Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation

Elizabeth Chason; Erin Wilson; Kristin Mullen; Meiyi Ma; Noah Mladenovski; Stephen Martini; Zirong Chen

REVIEW 4 major objections 5 minor 1 cited by

Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read Sim911 claims that an LLM playing the 9-1-1 caller produces realistic, authentic, and equitable training calls that match human-led role-play.

desk verdict A genuinely deployed LLM dispatcher-training simulation with real use hours, but the headline alignment metric is circular and needs an independent human-labeled evaluation. read the letter →

arxiv 2412.16844 v3 pith:2PLV6VYK submitted 2024-12-22 cs.CL cs.AI

Zirong Chen , Elizabeth Chason , Noah Mladenovski , Erin Wilson , Kristin Mullen , Stephen Martini , Meiyi Ma This is my paper · ORCID

classification cs.CLcs.AI

keywords LLMsimulation9-1-1dispatchertrainingretrieval-augmentedgenerationcontrolledtextcallerimagetaggingvalidationloopemergencycommunicationsequity

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

Sim911 is a training simulator in which a large language model plays the role of the 9-1-1 caller and interacts with a trainee dispatcher. The paper tries to establish that this LLM-powered simulation is realistic, authentic, and equitable enough to serve as a training tool, and that it can do so more scalably than traditional human role-play. On controlled runtimes built from archived calls, the full system reaches a perplexity of 11.07, a METEOR score of 0.85, a geocoding success rate of 99.19%, and a simulation alignment rate of 98.42%, and every ablation degrades these numbers. A user study of ten participants reported average scores of 4.50 for realism, 4.70 for authenticity, and 4.89 for training helpfulness, with nine of ten saying it matched or exceeded human-led training. If those results hold, Sim911 would let emergency communications centers train more dispatchers on a wider range of incident types and caller profiles while freeing experienced staff from role-play duties.

What carries the argument

The carrying mechanism is a three-stage loop. Knowledge construction converts archived call recordings, transcripts, and computer-aided dispatch logs into two knowledge bases: a static factual base with validated local addresses, map connectivity, and tree-structured emergency protocols, plus a retrievable base whose entries are tagged by incident specifications and caller images. Context-aware controlled generation then assembles each simulation prompt in three steps: vector-base incorporation pulls matching archived call pieces into the prompt through retrieval-augmented generation, detailed task explanation uses chain-of-thought prompting to lay out the scenario step by step, and caller-image deciphering adds few-shot examples from past callers with the same tags. Finally, validation with looped correction runs a format check, a BERT-based alignment check (the classifier confirms the incident type matches the requested scenario), a RoBERTa-based factual check on the address, and a human-in-the-loop feedback step, regenerating the response up to three times before accepting the best available one.

What would settle it

Have blinded expert dispatchers rate a set of Sim911-generated calls and a set of real 9-1-1 calls from the same jurisdiction on realism; if the experts rate the real calls substantially higher while the paper's script-based metrics stay high for Sim911, those metrics would be shown to reward script-following rather than real conversational behavior.

Watch

Extended reading notes

Core claim

The paper's central claim is that Sim911, built on 11,841 archived 9-1-1 calls in a metropolitan emergency communications department, generates caller dialogues that are realistic, authentic, and equitable enough for dispatcher training. With knowledge construction, context-aware controlled generation, and validation with looped correction all active, the generated calls score better on every measured realism and authenticity metric than any ablated configuration, including a plain LLM backend with all components disabled. The system covers 57 incident types and 14 caller tags, and its equity evaluation shows accurate caller-image matching across age, emotion, unhoused, mental health, non-native speaker, and low-income housing tags. The authors read the deployment logs and user study as evidence that the simulator saved 26.55 hours of active training time and was rated at least as effective as human-led training by 90.00% of the ten participants.

Load-bearing premise

The load-bearing assumption is that the rule-based scripts used as reference dialogues faithfully represent real 9-1-1 call-taking, because if those scripts are simpler or less varied than actual calls, the reported perplexity, METEOR, and alignment scores would not measure true conversational realism.

Editorial extensions

If this is right

Emergency communications centers with staffing shortages could let each trainee practice individually on 57 incident types and 14 caller profiles without pulling experienced dispatchers out of live operations.
Trainees would encounter rare and high-stakes scenarios such as mental-health crises, unhoused callers, and non-native speakers, categories that appeared in only about 12% of the reviewed conventional training scenarios.
The estimated 84 staff-hours that a traditional training session costs in experienced-dispatcher time could be redirected to live emergency duties, since the simulator runs without a human caller and instructor.
The component ablations indicate that knowledge construction and retrieval-augmented generation are the largest contributors to realism, so any deployment that skips those pieces would sacrifice most of the benefit.

