Natural Language Interaction with Databases on Edge Devices in the Internet of Battlefield Things

Christopher D. Molek; K. Brent Venable; Niranjan Suri; Roberto Fronteddu

REVIEW 3 major objections 6 minor 18 references

Natural Language Interaction with Databases on Edge Devices in the Internet of Battlefield Things

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A second LLM that summarizes database output lets imperfect Cypher queries still answer users correctly, raising accuracy by 19.4 points.

desk verdict Sensible two-step idea, but the headline accuracy gain rests on a test set paraphrased by the winning model. read the letter →

arxiv 2506.06396 v1 pith:FB7AHRXT submitted 2025-06-05 cs.CL cs.AIcs.DB

Christopher D. Molek , Roberto Fronteddu , K. Brent Venable , Niranjan Suri This is my paper

classification cs.CLcs.AIcs.DB

keywords largelanguagemodelsInternetofBattlefieldThingsCypherquerygraphdatabasestext-to-Cypheredgecomputingnaturalinterfaceexactmatchmetric

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that natural-language database querying on edge devices does not require the LLM to produce the exact, expert-written Cypher query. Instead, a first LLM converts the user's question into a Cypher query that is allowed to return the correct data buried in extra information, and a second LLM condenses that database output into a natural-language answer. On a Neo4j database modeled on a 13-tower Army sensing area, the authors show that this two-step workflow raises the absolute answer accuracy for Llama 3.1 (8B) from 37.7% (exact-match-only) to 57.1%, a 19.4-point gain. If correct, the finding means medium-sized models that fit on laptops or edge hardware can answer questions about dynamic battlefield IoT data without the expensive fine-tuning or cloud calls that exact query generation typically requires.

What carries the argument

The load-bearing mechanism is a two-stage LLM pipeline. In Task 1, a single prompt containing the user's question, the database schema (node labels, property keys) and one worked Cypher example is fed to an LLM, which emits a Cypher query; the query runs against a Neo4j graph database. In Task 2, the database output, the original question, and a summarization instruction are given to an LLM (the same model in these experiments), which produces the final natural-language answer. The argument turns on the distinction between EM (query matches ground truth), Content Score (query's database result contains the needed data), and Output Score (Task 2 answers correctly from that result). The paper's key move is to show that high Content with low EM is common, and that the second stage can absorb the extra content.

What would settle it

Run the identical two-stage workflow on a larger, independently authored question set (for example, questions written by human users unfamiliar with the schema, or taken from the Text2Cypher datasets) and compare Absolute Score versus EM-only Absolute Score; if the gap approaches zero or reverses, the claimed relaxation of the exact-match requirement is an artifact of the self-generated benchmark. A narrower check: inject a query that returns a different tower's data than the user asked about and confirm Task 2 reports that wrong data as the answer, demonstrating the misinformation failure mode.

Watch

Extended reading notes

Core claim

The central claim is that Exact Match (EM) — the usual yardstick that compares a generated Cypher query character-for-character against a ground-truth query — is unnecessarily strict for end-to-end question answering. The paper shows that when a second LLM is given the database response together with the user's original question, it can extract the correct information even when the first LLM's Cypher query returned the right content plus unrelated rows. On 77 rephrased questions about the sensor/tower database, Llama 3.1:8b's Content Score (queries whose database output contains the answer) reached 61.0%, while its EM score was 37.7%; the second stage converted that content into correct user answers 96.1% of the time, giving an Absolute Score of 57.1%. Across all four tested models, the workflow's Absolute Score exceeded the EM-only Absolute Score, and for three of the four models it more than doubled. The paper concludes that for edge-deployed, zero-shot text-to-Cypher systems, a post-processing summarization stage is an effective substitute for perfect query generation.

Load-bearing premise

The 77-question test set was produced by asking Llama3.1:8b to rephrase seven questions ten times each, and the summaries were manually checked by the authors; if those rephrasings happen to fit the style of that same model, the measured gains over exact match may not transfer to independently written questions.

