Pith. sign in

REVIEW 4 major objections 6 minor 7 references

Your Autoregressive Model Already Reveals the Causal Graph

T0 review · 4 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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

Pith's one-line read A next-token predictor can be repurposed as a causal-discovery engine that recovers the causal graph of a single event sequence.

desk verdict Novel combination, broken central claim: the 'do-operator' isn't an intervention, the oracle assumption doesn't cover the contexts used, and the threshold is tuned on ground truth. read the letter →

arxiv 2602.01135 v4 pith:USBF6IVM submitted 2026-02-01 cs.LG

Hugo Math , Rainer Lienhart This is my paper · ORCID
classification cs.LG
keywords causaldiscoveryautoregressivemodelsconditionalmutualinformationeventsequencesgraphinstance-levelcausalityindependencetestingscalable
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 tries to establish that an autoregressive model trained by plain next-token prediction can be reused, without retraining, as a conditional-mutual-information estimator, and that the standard cross-entropy training loss directly bounds the error of the causal graph it recovers. This matters because causal discovery within a single long event stream—vehicle logs, patient trajectories, manufacturing traces—has been seen as needing repeated samples or multivariate time series; TRACE claims to work from one sequence over an enormous vocabulary. A sympathetic reader would take away that causal structure is already latent in a good forecaster, and that structural identifiability survives imperfect models down to a noise floor rather than requiring oracle-perfect density estimation.

What carries the argument

The load-bearing object is the conditional mutual information I(E_t'; E_t | X_{<t}) estimated through the pretrained autoregressive model P_theta, with a Monte-Carlo estimator over sampled histories. To handle delayed effects, TRACE defines a 'randomized interventional do-operator' that averages the model's conditional probabilities over random replacements of the intermediate events, and it uses epsilon-Strong Faithfulness to place a detection threshold above the estimator's noise floor. The parallel GPU implementation builds a staircase tensor so all pairwise conditional-independence tests are evaluated in a small number of forward passes.

What would settle it

On a synthetic chain X→M→Y with no direct X→Y edge, run TRACE's lagged test with randomized mediators; if it reports X→Y, the do-operator is not blocking the indirect path. Conversely, on X→Y with a strong X→M link, the test should still flag X→Y; failure would show the procedure measures mediation rather than direct effect.

Watch

Extended reading notes

Core claim

On the paper's own terms, TRACE recovers both the instance-level time causal graph (a DAG over the time steps of an observed sequence) and its summary projection over event types, by estimating conditional mutual information with a frozen autoregressive model. The central theoretical result is Theorem 5.1: under temporal precedence, causal sufficiency, and an epsilon-Strong Faithfulness condition, the algorithm recovers the correct instance time causal graph asymptotically as the number of Monte-Carlo particles grows; Theorem 4.3 gives an explicit bound showing that as the model's KL divergence epsilon goes to zero, the causal identification error vanishes. The empirical claim is that this w

Load-bearing premise

The whole proof leans on assuming that averaging the model's predictions over random replacements of the intermediate events truly removes the causal influence of the earlier event—a randomized-conditioning step that the paper labels an intervention but does not justify as one.

Editorial extensions

If this is right

  • Any pretrained next-token forecaster can be converted into a causal discovery engine for the process it models, with no task-specific retraining.
  • Causal structure is identifiable before the autoregressive model fully converges: the paper reports a phase transition around epsilon ≈ 0.1, with precision staying high even as recall degrades.
  • The method scales linearly with vocabulary size and memory, making tens of thousands of event types tractable—regimes where constraint-based, Granger, Hawkes, and attention-based methods fail.
  • Because the training loss bounds the identification error, better density estimation directly translates into a tighter upper bound on structural error.
  • The recovered summary graph is allowed to contain cycles, since type-level cycles are projections of an acyclic unfolded time graph.