Reading between the lines

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

Beyond the paper, the same three-stage pattern—archival knowledge bases, retrieval-augmented prompt control, and a validation loop with regeneration—appears transferable to other scripted high-stakes call-taking roles such as poison control, suicide hotlines, and medical triage, where procedural fidelity and caller diversity matter.
Beyond the paper, the decision to hide vulnerable-group tags during runtime makes a testable behavioral prediction: comparing dispatcher responses to the same scenario with the tag visible versus hidden would reveal whether the hiding mechanism changes question wording and empathy in measurable ways.
Beyond the paper, the regeneration threshold of three sets up a cost-quality frontier that the deployment metrics do not yet quantify; measuring per-trainee cost and latency at thresholds one through five would tell a center how much realism each extra regeneration buys.
Beyond the paper, the ten-participant user study makes the 90% parity claim a pilot signal; a multi-site evaluation with follow-up on real call-handling performance would be the natural way to test whether simulation performance transfers to the job.

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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

The thing to know: Sim911 is a real, deployed LLM-based simulation for 9-1-1 dispatcher training, and the deployment evidence is genuinely useful. The thing to watch: the headline authenticity metric (SAR) is circular—the same BERT classifier that filters responses during validation also scores them—so the 98.42% alignment number is largely a pass-rate measurement, not an independent measure of realism or alignment.

What's actually new is the system integration: knowledge construction from 11,841 archived calls into incident-spec and caller-image tags, RAG/CoT/FSP prompting, and a validation loop with format/alignment/factual/human checks. That's a sensible engineering contribution, and the motivating study (only 48% incident coverage in conventional training, 12% vulnerable-population scenarios) is a solid empirical argument for the problem. Real deployment at MNDEC with 26.55 hours of active simulation and integration into four training classes is more than most applied NLP papers manage. The user study is tiny (n=10) but the participants are the actual training officers and dispatchers, so the qualitative feedback carries some weight.

The soft spots are where the evaluation stops being persuasive. First, SAR is defined as 'the copilot's results' (Section 4.1), and the copilot is the same BERT-based alignment check that discards misaligned responses before they reach the trainee (Section 3.3). So the high SAR in Table 1 is in large part guaranteed by construction; the Sim911¬VLC comparison is scored with that same classifier, so it doesn't rescue independence. You need a held-out human-annotated alignment sample. Second, the realism reference set is rule-based scripts mimicking call-taker questions, and the paper explicitly excludes 'granular discrepancies' from analysis—that inflates METEOR and perplexity relative to actual conversational realism. Third, GMap success (99.19%) is unsurprising when the system's factual base contains only validated addresses; it's a check that the RAG fetch stuck to the list, not that the system knows real geography. Fourth, no code or data is released yet; the appendix promises de-identified samples upon acceptance.

These aren't fatal to the system as a training tool. The deployment hours and the user perception data are real evidence that something useful is happening. But the paper over-claims what the quantitative metrics establish. The 'effectiveness and equity' conclusion rests on measurement instruments that need external validation.

Who should read it: people building LLM-based simulation for high-stakes training, and anyone interested in evaluation pitfalls for closed-loop generation systems. It deserves a serious referee—the application is important and the flaws are fixable with independent annotation, a better reference set, and artifact release. I'd send it to review, but I'd push hard on the SAR circularity and the reference construction before publication.

Referee Report

4 major / 5 minor

Summary. Sim911 is an LLM-based simulation system for training 9-1-1 dispatchers, built around three components: knowledge construction from archived call data, context-aware controlled generation (RAG, CoT, few-shot prompting), and validation with looped correction (VLC) that filters low-quality responses. The paper reports component-wise and system-level evaluations in terms of realism (PPL, METEOR, TTR), authenticity (GMap locating success and SAR alignment rate), equity (BART/margin scores across caller-image tags), and a user study. The central claims are that the full system achieves the best scores on all metrics and that 90% of user-study participants found it as effective as or superior to traditional human-led training.

