Pith. sign in

REVIEW 3 major objections 4 minor 45 references

Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper argues that legal chatbots can combine free-text conversation with a logic engine that, not the LLM, decides each step and the final conclusion.

desk verdict A clear architecture proposal for LLM-bounded-by-DCR chatbots, but the title's 'accurate' is a research goal, not a demonstrated property. read the letter →

arxiv 2608.01346 v1 pith:MLS3DVTZ submitted 2026-08-02 cs.CY

Ilias Chalkidis , Vlad Paul Cosma , S{o}ren Debois , Daniel Hershcovich , Thomas Hildebrandt , Hugo A. Lòpez , Amogh Raina , Konstantinos Varvoutas
show 1 more author
Tilman Zuckmantel
This is my paper
classification cs.CY
keywords conversationalAIhybridneuro-symboliclargelanguagemodelsDCRgraphseGovernmentexplainabilitycomputationallawlegalchatbots
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

The paper sets out to solve a practical problem: an eGovernment chatbot must let citizens type naturally, yet its legal consequences must be accurate enough for a public authority to stand behind. Its proposed answer is to split the job. A large language model turns free-text replies into candidate event-value pairs, but it never decides anything; a rule engine executing a Dynamic Condition Response (DCR) graph owns the state, decides which questions are enabled, which obligations are pending, and when a conclusion is reached. The paper's core claim is that this preserves process-level correctness—the engine cannot be induced to skip an obligation or reach an unpermitted conclusion—and produces a traceable, auditable conversation. A running Covid-19 close-contact prototype and an ongoing disability-grant case illustrate the idea; the load-bearing caveat is that everything is only as correct as the graph's encoding of the underlying law.

What carries the argument

DCR graphs: a declarative, executable process model whose nodes are activities or events and whose edges encode logical constraints such as conditions (one activity must happen before another), responses (one activity creates an obligation to eventually do another), and inclusions/exclusions (activities entering or leaving the model). The graph's marking records execution history, pending obligations, and deadlines, so the engine can compute enabled versus pending events at every step. That computation is what turns the conversation into a constrained decision procedure; the LLM is bounded to the set of enabled, well-typed activities.

What would settle it

Take a scenario from the 11-page Covid-19 guideline whose prescribed answer is unambiguous, run it through the public prototype, and compare the engine's terminal conclusion with the guideline's explicit instruction; any disagreement in a case where the graph was accepted as correct would falsify the claim at the encoding level. To test the process-level guarantee itself, inspect the engine's state after adversarial inputs: a reachable state in which an obligation remains forever pending without being disabled, or in which an excluded activity executes, would refute the stated invariant.

Watch

Extended reading notes

Core claim

The central claim is that explainable and accurate legal chatbots can be built by making a large language model a bounded conversational front end to a Dynamic Condition Response (DCR) graph engine. DCR graphs represent a law or guideline as activities with conditions, responses, inclusions and exclusions, typed data, and deadlines; the engine maintains a marking of which activities have been executed, are pending, or are currently included, and it computes the exact set of enabled activities at each state. The LLM is tasked only with interpreting free-text user utterances into typed event-value pairs drawn from that enabled set, with inferred values requiring explicit user confirmation. The

Load-bearing premise

Everything hinges on the DCR graph being a correct and complete formalization of the applicable law or guideline; if the graph encodes a wrong or incomplete rule, the engine will perfectly enforce that wrong rule.

Editorial extensions

If this is right

  • Citizens can answer a government chatbot in free text and still receive answers guaranteed consistent with the encoded process, with no unpermitted conclusion reached along the way.
  • Every conversation produces an execution log over the graph, so the exact activities, data values, and state transitions can be audited after the fact.
  • The same architecture can be reused across guidelines because the DCR graph and its execution API are domain-independent; only the graph and the NLP endpoint change.
  • The approach inherits the model-engineering bottleneck: the paper notes DCR graphs become unwieldy beyond roughly 20–30 activities and current text-to-graph tools ignore data and legal-language specifics, so expert human modelling remains necessary.
  • The design separates the two failure modes: the engine's guarantees concern process correctness, while the remaining risk is whether the LLM inferred the right value from the user's words.