Editorial extensions

If this is right

Exact Match is the wrong optimization target for edge-deployed NL-to-Cypher question answering; content-preserving queries plus a summarization stage can deliver correct answers.
Medium-sized models (1-10B parameters) running zero-shot on a laptop GPU can serve as a natural-language interface to graph databases, making on-device IoBT querying feasible without fine-tuning.
The second LLM stage doubles as a filter for verbose or noisy database output: for Llama3.2:3b, Output Score (100%) exceeded Content Score (71.4%), showing the summarizer can pick correct content out of longer results.
Misinformation errors — syntactically valid Cypher that retrieves the wrong entity — propagate through Task 2 and reach the user, so prompt design must minimize entity-number confusion (e.g., the tower-8 example overriding the user's tower number).
For resource-constrained settings, model choice matters most in Task 1: the best model (Llama3.1:8b) had both the highest Content Score and the lowest Misinformation Score, which together drove its overall accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 19.4-point gain is measured on only 77 questions, most of them variants of seven templates; larger, more diverse benchmarks are needed before the claim 'EM can be relaxed' should be treated as general, and the paper's future-work section does not add an independent test set.
If the second-stage LLM were a different, stronger model than the first, the two-stage design could in principle push accuracy higher by pairing a cheap query generator with a strong summarizer; the paper does not test this asymmetric configuration, so the 19.4% figure is a lower bound on what the architecture could achieve.
The same two-stage pattern applies to any formal-language-to-natural-language round trip (SQL, SPARQL, or API calls), not just Cypher; any setting where imperfect code still returns an information-rich result could inherit the relaxation argument.
The trick question about the nonexistent tower 22 is a useful probe: a correct response requires Task 2 to report that no such tower exists rather than hallucinate coordinates, and the paper's metrics count an empty database result as a potential failure case.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The thing to know: this paper's central architectural claim—that a second summarization LLM lets you relax exact-match (EM) on generated Cypher queries—is sensible, and the qualitative pattern holds across all four models. But the headline 19.4% gain is built on a test set that Llama3.1:8b itself paraphrased, so the number is not credible as an unbiased estimate.

What's genuinely new: the two-step pipeline itself. Prior text-to-Cypher work (Hornsteiner et al., Mandilara et al., Tran et al.) optimizes query generation; this paper adds a Task 2 summarizer that can recover the answer from a sub-optimal query's output. That is a legitimate extension, and the evaluation framework with Content Score and Output Score is a reasonable way to expose the distinction. The finding that Output Score exceeds Content Score for every model except one, and Content Score exceeds EM everywhere, is a real empirical pattern that supports the relaxation idea. The writing is clear, the related work is fair, and the edge-device motivation is concrete.

Soft spots, in order of severity. First, Section IV.B states that Llama3.1:8b generated all 77 rephrasings, manually checked. That same model then scores best on both Task 1 and Task 2. The test set is in-distribution for the winner, which inflates its absolute scores relative to the others. The 19.4-point improvement (Table V) is ~15 questions on one run, with no confidence intervals. Second, the database is tiny—135 nodes, 7 original questions—so generalization to real IoBT data is untested. Third, no artifacts or code are released, so the content-score "verification" is not independently reproducible. The trick question (tower 22) is fine, but its inclusion shifts scores nontrivially on a small n.

None of this kills the architecture; the paper is a proof-of-concept, and the qualitative trend is consistent. But the specific quantitative claim in the abstract overreaches.

Who's this for? People working on natural-language database interfaces for constrained/edge settings, and anyone interested in text-to-Cypher. It deserves a serious referee, but with a request to re-run on an independently written test set, report variance, and release data. Bottom line: send it out, but expect heavy revision on the evaluation.

Referee Report

3 major / 6 minor

Summary. The manuscript proposes a two-stage LLM pipeline for natural-language access to a Neo4j graph database on edge devices: a first LLM generates Cypher queries from user questions and a second LLM summarizes the raw database output into a natural-language answer. The authors evaluate four medium-sized LLMs (Gemma2:2b, Llama3.2:3b, Llama3.1:8b, Deepseek-coder:6.7b) on a database representing the US Army MSA sensor network. The evaluation uses 7 hand-written questions and 70 Llama3.1:8b-generated paraphrases (77 items). Metrics include Exact Match (EM), Content, Output, Misinformation, and Absolute scores. The headline result is that Llama3.1:8b's Absolute Score rises from 37.7% (EM-only) to 57.1% when non-EM but content-correct Cypher queries are accepted, which the paper interprets as a '19.4% increase' enabled by the second summarizing stage.