Significance. If the evaluation is valid, Sim911 is a valuable applied contribution: it is deployed in a real emergency communications center, supports 57 incident types and 14 caller tags, incorporates equity-focused caller images, and quantifies training-time savings. The paper also makes a methodical attempt to ablate each component. However, the evaluation of authenticity is compromised by circularity: the primary alignment metric (SAR) is computed with the same classifier that serves as the VLC filter, so the reported 98.42% alignment is largely a measure of the filter's pass rate. The realism evaluation also compares against rule-based scripted references and excludes the granular discrepancies that matter for realism. These issues are load-bearing because the abstract and conclusions rest on the reported realism and authenticity numbers.

major comments (4)

[Section 4.1, Table 1] The Simulation Alignment Rate (SAR) is defined as 'the copilot's results' (Section 4.1), and the copilot is the BERT-based classifier that implements the alignment check in VLC (Section 3.3, Algorithm 2). In the full system, responses that fail this check are discarded and regenerated until they pass or the loop threshold is reached, so SAR is effectively the filter's pass rate. The comparison with Sim911¬VLC does not resolve the circularity because both conditions are scored with the same classifier that only filters in the full system. An independent evaluation needs human-annotated alignment labels or a separately trained classifier that was not used during generation. Without this, the authenticity pillar of the central effectiveness/equity claim is not established.
[Section 4, component-wise analysis] The realism evaluation compares Sim911's generated speech to reference dialogues produced by 'rule-based scripts that mimic the questions typically asked by call-takers,' and then 'we exclude these granular discrepancies from our analysis.' This makes METEOR=0.85 and PPL=11.07 measures of similarity to a synthetic script, not to actual 9-1-1 call behavior, and the exclusion of the very details (e.g., license plates, window tint) that distinguish realistic from hallucinated content can inflate the scores. The paper should evaluate against a sample of real archived call transcripts or at least report results without excluding these discrepancies, and it should justify that the rule-based scripts are representative of real call-taking behavior.
[Section 4.1, realism metrics] Perplexity is reported as a realism metric, but the paper does not specify which language model computes the perplexity. Perplexity is a model-dependent measure of predictive likelihood; without naming the evaluator model and the tokenization, the absolute PPL values in Table 1 are not interpretable, and the ablation ranking could be an artifact of the evaluator model. I recommend reporting the evaluator model explicitly and, ideally, supplementing PPL with human realism judgments on a held-out sample.
[Section 4.3, user study] The headline claim that 90.00% of participants found Sim911 as effective as or superior to human-led training is based on 10 participants, of whom 9 gave that response. The survey was administered by MNDEC, and participants include personnel from the deploying agency, which introduces potential bias. The study also measures subjective self-report rather than learning outcomes or call-handling performance. I recommend framing this as a preliminary deployment study with appropriate caveats, and ideally supplementing it with a larger sample or an objective performance measure before drawing strong conclusions about effectiveness.

minor comments (5)

[Appendix E] The heading 'Secion' should be 'Section'.
[Table 2, Gunning Fog column] The Gunning Fog column for Non-Native Speakers is labeled 'Gunning Fog↑' with values around 0.17, but the appendix defines Gunning Fog as a readability index, not a margin score. Please explain how the margin score is derived from Gunning Fog and in which direction higher values indicate better performance.
[Table 1, condition labels] The condition 'Sim911¬All(GPT-4o)' is described in the text as 'when all components are disabled,' but the label could be misread as disabling all components except GPT-4o. Consider a more explicit label such as 'No components (GPT-4o only)'.
[Section 3.1, annotation procedure] The knowledge construction relies on manual annotation of 11,841 calls, but the paper does not report the number of annotators or inter-annotator agreement. Since the tags are used both for generation and for equity evaluation, a reliability measure would strengthen confidence in the tag-based metrics.
[Appendix A, limitations] The future-work section acknowledges hallucination but does not quantify its frequency or severity in the current system. Reporting hallucination rates on a small human-annotated sample would provide useful context for the authenticity measurements.

Circularity Check

1 steps flagged · score 6.0 of 10

SAR authenticity metric is circular: the copilot used to score Simulation Alignment Rate is the same BERT alignment filter that VLC uses to discard misaligned responses, so the 98.42% SAR is largely a pass-rate of the system's own filter.

self definitional [Section 4.1 (SAR definition) and Section 3.3 / Algorithm 2 (VLC Alignment check)]
"To measure simulation alignment, we use the copilot's results to determine if the indicated Incident Specification ( IS) aligns with the one provided in the simulation instructions. ... if ˆIS does not match IS then Discard R, continue ▷ ALIGNMENT CHECK"