Reading between the lines

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

  • If TRACE's core premise holds, event-sequence causal discovery becomes a byproduct of ordinary language-model pretraining: production systems that already predict logs could expose their causal structure on demand, without a dedicated discovery pipeline.
  • The empirically reported threshold scaling τ ≈ C/|X| suggests a testable direction: in higher-entropy vocabularies, spurious correlations dilute, potentially making weaker causal signals detectable—a claim the paper observes but does not fully explain.
  • A natural next step the paper does not take is applying the same CMI estimator to interleaved multi-stream data; the single-stream framing is central to the argument, and whether the dual-use forecaster result transfers to cross-stream causes is unsettled.
  • The randomized do-operator is the most exposed step: if a chain X→M→Y with no direct X→Y edge is misflagged as containing X→Y, the lagged-edge test would need a corrected mediation adjustment.
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

4 major / 6 minor

Summary. The paper proposes TRACE, a framework that repurposes a pretrained autoregressive model as a conditional mutual information (CMI) estimator for causal discovery from a single observed sequence of discrete events. It defines an instance-level time causal graph and its summary projection, and uses the model's next-token probabilities to perform parallel CMI-based conditional independence tests. To handle delayed effects, it introduces a 'randomized interventional do-operator' that replaces intermediate mediator tokens with samples from a proposal distribution. The paper claims theoretical guarantees: Theorem 4.3 bounds the CMI estimation error in terms of the model's KL divergence, and Theorem 5.1 states that TRACE recovers the correct instance time causal graph asymptotically under causal sufficiency, temporal precedence, and ε-strong faithfulness. Experiments on synthetic linear SCMs and on real vehicle diagnostic logs report large F1 improvements over several baselines.

Significance. If the central claims were sound, the paper would offer a substantial practical advance: amortized causal discovery in a single high-dimensional event sequence, with linear vocabulary scaling and no task-specific retraining. The parallel GPU formulation and the scale of the empirical study (vocabularies up to 29,100) are notable and the code is stated to be available. However, the paper's theoretical foundation has load-bearing gaps. The proposed 'do-operator' is not shown to correspond to a Pearl intervention, the ε-oracle assumption does not cover the out-of-distribution contexts the method requires, the threshold used in experiments is tuned on ground truth rather than derived from the theory, and the proof of the main theorem does not match the algorithm it claims to justify. As a result, the paper's central assertion that TRACE recovers the causal graph is not established.

major comments (4)
  1. [Definition 4.6 and Eqs. (9)–(10)] The 'Randomized Interventional Do-Operator' is not a Pearl do-operator. Eq. (9) defines P(E_t' | do(M~Q), X<t) as E_{M~Q} P(E_t' | M, X<t). Averaging observational model conditionals over a proposal distribution is at best a stochastic intervention if (i) P_θ equals the true conditional and (ii) X<t is a valid backdoor set relative to M → E_t'; neither condition is established for the model output. More importantly, Eq. (10) compares this quantity with the same expression conditioned on e_t, which is an observational conditioning on E_t, not an intervention do(E_t=e_t). The resulting KL divergence is therefore not a controlled direct effect, and no theorem in Appendix B shows that it is zero exactly when the direct edge E_t → E_t' is absent. Theorem 5.1's lagged-edge recovery claim is thus unsupported.
  2. [Assumption 3.6 and Theorem 4.3 vs. Definition 4.6] The ε-oracle assumption bounds D_KL(P(X_t|X<t) || P_θ(X_t|X<t)) for histories drawn from the training distribution. Definition 4.6 requires evaluating P_θ on contexts where the mediator block M is sampled uniformly from the vocabulary, which is far outside the support of natural event sequences. The error bound in Theorem 4.3 does not control model error at such counterfactual contexts, and the proof of Theorem 5.1 in Appendix B.4 does not address this. Consequently, even a perfect fit on the training distribution gives no guarantee for the randomized mediator inputs that are essential to the lagged-effect test.
  3. [Section 6.2, Fig. 9, Fig. 10, Appendix D.1.2] The threshold τ is load-bearing: Lemma 4.5 and Definition 4.4 define τϵ as a theoretical noise floor, and the main results depend on it. In the experiments, Fig. 9 selects τ_opt by maximizing F1 on ground truth, and Fig. 10 fits τ = C/|X| to the same family of synthetic data. Table 2 uses τ = 3×10^{-5} without deriving it from Theorem 4.3. This is circular when evaluating the method: the reported F1 scores measure the performance of TRACE with an oracle-selected threshold, not the parameter-free claims of the theory. The paper itself states in Appendix D.1.2 that the theoretical bound is orders of magnitude below the operating τ, so the theoretical bound does not justify the experimental threshold.
  4. [Theorem 5.1 proof (Appendix B.4) vs. Section 5] The proof of Theorem 5.1 does not use the lagged-effect test. The induction in Appendix B.4 only applies the CMI decision rule with conditioning on the full history; it never invokes Definition 4.8 or shows how mediator randomization distinguishes direct from mediated edges. The algorithm in Section 5, by contrast, uses truncated contexts, a sparse memory bound, and the lagged information gain. The soundness theorem therefore proves a different claim from the method it supposedly validates. In addition, Section 5.1 contains an unresolved 'Assumption ??' placeholder for the memory parameter m, indicating an incomplete formal description.
minor comments (6)
  1. [Section 5.1] The memory horizon m is referenced as 'Assumption ??'; the assumption is missing. This should be stated explicitly.
  2. [Eq. (7) and Section 5.1] Eq. (7) defines the estimator by sampling histories x^{(l)}_{<t} from P_θ, but the algorithm in Section 5 uses the single observed sequence and samples only mediator noise. These are different estimators; the text should reconcile the notation.
  3. [Figures 6 and 11] Figures 6 and 11 appear to be the same pair of graphs reprinted; one should be removed or the captions clarified.
  4. [Section 6.1] The ground-truth definition uses 'atomic interventions by uniformly randomizing E_t' and threshold τ > 0.05. This is a different τ from the τ used elsewhere; the relation between the two should be clarified.
  5. [Title of Theorem 4.3 / proof] The proof of Theorem 4.3 swaps the roles of the two arguments in the conditional entropy difference: Term B is written as H(E_t | E_t', X<t), but the autoregressive model is trained for next-token prediction and cannot directly supply a conditional distribution over a past event given a future event. This needs a more careful derivation or a restriction of the theorem to adjacent time steps.
  6. [References] The reference 'trumbelj, E. and Kononenko, I.' should be 'Štrumbelj, E. and Kononenko, I.'; also, several workshop citations lack page numbers or are marked as 'NeurIPS 2025 Workshop' without venue details.

