query descriptions, we employ crowdsourcing as a reliable means of obtaining diverse human perspectives on the accuracy and informativeness of the descriptions. This information can be used as an evaluation set for tasks such as ranking, query rewriting, or others.

Understanding the User: An Intent-Based Ranking Dataset

The paper "Understanding the User: An Intent-Based Ranking Dataset" addresses a significant gap in the field of Information Retrieval (IR) by introducing the DL-MIA dataset, which specifically aims to align user intent with the relevance annotations in ranking models. This work provides a novel contribution by not only generating detailed user intents for ambiguous queries but also ensuring comprehensive human validation through crowdsourcing.

Background

Information retrieval systems, such as web search engines, often struggle to interpret the true intent behind user queries. Traditional datasets, like MS MARCO, primarily offer keyword-based queries without additional context on user intent, making it challenging to correctly infer and rank the relevant documents. The paper seeks to bridge this gap by creating a dataset that maps queries to fine-grained user intents, thereby enabling a deeper understanding and evaluation of IR systems.

Methodology

The research outlines a multi-step process to construct the DL-MIA dataset:

1. Data Source: Utilizing queries from the TREC-DL test sets of 2021 and 2022, the authors start with a set of 118 queries.
2. Intent Generation: State-of-the-art LLMs, specifically GPT-4, are leveraged to generate candidate user intents for each query by analyzing the relevance passages retrieved using QRel files.
3. Clustering for Redundancy Elimination: Sentence-BERT embeddings and cosine similarity are employed to cluster similar passages and intents, effectively reducing redundancy.
4. Crowdsourcing for Validation: Human annotators validate and refine the LLM-generated intents, ensuring the resulting dataset reflects realistic and diverse human perspectives.
5. Merging and Cleanup: Post-annotation, the intents are manually reviewed, merged if redundant, and refined to eliminate irrelevant or overly specific intents.

Results and Analysis

The DL-MIA dataset includes 24 queries with 69 distinct intents, resulting in 2655 relevance annotations. The authors evaluate their methodology by introducing a series of baseline models, including BM25, BERT, and ColBERTv2, demonstrating the practical impact of specifying user intents on ranking performance.

The experimental results reveal that directly using user intents as queries significantly enhances the ranking performance and diversity of search results:

• Intent Ranking: The nDCG@10 scores for BM25, BERT, and ColBERTv2 when using user intents as queries are 0.116, 0.169, and 0.261, respectively, showing a substantial improvement over original queries.
• Diversity: Employing $\alpha$-nDCG@10, the diversified relevance of search results also saw marked improvement with user intents as queries, with scores of 0.532 for ColBERTv2, reflecting better coverage of the various aspects of user queries.

Implications

The introduction of the DL-MIA dataset has several crucial implications:

1. Enhanced Model Evaluation: DL-MIA allows for the precise evaluation of IR models on how well they infer and rank documents based on user-specific intents rather than generalized queries.
2. Training and Fine-tuning Generative Models: Models can be trained to generate more specific and user-aligned intents for ambiguous queries, bridging the gap between user intent and machine comprehension.
3. User Intent Understanding: The dataset aids in improving the alignment of retrieval systems with actual user needs, potentially leading to significant advances in the development of IR technologies.

Future Work

Potential future directions stemming from this research include extending the DL-MIA dataset to include queries from additional TREC-DL test sets and exploring more sophisticated techniques for intent generation and clustering. Further research could also investigate user behavior and feedback to refine the intent formulation process, ensuring that generated intents continue to evolve with changing user information needs.

In summary, the DL-MIA dataset introduced in this paper offers a valuable resource for the IR community, providing fine-grained intent annotations that facilitate improved understanding and evaluation of user intents within ranking models. This dataset stands to significantly contribute to advancements in creating more intuitive and effective information retrieval systems.