Significance. The two-stage workflow is a sensible and practically motivated design for resource-constrained deployments, and the authors correctly identify that the second stage can mitigate imperfect query generation. The qualitative finding that Output scores generally exceed Content scores for all models supports this idea, and the comparison across four models provides useful evidence that smaller LLMs can handle both Cypher generation and summarization. However, the paper's central quantitative claim rests on a benchmark generated by the same model that achieves the best results, and the scoring methodology is underspecified. If re-evaluated with an independent test set and transparent scoring, the result could be a useful contribution to text-to-Cypher and edge-LLM research. The paper also does not provide code, data, or full prompts, which would be needed for replication.

major comments (3)

[Section V, paragraph before Table IV] The 77-question test set is generated by using Llama3.1:8b to rephrase each of the seven original questions ten times, and the same model achieves the highest scores on this test set. This creates a circularity: the rephrasings are in-distribution for the champion model, potentially inflating its Task 1 and Task 2 scores relative to the other models. The paper should generate the paraphrases with a different model (or human annotators), use an existing independent Text2Cypher benchmark, or at minimum report results separately on the original 7 questions and the 70 paraphrases to demonstrate that the effect is not an artifact of in-distribution test data.
[Section IV.C] The definitions of Content, Misinformation, Output, and Absolute scores depend on judgments of whether a query 'returns the information needed' and whether a response is 'correct.' The manuscript does not state whether these judgments were made manually or automatically, whether the evaluator was blinded to model identity, or whether multiple annotators were used. The text acknowledges that 'the variability in the amount of extra information makes it difficult to automate the process,' implying manual assessment, but no rubric or inter-annotator reliability is reported. Since the headline 19.4-point gain is the difference between EM and Absolute scores, and EM is the only fully objective metric, this underspecification is a load-bearing gap.
[Section IV.B/V, Table V] The 77-item dataset is not an independent sample: it contains 10 paraphrases of only 7 base questions, with one deliberately impossible question. The reported 19.4 percentage-point difference for Llama3.1:8b corresponds to 15 questions (from 29/77 to 44/77), and no confidence intervals, significance tests, or per-base-question analyses are provided. The authors should report exact counts, per-question results, and uncertainty intervals (e.g., Wilson score intervals) so that the reader can assess the precision of the claim. As written, the point estimate is not robust to small perturbations in the test set.

minor comments (6)

[Abstract] The phrase '19.4% increase in accuracy' is ambiguous: the increase is 19.4 percentage points (absolute), not 19.4% relative. The abstract should state the baseline and absolute/relative nature of the increase to avoid overstatement.
[Section IV.B] The rephrasing methodology (using Llama3.1:8b to generate ten paraphrases per question) appears only in Section V, not in Section IV (Experimental Design). It should be described in Section IV.B for a complete experimental design.
[Section V] There are several typographical errors: 'inlcudes' should be 'includes' in Section IV.B; 'superfluos' should be 'superfluous' in Section V; and Table II title 'USING7QUESTIONS' is missing spaces.
[Table IV] The Deepseek-coder:6.7b EM score is listed as 20.78%, while all other scores in the table use one decimal place; this should be 20.8% for consistency.
[Section V] The paper would be strengthened by a reproducibility statement or link to the dataset, the ground-truth Cypher queries, and the full set of 77 questions and prompts, as none are currently provided.
[Section V] The authors note that misinformation errors often use the engineered prompt's example tower number instead of the user's input. This suggests that the choice of prompt example (tower 8) introduces a systematic bias; testing with a neutral or varying example would make the results more generalizable.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the 19.4% gain is a measured metric difference, not a fitted prediction; the self-generated benchmark is a validity concern, not circularity.

full rationale

The paper's central claim is empirical rather than derivational: a two-step LLM pipeline relaxes the Exact Match requirement for Cypher generation and improves end-to-end question answering. The headline 19.4% increase is the difference between the Absolute Score (57.1%) and the EM-only Absolute Score (37.7%) for Llama3.1:8b in Table V, both computed on the same 77-question benchmark. This is a comparison of two metrics on the same outputs, not a fitted parameter that is later called a prediction, and no equation in the paper defines the result as an identity with its inputs. The main evaluation caveat is that the 77-question set was created by paraphrasing the original seven questions with Llama3.1:8b and manually checking them (Section IV.B), and Llama3.1:8b is also the top-scoring model. That could make the benchmark in-distribution for the champion model and weaken the generality of the quantitative gain, but it is a benchmark-construction and external-validity concern rather than circularity: the test items are not logically forced to favor one model, and the scores are derived from actual database responses and manually graded natural-language answers. The only self-citation, [17], includes co-author Suri and supports a peripheral statement about edge hardware accelerators; it is not load-bearing, and no uniqueness theorem or ansatz is imported from prior work by the authors. Under the required standard of exhibiting an equation-level or fit-level equivalence, no circular step can be identified, so the score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The evaluation rests on self-authored ground truth, self-generated rephrasings, and a hand-crafted prompt with a concrete example; these are the upstream inputs that the reported scores depend on.