Circularity Check

1 steps flagged · score 4.0 of 10

Core CMI bound is not circular; the concrete circularity is empirical: the edge threshold is fit to maximize the reported F1, and the lagged-edge identifiability rests on an unproven definitional 'do' operator rather than a derived guarantee.

  1. fitted input called prediction [Appendix D.1.3 / Fig. 10; operationalized in Sec. 6.1 and used in Sec. 6.2-6.3]
    "We perform a sensitivity analysis across vocabulary sizes |X | ∈ {100, . . . ,2000} to find the optimal filtering threshold τ_opt (maximizing F1-score) with ϵ= 0.04 . The result reveals a power-law relationship τ_opt ∝ |X |−0.96."

    The threshold τ decides every edge in the output graph. Instead of using the theoretical noise floor τ_epsilon from Theorem 4.3, the experiments sweep τ to maximize F1 on the same synthetic benchmark, then report that F1 (Table 2: τ=3e-5; Fig 4: τ=1e-4) as evidence of performance. Since the evaluation metric F1 is used to select the very threshold that creates the edges, the reported F1 is the maximum of a fitted family, not an independent prediction. Appendix D.1.2 concedes the operating τ is orders of magnitude below the theoretical bound, so the bound is not what sets the threshold in the experiments.

full rationale

The central theoretical chain is largely self-contained, not circular. Theorem 4.3 is a genuine continuity bound: from the ε-oracle KL assumption it derives a total-variation-based bound on |I − I_hat|, and Lemma 4.5 is a standard threshold identifiability argument under ε-Strong Faithfulness. The abstract's claim that minimizing cross-entropy minimizes an upper bound on the causal identification error follows from that theorem, and the bound itself does not presuppose the conclusion. The main circularity is empirical: the edge threshold is fit by maximizing F1 on the same synthetic data, so the reported F1 is inflated by construction and cannot be read as an out-of-sample prediction of the theory. Separately, Definition 4.6 defines do(M~Q) as an expectation of observational conditional probabilities, and Theorem 5.1's proof (B.4) invokes Def. 4.8 for lagged effects without proving that the ε-oracle bound covers uniform-random mediator contexts or that this definitional quantity equals a structural direct effect; this is an unsupported derivation step, but it is a gap/assumption issue rather than an equivalence-to-input, so I do not count it as a separate circular step. The vehicle-domain results use the authors' own dataset but are illustrative and not load-bearing for the main theoretical claim.