Reading between the lines

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

  • The bounded-interface pattern could generalize beyond law to any high-stakes, rule-governed service—benefits eligibility, permits, regulatory filings—where state and obligations matter more than fluent generation.
  • Because the DCR graph is the actual decision procedure, certifying the graph against the legal text becomes the natural audit unit; adding explanation endpoints to answer 'why not this activity?' would make the audit directly citizen-facing, something the paper lists as future work.
  • The acknowledged text-to-graph bottleneck suggests a testable extension: let modern LLMs propose candidate DCR mappings with human-in-the-loop correction, then measure whether the resulting graphs capture data-dependent legal rules better than the current tools.
  • The paper's planned LLM-as-a-judge evaluation, calibrated against expert annotations, could be used to compare this hybrid architecture against RAG baselines specifically on process consistency, not just answer fluency.
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.

Referee Report

3 major / 4 minor

Summary. The paper proposes a Conversational Hybrid AI (CHAI) architecture for eGovernment chatbots. An LLM-based natural-language component interprets free-text user input, while a symbolic DCR-graph engine maintains the authoritative process state, determines which activities are enabled/pending, and produces the final conclusion. The architecture is illustrated with two examples: a COVID-19 close-contact guideline and a supplementary-grants-for-students-with-disabilities case-management process. Three interaction algorithms are given: purely symbolic, purely RAG-based, and hybrid. The paper argues that this design yields explainability and process-level correctness, but it explicitly defers empirical evaluation to future work and acknowledges residual risks in the interpretation step.

Significance. If the claimed guarantees were validated, the architecture would be a useful contribution to trustworthy eGovernment chatbots: it cleanly separates open-ended natural-language understanding from closed-world process execution, provides a verifiable execution trace, and is backed by a formal DCR semantics. Strengths include clear pseudo-code (Algorithms 1–3), a concrete REST API description, an openly accessible prototype URL, and an unusually candid limitations section. The paper is best read as an architecture/position paper; as written, it does not provide evidence for the 'accurate' in its title.

major comments (3)
  1. [Section 5.2] No evaluation is reported. Section 5.2 states that evaluation is planned and that the current stage is 'moving beyond proof-of-concept', with synthetic cases, expert validation, and LLM-as-a-judge all deferred. The title claims 'Accurate Conversational Agents', and the conclusion claims the prototype 'demonstrates that the idea is indeed possible in practice', but no data, benchmark, or user study supports these claims. At minimum, either report a small-scale evaluation (e.g., on a set of curated legal scenarios) or explicitly reframe the contribution as an architecture proposal with accuracy as a design goal rather than an established property.
  2. [Section 4.5, Algorithm 3] The paper itself concedes a load-bearing limitation: type-level validation (Algorithm 3, line 29) cannot reject a value that is correctly typed but semantically wrong, so 'the process may reach a legitimate terminal state based on a factually incorrect value if the interpretation step misreads the user's intent'. This means the guaranteed property is only 'process-level correctness relative to the DCR graph', not legal or factual accuracy. The human-in-the-loop edit mechanism mitigates but does not eliminate the risk. To support the title's 'accurate', the authors need either to provide evidence on the interpretation error rate or to weaken the accuracy claim to 'process-level correctness'.
  3. [Sections 3.1–3.2 and 5.1] The correctness of the entire system rests on the DCR graph being a faithful and complete encoding of the applicable law or guideline. The paper shows only small excerpts (five activities in Fig. 1) and points to external URLs for full encodings. Section 5.1 admits that current text-to-graph mapping tools 'completely ignore data', are trained on business-process rather than legal texts, and that DCR graphs become 'difficult to comprehend and manage' beyond 20–30 activities. The two running examples are 11-page and 21-page legal documents, so it is not established that the published graphs correctly capture all data-dependent rules. Unless the full graph encodings are included and validated against official decisions, process-level enforcement alone does not imply legal correctness.
minor comments (4)
  1. [Throughout] There are several typos: 'activites' in §4.1, 'manger' instead of 'manager' in §4.1, 'wether' in §3.1, 'dissabilities' in the caption of Fig. 1, and 'Enqeue' in Algorithm 3. A careful proofreading pass is needed.
  2. [Abstract, §1, and §4.5] The abstract and introduction say the interpreter is an LLM, but §4.5 says the prototype uses 'an NLP endpoint available in the DCR Active Repository’s execution API'. Clarify whether the deployed NLP component is an LLM, a smaller classifier, or an unspecified endpoint, since this affects the generality of the architecture claim.
  3. [Section 5.1] The sentence 'The DCR technology and tools, still have some limitations that need to be addressed' has a misplaced comma and could be rephrased. More substantively, the discussion of DCR graph scalability would benefit from concrete numbers: how many activities do the two example graphs actually contain?
  4. [Section 4.3] Algorithm 1 includes a footnote about deadlock in DCR graphs, which is useful, but the main text could state more explicitly that a deadlocking graph would cause the conversational loop to fail to terminate or to return no conclusion. This connects to the accuracy discussion and should be acknowledged in the hybrid section as well.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's guarantees are formal and model-relative; legal fidelity is an unvalidated assumption, not a circular derivation.