free parameters (2)

Sampling temperature = 0
Set to zero for all model runs (Section V) to make outputs deterministic; a hand-chosen experimental setting rather than a fitted constant, but it affects all reported results.
Prompt example tower number = 8
The Task 1 prompt includes an example query referencing tower 8 (Figure 2); the authors note this example caused misinformation errors when models copied it (Section V), so it is a hand-chosen component that shaped results.

assumptions (3)

ad hoc to paper Ground-truth Cypher queries in the evaluation are correct and representative.
The paper authors constructed the ground truth for the 7 questions; no independent benchmark is used, so the measured scores assume these queries are correct and canonical (Section IV.C).
domain assumption The 77 rephrased questions are semantically equivalent to the original 7 questions.
The rephrasings were generated by Llama3.1:8b and manually checked by the authors (Section IV.B); any semantic drift would bias the model comparison.
domain assumption The database schema described in the prompt is complete and matches the actual database.
The prompt in Figure 2 lists node labels and property keys; if incomplete, Cypher generation results would not generalize (Section III).

how reviews work

0 comments

Cite this review

Pith. "Pith review of Natural Language Interaction with Databases on Edge Devices in the Internet of Battlefield Things." pith.science (2026). https://pith.science/paper/FB7AHRXT

@misc{pith2026250606396,
  author       = {Pith},
  title        = {Pith review of: Natural Language Interaction with Databases on Edge Devices in the Internet of Battlefield Things},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FB7AHRXT}},
  note         = {Machine review of arXiv:2506.06396}
}

read the original abstract

The expansion of the Internet of Things (IoT) in the battlefield, Internet of Battlefield Things (IoBT), gives rise to new opportunities for enhancing situational awareness. To increase the potential of IoBT for situational awareness in critical decision making, the data from these devices must be processed into consumer-ready information objects, and made available to consumers on demand. To address this challenge we propose a workflow that makes use of natural language processing (NLP) to query a database technology and return a response in natural language. Our solution utilizes Large Language Models (LLMs) that are sized for edge devices to perform NLP as well as graphical databases which are well suited for dynamic connected networks which are pervasive in the IoBT. Our architecture employs LLMs for both mapping questions in natural language to Cypher database queries as well as to summarize the database output back to the user in natural language. We evaluate several medium sized LLMs for both of these tasks on a database representing publicly available data from the US Army's Multipurpose Sensing Area (MSA) at the Jornada Range in Las Cruces, NM. We observe that Llama 3.1 (8 billion parameters) outperforms the other models across all the considered metrics. Most importantly, we note that, unlike current methods, our two step approach allows the relaxation of the Exact Match (EM) requirement of the produced Cypher queries with ground truth code and, in this way, it achieves a 19.4% increase in accuracy. Our workflow lays the ground work for deploying LLMs on edge devices to enable natural language interactions with databases containing information objects for critical decision making.

Figures

Figures reproduced from arXiv: 2506.06396 by the authors.

**Figure 2.** regards the relationship between a sensor s and the tower on which it is located. Task 1 Prompt: What is the location of tower 4? You are an AI agent that is an expert with Cypher query language and relational databases that thinks before answering. Example: { "step1": "To solve this problem, I need to identify the correct nodes and labels...", "step2": "Based on results from step1, I need to identify the name of th… view at source ↗

**Figure 1.** Database natural language interaction workflow. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 3.** Example prompt used for Task 2. A correct output of Task 2 for this case would be a sentence similar to: The location of Tower 4 is at 32.58088351° latitude and - 106.7533307° longitude [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 2.** Figure 2: B. Questions To evaluate our architecture’s question-answering performance, we generate several natural language questions, as shown in Table I. Note that one of the questions is a trick question asking about tower 22, in that tower 22 does not exist. These questions …

**Figure 3.** Figure 3: The Llama3.2:3b model produced the longest Content [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

18 extracted references · 14 canonical work pages

[1]

A Smart City Assisted Internet of Military Defense Things Architecture,

D. Tsiolis and A. Leivadeas, “A Smart City Assisted Internet of Military Defense Things Architecture,”IEEE Internet of Things Magazine, vol. 8, no. 2, pp. 18–24, Mar. 2025

work page 2025
[2]

A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments,

S. Bhardwaj, P. Singh, and M. K. Pandit, “A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments,” in2024 16th International Conference on Computer and Automation Engineering (ICCAE), Mar. 2024, pp. 168–172

work page 2024
[3]

