ComLQ: Benchmarking Complex Logical Queries in Information Retrieval
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917099707826176 |
|---|---|
| author | Xu, Ganlin Yin, Zhitao Zhang, Linghao Liang, Jiaqing Lu, Weijia Zhang, Xiaodong Yang, Zhifei Jiang, Sihang Yang, Deqing |
| author_facet | Xu, Ganlin Yin, Zhitao Zhang, Linghao Liang, Jiaqing Lu, Weijia Zhang, Xiaodong Yang, Zhifei Jiang, Sihang Yang, Deqing |
| contents | Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries that are semantically analogous to single- and multi-hop relations, overlooking \emph{complex logical queries} involving first-order logic operations such as conjunction ($\land$), disjunction ($\lor$), and negation ($\lnot$). Thus, these benchmarks can not be used to sufficiently evaluate the performance of IR models on complex queries in real-world scenarios. To address this problem, we propose a novel method leveraging large language models (LLMs) to construct a new IR dataset \textbf{ComLQ} for \textbf{Com}plex \textbf{L}ogical \textbf{Q}ueries, which comprises 2,909 queries and 11,251 candidate passages. A key challenge in constructing the dataset lies in capturing the underlying logical structures within unstructured text. Therefore, by designing the subgraph-guided prompt with the subgraph indicator, an LLM (such as GPT-4o) is guided to generate queries with specific logical structures based on selected passages. All query-passage pairs in ComLQ are ensured \emph{structure conformity} and \emph{evidence distribution} through expert annotation. To better evaluate whether retrievers can handle queries with negation, we further propose a new evaluation metric, \textbf{Log-Scaled Negation Consistency} (\textbf{LSNC@$K$}). As a supplement to standard relevance-based metrics (such as nDCG and mAP), LSNC@$K$ measures whether top-$K$ retrieved passages violate negation conditions in queries. Our experimental results under zero-shot settings demonstrate existing retrieval models' limited performance on complex logical queries, especially on queries with negation, exposing their inferior capabilities of modeling exclusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ComLQ: Benchmarking Complex Logical Queries in Information Retrieval Xu, Ganlin Yin, Zhitao Zhang, Linghao Liang, Jiaqing Lu, Weijia Zhang, Xiaodong Yang, Zhifei Jiang, Sihang Yang, Deqing Information Retrieval Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries that are semantically analogous to single- and multi-hop relations, overlooking \emph{complex logical queries} involving first-order logic operations such as conjunction ($\land$), disjunction ($\lor$), and negation ($\lnot$). Thus, these benchmarks can not be used to sufficiently evaluate the performance of IR models on complex queries in real-world scenarios. To address this problem, we propose a novel method leveraging large language models (LLMs) to construct a new IR dataset \textbf{ComLQ} for \textbf{Com}plex \textbf{L}ogical \textbf{Q}ueries, which comprises 2,909 queries and 11,251 candidate passages. A key challenge in constructing the dataset lies in capturing the underlying logical structures within unstructured text. Therefore, by designing the subgraph-guided prompt with the subgraph indicator, an LLM (such as GPT-4o) is guided to generate queries with specific logical structures based on selected passages. All query-passage pairs in ComLQ are ensured \emph{structure conformity} and \emph{evidence distribution} through expert annotation. To better evaluate whether retrievers can handle queries with negation, we further propose a new evaluation metric, \textbf{Log-Scaled Negation Consistency} (\textbf{LSNC@$K$}). As a supplement to standard relevance-based metrics (such as nDCG and mAP), LSNC@$K$ measures whether top-$K$ retrieved passages violate negation conditions in queries. Our experimental results under zero-shot settings demonstrate existing retrieval models' limited performance on complex logical queries, especially on queries with negation, exposing their inferior capabilities of modeling exclusion. |
| title | ComLQ: Benchmarking Complex Logical Queries in Information Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2511.12004 |