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An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks
Open accessAbstract
The increasing use of retrieval-augmented applications, where large language models (LLMs) are instructed to generate queries, assess relevance, and synthesise responses, has introduced new challenges in Information Retrieval (IR).The lack of transparency in LLMs means that even subtle variations in instructions can significantly impact the quality, consistency, and reliability of their responses.To address this issue, we propose Instruction-Response Study, an experimental framework for systematically analysing how task instructions influence LLM-generated responses in IR tasks.This paper presents the core components of the framework and demonstrates its utility through four case studies, examining 1) the effect of IR tasks on query formulation, 2) the impact of topic information size on retrieval effectiveness, 3) the reproducibility of LLM-generated queries, and 4) the role of meta-instructions in diversifying instruction design.The findings highlight how the proposed framework enables controlled experimentation on instruction design and its effects, offering a foundation for optimising prompt engineering and enhancing retrieval-augmented applications.
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TL;DR
The paper proposes Instruction-Response Study, an experimental framework for analysing how task instructions shape LLM-generated responses in IR tasks, and demonstrates it through four case studies on query formulation. It finds that instruction design, model choice, topic information, and reproducibility all materially affect generated queries and retrieval performance, though many effects are small or not statistically significant.
Key findings
- In Case Study 1, LLM-generated queries averaged 13–26 words; recall-oriented queries were longest and precision-oriented shortest, with gpt4o-mini producing the longest queries and qwen2.5 the shortest. Two-way ANOVA showed significant main effects of Task and Model on query length but no interaction; effect size was small for Task (η² = 0.024) and large for Model (η² = 0.225).
- In Case Study 2, retrieval effectiveness (nDCG@1000) tended to decrease as more topic information was included in the instruction, with exceptions in TN and TDN for gpt4o-mini and llama3.3; description information appeared to contribute more than narrative. Topic Information and Model had significant main effects but no interaction, with small effect sizes (Topic η² = 0.043; Model η² = 0.055).
- In Case Study 3, no variance values were zero across ten identical instructions, and proprietary models showed greater inconsistency than open-weight models, reaching up to 15× in DocID variance (gemini1.5 0.109 vs. llama3.3 0.007). A one-way ANOVA on query variance was significant (F = 23.46, p = 0.00), with a large Model effect size (η² = 0.264).
- In Case Study 4, the simple initial instruction was consistently among the top three; some diversified instructions scored higher, e.g. median nDCG rose from 0.061 to 0.107 for qwen2.5 (with score) in high-recall and median MRR rose from 0.011 to 0.066 for llama3.3 (with score) in high-precision, but Wilcoxon Rank-Sum tests found none of these differences statistically significant.
Methods
- The paper is a perspective paper introducing the Instruction-Response Study framework, whose components include IR tasks, test collections, instructions, LLMs, tools, responses, and measurements; four case studies apply it primarily to query formulation.
- Case studies use 50 topics from the TREC 2005 Robust Track and the Aquaint collection indexed with OpenSearch 2.18.0 using Lucene query syntax and BM25; four LLMs are tested: gpt-4o-mini-2024-07-18, gemini-1.5-flash, llama3.3:70b-instruct-q4_K_M, and qwen2.5:72b-instruct-q4_K_M, with temperature 0.0.
- Analyses include descriptive statistics, two-way ANOVA with Tukey HSD, one-way ANOVA, and Wilcoxon Rank-Sum tests; Case 2 and 3 retrieve top 1,000 documents and use nDCG@1000, Case 3 repeats instructions ten times and measures query-string, top-ranked document, and MRR inconsistency, and Case 4 splits topics into 25 training and 25 validation topics with a meta-instruction feedback loop over N = 10 epochs.
Limitations
- The paper states that the case studies are not exhaustive or fully generalisable, that findings should not be overgeneralised, that alternative ranking models such as DPR could be tested, that meta-instruction design could be further diversified and evaluated, and that self-correction and self-contrast methods are promising future directions.