SAR is defined as the copilot's determination of whether the generated response's incident specification matches the instruction. The same copilot (a BERT-based classifier) is the Alignment check inside Validation with Looped Correction: Algorithm 2 discards any response whose predicted IS does not match the instruction and regenerates it until the threshold of 3 is reached. Therefore the responses delivered by the full Sim911 system have already been filtered by the exact criterion that SAR measures; SAR=98.42% is the pass-rate of the system's own alignment filter. It cannot serve as an independent authenticity measurement. The Sim911¬VLC comparison (89.48%) is scored with the same classifier, so both conditions rely on the same unvalidated measure of 'alignment.'

full rationale

The central realism claims (perplexity, METEOR, TTR) and the user study are computed against external references or human feedback, so they are not circular. However, the authenticity pillar's Simulation Alignment Rate (SAR) is defined through 'the copilot's results,' and the same copilot is the VLC alignment filter that discards responses before they reach the trainee. The reported 98.42% SAR therefore reduces, by construction, to the pass-rate of the system's own BERT-based filter: the system only emits responses that the filter has already accepted. This makes the headline 'optimal authenticity' at least partially forced by the system's own validation loop, rather than independently established. The GMap and user-study results provide some independent support, so the circularity is partial, not total. An independent human-labeled alignment sample or a separately trained/evaluated classifier would be needed to validate the SAR-based authenticity claim.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claims of realism and authenticity rest on several unvalidated assumptions: the representativeness of the archived call data, the fidelity of the rule-based evaluation references, and the reliability of the in-house BERT/RoBERTa classifiers that serve as both filters and evaluators. The system has one hand-tuned parameter (loop threshold T=3) and no independently verified invented entities.

free parameters (1)

Loop threshold T = 3
Chosen to balance validation capture rate (98.47%) and latency (61 ms/token) during deployment; higher thresholds (4,5) give similar capture rate but more latency, lower (1,2) give lower capture rate. This is a hand-tuned system parameter, not derived from first principles.

assumptions (5)

domain assumption The archived 9-1-1 call recordings and their expert annotations are representative of the real-world call distribution, including the diversity of caller images and incident types.
The entire knowledge construction and evaluation depend on this representativeness; the paper does not validate the sampling or annotation against an external benchmark.
domain assumption The rule-based scripts that mimic call-taker questions are a faithful reference for realistic 9-1-1 calls.
Used in component-wise evaluation to compute METEOR and perplexity references; if scripts are simplistic, the realism metrics are not meaningful.
domain assumption The BERT-based alignment classifier and RoBERTa-based question answering are reliable enough to serve both as validation filters and as evaluation measures.
The paper does not provide accuracy or calibration of these classifiers against human judgment; their use in evaluation is therefore not independently validated.
ad hoc to paper Perplexity is an appropriate measure of dialogue realism for this domain.
Perplexity measures model confidence, not human-perceived realism; its use as the primary realism metric is not standard for open-ended dialogue and may not correlate with training quality.
domain assumption GPT-4o's behavior is stable and consistent for simulation purposes across sessions.
The system relies on a third-party API; the paper does not address stochasticity or version drift, though these affect reproducibility.

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Cite this review

Pith. "Pith review of Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation." pith.science (2026). https://pith.science/paper/2PLV6VYK

@misc{pith2026241216844,
  author       = {Pith},
  title        = {Pith review of: Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2PLV6VYK}},
  note         = {Machine review of arXiv:2412.16844}
}

read the original abstract

Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training methods, which rely on role-playing by experienced personnel, are labor-intensive, time-consuming, and often neglect the specific needs of underserved communities. To address these challenges, we introduce Sim911, the first training simulation for 9-1-1 dispatchers powered by Large Language Models (LLMs). Sim911 enhances training through three key technical innovations: (1) knowledge construction, which utilizes archived 9-1-1 call data to generate simulations that closely mirror real-world scenarios; (2) context-aware controlled generation, which employs dynamic prompts and vector bases to ensure that LLM behavior aligns with training objectives; and (3) validation with looped correction, which filters out low-quality responses and refines the system performance.

Figures

Figures reproduced from arXiv: 2412.16844 by the authors.