full rationale

The paper does not fit parameters to data and then predict a closely related quantity; it presents an architecture in which an LLM maps free text to typed event-value pairs and a DCR execution engine deterministically updates state and produces a terminal conclusion. The central 'process-level correctness' claim in §4.5 is a formal statement relative to the DCR graph's own semantics: the engine cannot skip an obligation or reach an unpermitted conclusion as defined by that graph. That is an implementation property, not an empirical prediction, and it does not by itself assert legal accuracy. The legal fidelity of the DCR graph is an input assumption, explicitly left unvalidated: §5.1 concedes that current text-to-graph tools ignore data and are trained on business-process text, and §5.2 defers expert validation. These are external-validity limitations, not circularity. Self-citations to the DCR formalism and tooling ([21,23,30,31]) are references to prior formal definitions and engineering artifacts with independent content; the paper invokes no uniqueness theorem and does not smuggle in its conclusion via those citations. No step in the derivation reduces the claimed result to its own inputs, so the appropriate finding is no significant circularity.

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

The central feasibility claim rests on author-developed DCR semantics, an unverified legal encoding, and an NLP interpreter that can misread intent. No free parameters are fitted because the paper reports no quantitative evaluation.

assumptions (3)
  • domain assumption DCR graph formal semantics, as presented in references [21, 22, 23], is a correct executable model of legal processes.
    All process-level correctness claims rest on this formalism, developed in part by the same authors, and no independent formalization or external benchmark is given.
  • domain assumption The DCR graphs encoding the Covid-19 guideline and the study-grant subsections faithfully capture the original legal texts.
    Section 3 shows only small excerpts; the full encodings are external links. Section 5.1 concedes that text-to-graph translation tools are immature and were trained on business process descriptions, not legal texts.
  • domain assumption The NLP interpreter maps free-text user utterances to the correct typed event-value pair with sufficient reliability, given the constrained hypothesis space.
    Section 4.5 explicitly admits that values that are correctly typed but semantically wrong can enter the engine and lead to a legitimate but factually wrong terminal state.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment." pith.science (2026). https://pith.science/paper/MLS3DVTZ

@misc{pith2026260801346,
  author       = {Pith},
  title        = {Pith review of: Hybrid AI for Explainable and Accurate Conversational Agents in eGovernment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MLS3DVTZ}},
  note         = {Machine review of arXiv:2608.01346}
}
read the original abstract