- The paper also states that the LLMs may have been pre-trained on datasets including the document collections and topic descriptions used, and that no measures were implemented to control for potential data leakage.
- Evident from the text: the case studies focus mainly on query formulation, use one test collection (50 TREC 2005 Robust topics) and four LLMs, and report small effect sizes for topic information and no statistically significant improvements from diversified instructions in Case Study 4.
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In the paper
“Additionally, description information appears to contribute more to retrieval effectiveness than narrative information.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.2.5 Results.
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“This model exhibited the highest variance in query strings for this topic.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.3.6 Results.
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“In contrast, the influence of task type appears limited, though small variations can be observed.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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“Additionally, later iterations tend to introduce another step for reflecting on queries in terms of relevance or query syntax.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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“The initial instruction (denoted as 0: in the table 8) consistently ranks among the top three across most experimental settings.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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“No strong evidence was found to suggest that adding further descriptive elements to the instruction significantly improves retrieval performance.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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“Contrary to our initial research assumption, the experimental results suggest that providing a greater amount of topic information in the model instruction can degrade retrieval effectiveness in a high-recall retrieval task.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.2.5 Results.
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“2) A notable observation from Case Study 2 is that increasing topic information does not necessarily enhance retrieval performance in high-recall tasks.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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“As shown, across all examined LLMs, retrieval effectiveness tends to decrease as more topic information is included in the model instruction.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.2.5 Results.
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“More complex instructions did not consistently yield better results, indicating that minimal, well-structured prompts may be sufficient.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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“Some instructions (not shown) further instruct the model to categorise keywords.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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“As shown, both query strings and MRR scores vary across the ten trials.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.3.6 Results.
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“Moreover, the length and phrasing of individual steps varied across iterations.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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In the paper
“As shown, the main effects of Topic Information and Model are significant, but their interaction is not.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.2.5 Results.
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In the paper
“As shown, both Task and Model have a significant main effect on query length, but no interaction effect is observed.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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“While all models exhibited some level of response variation when given identical instructions, proprietary models showed greater inconsistencies across repeated trials.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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“Second, query length varies across tasks, with recall-oriented queries being the longest on average, followed by diversity-focused queries, while precision-focused queries tend to be the shortest.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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“First, none of the variance values is zero, indicating that LLM responses were not identical across the ten trials of the same instruction.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.3.6 Results.
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“Clear differences can be observed among the models, with proprietary models tending to use double quotations for phrase operators more frequently, while open-weight models make greater use of the OR operator.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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“For example, instructions in later iterations include an additional step for listing keywords before formulating queries.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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“Second, proprietary models exhibit greater variance than open-weight models, a trend consistent across all three metrics.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.3.6 Results.
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“For instance, in qwen2.5, the use of phrase operators is slightly higher in the recall task compared to the others.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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In the paper
“Third, query length differs across models, with gpt4o-mini producing the longest queries on average, followed by gemini1.5, llama3.3,and qwen2.5, which consistently generates the shortest queries across all tasks.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.5 Results.
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In the paper
“However, since these architectures rely on the top 𝑘 results from the IR system, they may not explicitly perform the search action of document selection (i.e., clicks) from search results.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
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In the paper
“In contrast, in an assisted literature survey task [3], GIIs are assigned a high-recall retrieval task, as literature surveys often require exhaustive search on a given topic.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
What the sources say
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In the paper
“Since the scores were not normally distributed, the table reports the median values.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.6 Results.
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In the paper
“For example, Chen et al. [8] demonstrated that attention heads processing axiomatic concepts such as term frequency could be identified in a neural network IR model using a mechanistic interpretability technique.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
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In the paper
“The findings highlight how the proposed framework enables controlled experimentation on instruction design and its effects, offering a foundation for optimising prompt engineering and enhancing retrieval-augmented applications.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > Abstract
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“The descriptions are intentionally brief to prevent over-specification in instructions.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.2 Experimental Setting I: Task and Instruction.