**Figure 1.** Year-round Distribution of 9-1-1 Calls and Vulnerability Index in Nashville, 2023. directed by the instructor and an experienced dispatcher. Each trainee typically participates in 60 independent simulated calls, with each call requiring the involvement of three participants. Based on past recordings, the average call duration is 3.5 minutes; with an average of 12 trainees per session, the total time commitment fo… view at source ↗

**Figure 2.** An Overview of Sim911’s Key Components: knowledge construction (KC), context-aware controlled generation (CaCG), and validation with looped correction (VLC). KC integrates real-world data into knowledge bases before runtime. CaCG tailors LLM behaviors during runtime. VLC involves in-context validation during runtime and periodic correction after runtime. struction phase, 11,841 calls are analyzed to develop knowled… view at source ↗

**Figure 3.** An Example of the 3-step Context-aware Controlled Generation with Vector Base Incorporation, Detailed Task Explanation, and Caller Image Deciphering. The incident type specification (IS) comes with tags crash report (incident type), medical emergency(special request), and severe weather (scenario contexts), and the caller image (CI) is set to be an unhoused non-native speaker adult. Factual Bases. First, we build a … view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Examples on Knowledge Construction process – introducing tags to existing calls. Threshold Adjustment and Backend Selection The threshold defines how often Sim911 regenerates responses when failing checks (format, alignment, factual), before being reviewed by human su…

**Figure 5.** Figure 5: Conventional Training Pieces. 911 caller: At 411 Murfreesboro Pike, Apartment 302. 911 call-taker: Okay, you said 411 Murfreesboro Pike in South City X, apartment 302. 911 call-taker: Okay, is there a building letter or anything with this? 911 call-taker: Okay, is this…

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Reference graph

Works this paper leans on

24 extracted references · 20 canonical work pages · cited by 1 Pith paper

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Matching: Tokens from the candidate translation are matched to tokens in the reference translation using ex- act matches, stem matches, synonym matches, and para- phrase matches

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P = m |c| (3) R = m |r| (4) where: • m is the number of matched tokens

Precision and Recall: The precision (P ) and recall ( R) are calculated based on the matches. P = m |c| (3) R = m |r| (4) where: • m is the number of matched tokens. • |c| is the total number of tokens in the candidate transla- tion. • |r| is the total number of tokens in the reference transla- tion. Harmonic Mean and Penalty . The harmonic mean of precis...

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Compute TTR: Divide the number of types by the num- ber of tokens. Given a text sample, the TTR can be computed as follows: TTR = Number of Unique Words (Types) Total Number of Words (Tokens) (13) Interpreting TTR Scores . TTR scores provide insight into the lexical diversity ...
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Extract Syntactic Structures: Extract the syntactic structures from the parse trees, focusing on key elements such as noun phrases (NP), verb phrases (VP), and other grammatical constituents
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Compare Structures: Compare the syntactic structures of the sentences to identify overlaps and differences. This can be done by matching corresponding subtrees or syn- tactic patterns between the parse trees. Detailed Steps. Step 1: Parse the Sentences. Given two sentences S1 ...
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Compute TF-iDF Scores: Calculate the TF-iDF scores for all terms in the documents
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Identify Significant Terms: Identify the significant terms in each document based on their TF-iDF scores
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Step 1: Compute TF-iDF Scores Given a set of documentsD = {d1, d2,

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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Tokenization: Both the candidate translation and refer- ence translations are tokenized into words

work page

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Matching: Tokens from the candidate translation are matched to tokens in the reference translation using ex- act matches, stem matches, synonym matches, and para- phrase matches

work page

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arXiv preprint arXiv:1907.11692

Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint arXiv:1907.11692. Loria, S. 2018. textblob Documentation. Release 0.15, 2. Ma, M.; Preum, S. M.; Ahmed, M. Y .; T¨arneberg, W.; Hen- dawi, A.; and Stankovic, J. A. 2019. Data sets, modeling, and decision making in smart cities: A survey. ACM Trans- actions on Cyber-Physical Systems,...

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In International Conference on Learning Representa- tions

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[5] [7]

P = m |c| (3) R = m |r| (4) where: • m is the number of matched tokens

Precision and Recall: The precision (P ) and recall ( R) are calculated based on the matches. P = m |c| (3) R = m |r| (4) where: • m is the number of matched tokens. • |c| is the total number of tokens in the candidate transla- tion. • |r| is the total number of tokens in the reference transla- tion. Harmonic Mean and Penalty . The harmonic mean of precis...

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Log-Likelihood: Calculate the log-likelihood of each word in the sequence given the previous words

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Average Log-Likelihood:Compute the average of these log-likelihoods over the entire sequence

work page

[8] [10]

Given a language model that assigns a probability P (wi | w1, w2,

Exponentiation: Exponentiate the negative average log- likelihood to obtain the perplexity. Given a language model that assigns a probability P (wi | w1, w2, . . . , wi−1) to each word wi in the sequence, the per- plexity is calculated as: P P= exp − 1 N NX i=1 log P (wi | w1, w2, . . . , wi−1)) (9) Alternatively, if the cross-entropy H of the model on th...

work page