We present a so-called Conversational Hybrid AI (CHAI) architecture for building explainable and accurate conversational agents for eGovernment. We exemplify the architecture with a running prototype of a Covid-19 Chatbot based on a governmental guideline directed to citizens. We also describe an ongoing case on case management for supplementary grants for students with disabilities. We use large language models (LLMs) as a bounded conversational interface to a rule-based (symbolic AI) controller that executes a logical model expressing the provisions and obligations of the law and/or guidelines. As logical modelling language we use Dynamic Condition Response (DCR) graphs, a symbolic declarative process-modeling language developed with the aim to be able to express both deontic, defeasible and temporal logic properties, making it suitable for expressing both the rules of the law and the steps of the legal case management processes.

Figures

Figures reproduced from arXiv: 2608.01346 by the authors.

Figure 1
Figure 1. A DCR Graph coding a sub process of the handling [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Selecting the deadline in the simulation tool. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. After selecting the deadline, the activities [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Rules and activities in law texts can be highlighted by domain-experts and thereby translated to a DCR graph in the [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Example interaction between client and API show [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: The Chatbot prototype applied to the DCR graph for [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

45 extracted references · 31 canonical work pages

  1. [1]

    Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, and Pavlo Molchanov. 2025. Small Lan- guage Models are the Future of Agentic AI. arXiv:2506.02153 [cs.AI] https: //arxiv.org/abs/2506.02153

    arXiv 2025

  2. [2]

    Vaishak Belle. 2025. On the relevance of logic for artificial intelligence, and the promise of neurosymbolic learning.Neurosymbolic Artificial Intelligence1 (2025)

    work page 2025

  3. [3]

    Trevor Bench-Capon, Michał Araszkiewicz, Kevin Ashley, et al. 2012. A History of AI and Law in 50 Papers: 25 Years of the International Conference on AI and Law.Artificial Intelligence and Law20, 3 (2012), 215–319

    work page 2012

  4. [4]

    Krzysztof Budzyń, Marcin Romańczyk, Diana Kitala, Paweł Kołodziej, Marek Bugajski, Hans O Adami, Johannes Blom, Marek Buszkiewicz, Natalie Halvorsen, Cesare Hassan, et al. 2025. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study.The Lancet Gastroenterology & Hepatology(2025)

    2025

  5. [5]

    Ilias Chalkidis and Anders Søgaard. 2026. Brainrot: Deskilling and Addiction are Overlooked AI Risks. InProceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency(Montreal, QC, Canada)(FAccT ’26). Association for Computing Machinery, New York, NY, USA

    work page 2026

  6. [6]

    Axel KF Christfort and Hugo A. López. 2025. DCR-JS: An Online Environment for Declarative Process Mining. InBPM. CEUR-WS, 256–263

    work page 2025

  7. [7]

    María Emilia Cortés-Cediel, Iván Cantador, Julio Viejo-Tardío, and Manuel Pedro Rodríguez Bolívar. 2024. Trends and Challenges of e-Government Chatbots: Advances in Exploring Open Government Data and Citizen Participation Content. Government Information Quarterly41, 1 (2024). doi:10.1016/j.giq.2023.101892

    work page arXiv 2024

  8. [8]

    Hildebrandt, Xixi Lu, Hajo A

    Vlad Paul Cosma, Axel Kjeld Fjelrad Christfort, Thomas T. Hildebrandt, Xixi Lu, Hajo A. Reijers, and Tijs Slaats. 2024. Improving Simplicity by Discovering Nested Groups in Declarative Models. InAdvanced Information Systems Engineering, Giancarlo Guizzardi, Flavia Santoro, Haralambos Mouratidis, and Pnina Soffer (Eds.). Springer Nature Switzerland, Cham, 440–455

    work page 2024

Show all 45 references
  1. [9]

    Matthew Dahl, Varvara Murems, Arjun Krishnan, et al. 2024. Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models.Journal of Legal Analysis(2024). Preprint available at arXiv:2401.01301

    2024 arXiv

  2. [10]