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“The temperature parameter was set to 0.0 to maximise the reproducibility of model responses throughout this study.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.4 Experimental Setting III: Large Language Models.
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In the paper
“In this original RAG architecture, the LLM performs the search action of query formulation as a GII.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
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- Not matchedcites [20]
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In the paper
“This opacity complicates efforts to design reliable GIIs for IR tasks and raises concerns regarding their interpretability, robustness, and reproducibility in real-world applications.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 1 Introduction
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In the paper
“The proposed framework provides a structured approach to investigating these mechanisms and assessing the influence of external factors on LLM-driven retrieval.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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- Rests on an assumption: LLMs function as black boxes, and their underlying mechanisms for search-related actions remain largely unexplored.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This highlights the influence of both model selection and task design on the responses generated by query formulation instructions.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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In the paper
“However, this does not guarantee a similar distribution of retrieval effectiveness, as effectiveness is influenced by the generated queries.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.4.3 Experimental Setting II: Test Collection and Search Tool.
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In the paper
“In this advanced architecture, the LLM performs search actions including query formulation, query reformulation, and relevance judgements as a GII.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
What the sources say
- Not matchedcites [5]
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“In summary, while GIIs are embedded within diverse LLM-based applications, many IR tasks can be broadly characterised as high-precision, high-recall, high-diversity, or a combination thereof.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
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In the paper
“For instance, in open-domain QA [5,20], GIIs are effectively tasked with highprecision retrieval, as the top 𝑘 results serve as the primary contextual information.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [20]
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In the paper
“LLMs operate as black-box systems [8], making it difficult to fully understand how they generate search artefacts such as queries, document IDs, and relevance labels in response to instructions.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 1 Introduction
What the sources say
- Not matchedcites [8]
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In the paper
“For instance, Alaofi et al. [4] proposed three instruction designs-basic, rational, and utility prompts-to examine how different instructions affect LLM-based relevance predictions of retrieved documents.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
What the sources say
- Not matchedcites [4]
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In the paper
“As previously discussed, interpretability and explainability are key challenges for generative models due to the limited transparency of their inner workings [17].”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 2 Related Work
What the sources say
- Not matchedcites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Retrieval-Augmented Generation (RAG) and its variants often incorporate GII components within their architectures (See Figure 1) [5,20].”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 1 Introduction
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [20]
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In the paper
“All LLMs used in this study have a maximum token length of 128K, and none of the instructions or responses reported in this paper exceeded this limit.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 4.1.4 Experimental Setting III: Large Language Models.
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In the paper
“By configuring the key components of the framework, researchers can pursue diverse research questions in various experimental settings.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 6 Conclusion and Future Work
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In the paper
“The findings from this study provide empirical evidence to support more systematic and principled approaches to prompt design, offering a foundation for optimising retrieval functionality in retrieval-enhanced natural language applications.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.1 Findings and Implications
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- Rests on an assumption: Prompt design for retrieval components is critical to the quality of downstream tasks.
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In the paper
“As noted in Section 4, the scope and depth of research questions addressed in the case studies are not intended to be exhaustive or fully generalisable.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 5.2 Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This perspective paper proposed an experimental framework called Instruction-Response Study to better understand the complex interactions between instruction design, LLMs, responses, and performance in the context of Information Retrieval.”
§ An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks > 6 Conclusion and Future Work
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: LLMs function as black boxes, and their underlying mechanisms for search-related actions remain largely unexplored.
The authors state this as an assumption.
The experimental flow assumes the presence of a set of search topics, a document collection indexed by a retrieval tool, and corresponding qrels.
“Here, we assume the presence of a set of search topics, a document collection indexed by a retrieval tool, and corresponding qrels.”
The paper relies on this without support.
Reproducibility of GII responses is more critical when accuracy is prioritised over exhaustivity or diversity.
“Since the reproducibility of GII responses is more critical when accuracy is prioritised over exhaustivity or diversity, this case study focuses on high-precision retrieval.”