Large language models surpass human experts in predicting neuroscience results,

X. Luo, A. Rechardt, G. Sun, K. K. Nejad, F. Y ´a˜nez, B. Yilmaz, K. Lee, A. O. Cohen, V . Borghesani, A. Pashkov, D. Marinazzo, J. Nicholas, A. Salatiello, I. Sucholutsky, P. Minervini, S. Razavi, R. Rocca, E. Yusifov, T. Okalova, N. Gu, M. Ferianc, M. Khona, K. R. Patil, P.-S. Lee, R. Mata, N. E. Myers, J. K. Bizley, S. Musslick, I. P. Bilgin, G. Niso, ...

work page 2025
[4]

Large Language Models for Information Retrieval: A Survey,

Y . Zhu, H. Yuan, S. Wang, J. Liu, W. Liu, C. Deng, H. Chen, Z. Liu, Z. Dou, and J.-R. Wen, “Large Language Models for Information Retrieval: A Survey,” Sep. 2024,arXiv preprint arXiv:2308.07107

arXiv 2024
[5]

A Review on Code Generation with LLMs: Application and Evaluation,

J. Wang and Y . Chen, “A Review on Code Generation with LLMs: Application and Evaluation,” in2023 IEEE International Conference on Medical Artificial Intelligence (MedAI), Nov. 2023, pp. 284–289

work page 2023
[6]

Cypher: An Evolving Query Language for Property Graphs,

N. Francis, A. Green, P. Guagliardo, L. Libkin, T. Lindaaker, V . Marsault, S. Plantikow, M. Rydberg, P. Selmer, and A. Taylor, “Cypher: An Evolving Query Language for Property Graphs,” in Proceedings of the 2018 International Conference on Management of Data, ser. SIGMOD ’18. New York, NY , USA: Association for Computing Machinery, May 2018, pp. 1433–1445

work page 2018
[7]

Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases,

M. Hornsteiner, M. Kreussel, C. Steindl, F. Ebner, P. Empl, and S. Sch ¨onig, “Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases,”Future Internet, vol. 16, no. 12, p. 438, Nov. 2024

work page 2024
[8]

Decoding the Mystery: How Can LLMs Turn Text Into Cypher in Complex Knowledge Graphs?

I. Mandilara, C. Maria Androna, E. Fotopoulou, A. Zafeiropoulos, and S. Papavassiliou, “Decoding the Mystery: How Can LLMs Turn Text Into Cypher in Complex Knowledge Graphs?”IEEE Access, vol. 13, pp. 80 981–81 001, 2025

work page 2025

Show all 18 references

[9]

Text2Cypher: Bridging Natural Language and Graph Databases,

M. G. Ozsoy, L. Messallem, J. Besga, and G. Minneci, “Text2Cypher: Bridging Natural Language and Graph Databases,” Dec. 2024,arXiv preprint arXiv:2412.10064

2024 arXiv
[10]

SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task,

Z. Zhong, L. Zhong, Z. Sun, Q. Jin, Z. Qin, and X. Zhang, “SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task,” Jan. 2025,arXiv preprint arXiv:2406.10710

2025 arXiv
[11]

Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model,

Q.-B.-H. Tran, A. A. Waheed, and S.-T. Chung, “Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model,”Applied Sciences, vol. 14, no. 17, p. 7881, Jan. 2024

2024
[12]

Frameworks for Querying Databases Using Natural Language: A Literature Review – NLP-to-DB Querying Frameworks,

S. A. C. Bukhari, H. S. Dar, M. I. Lali, F. Keshtkar, K. M. Malik, and S. Kadry, “Frameworks for Querying Databases Using Natural Language: A Literature Review – NLP-to-DB Querying Frameworks,” International Journal of Data Warehousing and Mining (IJDWM), vol. 17, no. 2, pp. 2...