    Artur d’Avila Garcez and Luís C. Lamb. 2023. Neurosymbolic AI: The 3rd Wave. Artificial Intelligence Review56 (2023), 12387–12406. doi:10.1007/s10462-023- 10448-w

    2023 doi

  3. [11]

    DCR Solutions A/S. 2024. DCR Active Repository. https://documentation.dcr. design/documentation/dcr-active-repository/ Accessed: 2026-04-29

    2024

  4. [12]

    Søren Debois and Tijs Slaats. 2024. Large Language Models as User Interface mechanisms for declarative process modelling. InGenerative AI for Process Mining (GenAI4PM) Workshop

    2024

  5. [13]

    Avigail Ferdman. 2025. AI deskilling is a structural problem.AI & SOCIETY (2025), 1–13

    2025

  6. [14]

    Asbjørn Følstad, Marita Skjuve, Ida Kristine Frigaard Haugeland, and Petter Bae Brandtzaeg. 2024. The Impact of Chatbots on Public Service Provision: A Quali- tative Interview Study with Citizens and Public Service Providers.Government Information Quarterly41, 2 (2024), 101925...

    2024

  7. [15]

    Artur d’Avila Garcez and Luis C Lamb. 2023. Neurosymbolic AI: The 3rd Wave. Artificial Intelligence Review(2023)

    2023

  8. [16]

    Michael Gerlich. 2025. AI tools in society: Impacts on cognitive offloading and the future of critical thinking.Societies15, 1 (2025), 6

    2025

  9. [17]

    Hui Wen Goh and Jonas Mueller. 2025. Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction.arXiv preprint arXiv:2603.18014 (2025). https://arxiv.org/abs/2603.18014

    2025

  10. [18]

    Grohs, V

    M. Grohs, V. Fionda, et al. 2025. Are Large Language Models Fluent in Declarative Process Mining?. InProceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI-25)

    2025

  11. [19]

    Object Management Group. 2020. Decision Model and Notation V 1.3. (2020). https://www.omg.org/spec/DMN/1.3/

    2020

  12. [20]

    {Simon V.H.} Hermansen, Ragnar Jónsson, {Jonas L.} Kjeldsen, Tijs Slaats, {Vlad Paul} Cosma, and {Hugo A.} López. 2024. DCR4Py: A PM4Py Library Extension for Declarative Process Mining in Python. InICPM Demo

    2024

  13. [21]

    Thomas T Hildebrandt and Raghava Rao Mukkamala. 2011. Declarative event- based workflow as distributed dynamic condition response graphs.arXiv preprint arXiv:1110.4161(2011)

    2011 arXiv

  14. [22]

    Hildebrandt, Raghava Rao Mukkamala, Tijs Slaats, and Francesco Zanitti

    Thomas T. Hildebrandt, Raghava Rao Mukkamala, Tijs Slaats, and Francesco Zanitti. 2013. Contracts for cross-organizational workflows as timed Dynamic Condition Response Graphs.J. Log. Algebr. Program.82, 5-7 (2013), 164–185

    2013

  15. [23]

    Thomas T Hildebrandt, Håkon Normann, Morten Marquard, Søren Debois, and Tijs Slaats. 2021. Decision modelling in timed dynamic condition response graphs with data. InInternational Conference on Business Process Management. Springer

    2021

  16. [24]

    Daniel Martin Katz, Michael James Bommarito, Shang Gao, and Pablo Arredondo

  17. [25]

    Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, and Pattie Maes. 2025. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv:2506.08872 [cs.AI]

    2025 arXiv

  18. [26]

    Stanford Legal Design Lab. 2025. Auditability in Legal AI: A Design Perspective. InProceedings of the 2025 Conference on AI and Law (ICAIL)

    2025

  19. [27]

    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. 2021. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.o...