The paper relies on this without support.
Providing a rich set of topic information to GIIs is not always feasible.
“While providing a rich set of topic information to GIIs may be ideal, it is not always feasible.”
The paper relies on this without support.
Prompt design for retrieval components is critical to the quality of downstream tasks.
“For retrieval-augmented applications, prompt design for retrieval components is critical to the quality of downstream tasks.”
The paper relies on this without support.
The role of users in IR systems is expanding beyond humans and bots to include LLM-based agents.
“The role of users in IR systems is expanding beyond humans and bots to include LLM-based agents.”
The paper relies on this without support.
LLMs function as black boxes, and their underlying mechanisms for search-related actions remain largely unexplored.
“However, like human users, LLMs function as black boxes, and their underlying mechanisms for search-related actions remain largely unexplored.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
ir_datasets citation points to a different paper
MinorThe text cites reference 21 for ir_datasets, but reference 21 is a paper on Retrieval-Augmented Generation; the ir_datasets entry is numbered 22.
“Test collection data is accessed via the ir_datasets package [21].”
“21. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.”
Framework has both six and seven components
MinorThe section says the framework consists of six components, but it labels measurements as the seventh and final component.
“The proposed framework consists of six main components.”
“The seventh and final component, measurements, focuses on evaluating model responses.”
Query-length range excludes a reported mean
MinorThe stated upper bound is 26 words, but Table 1 reports a mean of 26.84 words for gpt4o-mini in the recall task.
“the queries generated by LLMs tend to be relatively long, averaging between 13 and 26 words.”
“| Recall | 26.84 (9.69) 23.08 (10.15) 17.58 (6.34) 15.12 (6.39) | | |”
Citations
Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
TREC: Experiment and evaluation in information retrieval
Vicedo, J., & Gómez, J. (2007) · J. Assoc. Inf. Sci. Technol. · doi:10.1002/asi.20583 · Cited by 1,130
Novelty and diversity in information retrieval evaluation
Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390446 · Cited by 997
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Jacovi, A., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.386 · Cited by 503
Perspectives on Large Language Models for Relevance Judgment
Faggioli, G., Dietz, L., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., et al. (2023) · Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval · doi:10.1145/3578337.3605136 · Cited by 184
Information‐seeking and mediated searching. Part 1. Theoretical framework and research design
Spink, A. H., Wilson, T. D., Ford, N. J., Foster, A. E., & Ellis, D. J. (2002) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.10081 · Cited by 180
Anthropomorphism in AI: hype and fallacy
Placani, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00419-4 · Cited by 170 · cited 1×
“Submit the generated query to retrieve the top 1000 documents.”
Large Language Models can Accurately Predict Searcher Preferences
Thomas, P., Spielman, S., Craswell, N., & Mitra, B. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657707 · Cited by 168
Flagged first, then by citations
Research works printed but not linked: 9
- Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=hSyW5go0v8
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- 10.5555/3600270.3602070 · DOI
Cited by
Sorted by citations
Geniie-Lab: A Testbed for Controlled Experimentation of Model Search Behaviour
Joho, H. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769479 · Cited by 1
Self-citationControlled Experimentation of Model Search Behaviour with Geniie-Lab
Joho, H. (2026) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3787894
Self-citationGuidedRAG: Semantic Steering of Retrieval-Augmented GenerationPreprint
de Haar, M. J. O., Stähle, T., & Gatti, L. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2607.26071
Metadata
- Authors
- Joho, H., & Jose, J. M.
- Affiliations
- University of Tsukuba (JP)
- University of Glasgow (GB)
- Venue
- Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2025-07-13
- Pages
- 3843-3852
- OpenAlex
- W4412378020
- Cited by
- 3
- References
- 17
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.64
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Funders
- Japan Society for the Promotion of Science (JP24K03048)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, retrieval-augmented generation, query generation, prompt engineering, instructional design, information retrieval evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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