2021
[13]

DSL-Xpert: LLM-driven Generic DSL Code Generation,

V . Lamas, M. R. Luaces, and D. Garcia-Gonzalez, “DSL-Xpert: LLM-driven Generic DSL Code Generation,” inProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineer- ing Languages and Systems. Linz Austria: ACM, Sep. 2024, pp. 16–20

2024
[14]

From a Natural to a Formal Language with DSL Assistant,

M. M. Mosthaf and A. Wasowski, “From a Natural to a Formal Language with DSL Assistant,” inProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. Linz Austria: ACM, Sep. 2024, pp. 541–549

2024
[15]

Leveraging Code to Improve In-context Learning for Semantic Parsing,

B. Bogin, S. Gupta, P. Clark, and A. Sabharwal, “Leveraging Code to Improve In-context Learning for Semantic Parsing,” Mar. 2024,arXiv preprint arXiv:2311.09519

2024 arXiv
[16]

DocCGen: Document-based Controlled Code Generation,

S. Pimparkhede, M. Kammakomati, S. Tamilselvam, P. Kumar, A. P. Kumar, and P. Bhattacharyya, “DocCGen: Document-based Controlled Code Generation,” Jul. 2024,arXiv preprint arXiv:2406.11925

2024 arXiv
[17]

Collaborative Inference in Resource-Constrained Edge Networks: Challenges and Opportunities,

N. Ng, A. Souza, S. Diggavi, N. Suri, T. Abdelzaher, D. Towsley, and P. Shenoy, “Collaborative Inference in Resource-Constrained Edge Networks: Challenges and Opportunities,” inMILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), Oct. 2024, pp. 1–6

2024
[18]

Large Language Models: A Survey,

S. Minaee, T. Mikolov, N. Nikzad, M. Chenaghlu, R. Socher, X. Am- atriain, and J. Gao, “Large Language Models: A Survey,” Feb. 2024, arXiv preprint arXiv:2402.06196

2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

A Smart City Assisted Internet of Military Defense Things Architecture,

D. Tsiolis and A. Leivadeas, “A Smart City Assisted Internet of Military Defense Things Architecture,”IEEE Internet of Things Magazine, vol. 8, no. 2, pp. 18–24, Mar. 2025

work page 2025

[2] [2]

A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments,

S. Bhardwaj, P. Singh, and M. K. Pandit, “A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments,” in2024 16th International Conference on Computer and Automation Engineering (ICCAE), Mar. 2024, pp. 168–172

work page 2024

[3] [3]

Large language models surpass human experts in predicting neuroscience results,

X. Luo, A. Rechardt, G. Sun, K. K. Nejad, F. Y ´a˜nez, B. Yilmaz, K. Lee, A. O. Cohen, V . Borghesani, A. Pashkov, D. Marinazzo, J. Nicholas, A. Salatiello, I. Sucholutsky, P. Minervini, S. Razavi, R. Rocca, E. Yusifov, T. Okalova, N. Gu, M. Ferianc, M. Khona, K. R. Patil, P.-S. Lee, R. Mata, N. E. Myers, J. K. Bizley, S. Musslick, I. P. Bilgin, G. Niso, ...

work page 2025

[4] [4]

Large Language Models for Information Retrieval: A Survey,

Y . Zhu, H. Yuan, S. Wang, J. Liu, W. Liu, C. Deng, H. Chen, Z. Liu, Z. Dou, and J.-R. Wen, “Large Language Models for Information Retrieval: A Survey,” Sep. 2024,arXiv preprint arXiv:2308.07107

arXiv 2024

[5] [5]

A Review on Code Generation with LLMs: Application and Evaluation,

J. Wang and Y . Chen, “A Review on Code Generation with LLMs: Application and Evaluation,” in2023 IEEE International Conference on Medical Artificial Intelligence (MedAI), Nov. 2023, pp. 284–289

work page 2023

[6] [6]

Cypher: An Evolving Query Language for Property Graphs,

N. Francis, A. Green, P. Guagliardo, L. Libkin, T. Lindaaker, V . Marsault, S. Plantikow, M. Rydberg, P. Selmer, and A. Taylor, “Cypher: An Evolving Query Language for Property Graphs,” in Proceedings of the 2018 International Conference on Management of Data, ser. SIGMOD ’18. New York, NY , USA: Association for Computing Machinery, May 2018, pp. 1433–1445

work page 2018

[7] [7]

Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases,

M. Hornsteiner, M. Kreussel, C. Steindl, F. Ebner, P. Empl, and S. Sch ¨onig, “Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases,”Future Internet, vol. 16, no. 12, p. 438, Nov. 2024

work page 2024

[8] [8]

Decoding the Mystery: How Can LLMs Turn Text Into Cypher in Complex Knowledge Graphs?

I. Mandilara, C. Maria Androna, E. Fotopoulou, A. Zafeiropoulos, and S. Papavassiliou, “Decoding the Mystery: How Can LLMs Turn Text Into Cypher in Complex Knowledge Graphs?”IEEE Access, vol. 13, pp. 80 981–81 001, 2025

work page 2025