    2021 arXiv

  20. [28]

    Jonas Lindner and Hugo A. López. 2025. Discovering Declarative Processes in Textual Descriptions using Large Language Models. InThe Second International Workshop on Generative AI for Process Mining. Springer

    2025

  21. [29]

    Hugo A López, BingKun Feng, Jonas Lindner, Marco Franceschetti, and Amine Abbad-Andaloussi. 2025. Ambiguity Detection in Business Process Descriptions: An Evidence and an Automated Approach. InInternational Conference on Business Process Management. Springer, 379–396

    2025

  22. [30]

    López, Thomas Hildebrandt, Søren Debois, and Morten Marquard

    Hugo A. López, Thomas Hildebrandt, Søren Debois, and Morten Marquard. 2018. The Process Highlighter: From Texts to Declarative Processes and Back.CEUR Workshop Proceedings, 66–70. Joint Proceedings of the BIR 2018 Short Papers, Workshops and Doctoral Consortium co-located with...

    2018

  23. [31]

    Hugo A López, Morten Marquard, Lukas Muttenthaler, and Rasmus Strømsted

  24. [32]

    Man- ning, and Daniel E

    Varun Magesh, Faiz Surani, Matthew Dahl, Mirac Suzgun, Christopher D. Man- ning, and Daniel E. Ho. 2025. Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools.Journal of Empirical Legal Studies22, 2 (2025), 216–242. arXiv:https://onlinelibrary.wiley....

    2025 doi

  25. [33]

    Morten Marquard and Tijs Slaats. 2017. From Law to DCR Graphs: A Case Study in Digitizing Public Administration. InBusiness Process Management Workshops. Springer

    2017

  26. [34]

    Martínez

    E. Martínez. 2025. Re-evaluating GPT-4’s bar exam performance.Artificial Intelligence and Law33 (2025), 581–604. doi.org/10.1007/s10506-024-09396-9

    2025 doi

  27. [35]

    Donald Nute. 2003. Defeasible Logic. InWeb Knowledge Management and Decision Support, Oskar Bartenstein, Ulrich Geske, Markus Hannebauer, and Osamu Yoshie (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 151–169

    2003

  28. [36]

    2013.Business Process Model and Notation (BPMN), Version 2.0.2

    Object Management Group. 2013.Business Process Model and Notation (BPMN), Version 2.0.2. Technical Report. Object Management Group. https://www.omg. org/spec/BPMN/2.0.2 Formal Version: 2013-12-09. Explainable and Accurate Conversational Agents for eGovernment AIDA2J at ICAIL ’...

    2013

  29. [37]

    2024.GPT-4 Technical Report

    OpenAI. 2024.GPT-4 Technical Report. Technical Report. OpenAI. https: //arxiv.org/abs/2303.08774

    2024 arXiv

  30. [38]

    Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang. 2023. Logic- LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning. InFindings of the Association for Computational Linguistics: EMNLP 2023. 3806–3824

    2023

  31. [39]

    Evan F Risko and Sam J Gilbert. 2016. Cognitive offloading.Trends in cognitive sciences20, 9 (2016), 676–688

    2016

  32. [40]

    Asai, et al

    Ken Satoh, K. Asai, et al. 2010. Logic Programming for Legal reasoning. InLogic Programming, 26th International Conference

    2010

  33. [41]

    2019.Online Courts and the Future of Justice

    Richard Susskind. 2019.Online Courts and the Future of Justice. Oxford University Press

    2019

  34. [42]

    Imperial College London Team. 2024. LawGiBa: Combining GPT, Knowledge Bases, and Logic Programming in a Legal Assistance System. InProceedings of the 2024 International Conference on Legal Knowledge and Information Systems

    2024

  35. [43]

    S M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Usman Bhatt, and Asif Ekbal. 2024. A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models.arXiv:2401.01313(2024)

    2024 arXiv

  36. [2019]

    In2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW)

    Assisted declarative process creation from natural language descriptions. In2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE, 96–99

  37. [2024]

    GPT-4 Passes the Bar Exam.SSRN Electronic Journal(2024)

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.