ChronoScope: Enhancing Information Retrieval with Temporal Context Using Zero-Shot Learning and Large Language Models
Authors: Aadyant Khatri, Chang-Tien Lu


Reviewer paper summaries: This paper introduces ChronoScope, a system designed to enhance article retrieval by incorporating temporal context into the search process. ChronoScope uses zero-shot learning and Large Language Models to estimate the temporal scope of both user queries and document contents. The system operates in two main phases: temporal prediction for user queries and temporal alignment for documents. The authors claim that ChronoScope can achieve comparable results to existing information retrieval systems by solely using temporal context.
Summary of strengths: - Overall the motivation of this work is clearly stated and the time-sensitive information retrieval is an interesting IR direction.
- The use of zero-shot LLMs for temporal prediction could make the system more adaptable to new queries without requiring extensive retraining.
Summary of weaknesses: - Poor presentation quality: The paper suffers from low-resolution figures and inadequate captions, making it difficult for readers to understand the visual content.
- Lack of formula explanations: The paper fails to provide clear explanations for the all parameters used in formulas.
- Suboptimal performance: Experimental results show that the proposed method fails to outperform existing baselines in all Top-K accuracy metrics, raising concerns about its effectiveness.
- Insufficient evaluation of key components: The paper lacks a thorough evaluation of the SUTime tool's accuracy in extracting time information, which is crucial to the system's performance. Also, there is inadequate discussion on the specific benefits of introducing LLMs into the pipeline and how they improve overall performance.
- Concerns on generalizability: The paper lacks discussions on how well the approach might perform on diverse types of queries and documents beyond the tested dataset.
Comments, suggestions and typos: Please refer to the comments and questions raised in Weakness section.
Overall assessment scores: 2
Metareview score: 2
Metareview: The paper proposes to predict a temporal span of relevance based on the query and uses it to compute a relevance score. The temporal span is estimated using an LLM (Gemini). It achieves comparable effectiveness to strong baselines such as BM25. 

The result is interesting, but reviewers raise multiple concerns with novelty, presentation quality, as well as with the importance of the result (since authors do not outperform BM25 by a significant margin). I therefore recommend a thorough revision that includes a broader comparison with prior art and more rigorous experimentation (e.g., use more standard IR metrics such as NDCG, MRR). 

I also recommend combining temporal scores with regular retrieval scores such as BM25 or scores produced by neural rankers/retrievers. Our objective is not to match performance of BM25 (or neural ranker), but improve it by incorporating temporal information into the ranking process.
Summary of reasons to publish: 1. Important topic.
2. Use of LLMs for query understanding is a trendy research direction.