Assumptions & free parameters 6 free parameters · 8 assumptions · 0 invented entities

The method loads most of its causal content onto assumptions. The central free parameter is the threshold τ, which is fit to maximize F1 on synthetic ground truth; the theory's bound is not used to set it. The causal interpretation requires faithfulness, sufficiency, temporal precedence, and a new strong-faithfulness condition. The do-operator is an invented operation with no justification as a causal intervention.

free parameters (6)
  • threshold τ = 3e-5 (Table 2), 1e-4 (Fig 4/5), τ_opt ≈ 1.72e-2/|X| (Fig 10)
    Chosen to maximize F1 on synthetic ground truth; the theoretical noise floor is not used as the operating threshold.
  • context length c = max(0.1L, 20)
    Hand-chosen truncation to enable GPU batching; the paper admits it may break Markovianity for long sequences.
  • memory horizon m (sparse variant) = 6, 20, 48 in experiments
    Bounds the number of lagged effects considered; recall degrades when m underestimates the true memory.
  • number of particles N = 128 (Table 2), 64 (Fig 4)
    Monte Carlo sample count for CMI estimation; the paper reports N>256 gives no significant change.
  • proposal distribution Q = Uniform over |X|
    Chosen by hand as the randomized mediator distribution in the do-operator; no justification is given.
  • scaling-law constant C = 1.72e-2
    Fitted via power-law regression to optimal thresholds in Fig 10.
assumptions (8)
  • standard math Causal Markov condition and faithfulness (Spirtes et al., 2001)
    Invoked in Def A.1 and throughout to interpret CMI > 0 as a causal edge rather than plain dependence.
  • domain assumption Temporal Precedence (Assumption 3.4)
    Events only influence later events; excludes instantaneous effects and relies on perfect time recording.
  • domain assumption Causal Sufficiency (Assumption 3.5)
    No hidden confounders; the authors test robustness to violations but cannot guarantee it in real logs.
  • domain assumption Epsilon-Oracle Model (Assumption 3.6)
    Requires the AR model's per-step KL to the true transition to be ≤ ε; training only bounds average loss, not per-step KL.
  • ad hoc to paper Epsilon-Strong Faithfulness (Def 4.4)
    Defines identifiability by assuming every true edge has CMI > 2τ_ε; the conclusion is built into the assumption.
  • domain assumption Consistency through time and ergodicity (Appendix C.1.3)
    Needed to justify the summary graph and single-trajectory estimation from one observed sequence.
  • domain assumption High-order Markov chain / finite alphabet (Section 3.1)
    The DGP is assumed to be a stochastic process over a finite alphabet with a full-history transition distribution.
  • ad hoc to paper Do-operator as randomized conditioning (Definition 4.6)
    Assumes averaging model conditionals over random mediator tokens yields a meaningful interventional quantity; no proof is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Your Autoregressive Model Already Reveals the Causal Graph." pith.science (2026). https://pith.science/paper/USBF6IVM

@misc{pith2026260201135,
  author       = {Pith},
  title        = {Pith review of: Your Autoregressive Model Already Reveals the Causal Graph},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/USBF6IVM}},
  note         = {Machine review of arXiv:2602.01135}
}
read the original abstract

Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We exploit this observation to perform scalable causal discovery from a single observed sequence of discrete events -- without any task-specific retraining. Such single-stream settings arise naturally in vehicle diagnostics, manufacturing systems, and patient trajectories, yet they remain largely unsolved: the absence of repeated samples, massive event vocabularies, and long-range temporal dependencies render existing methods either inaccurate or computationally intractable. We introduce TRACE, a framework that repurposes any pretrained autoregressive model as a density estimator for conditional mutual information, the fundamental primitive for conditional independence testing. By constructing parallelized CI tests on GPUs, TRACE recovers both the sample-level time causal graph and its summary projection, scaling linearly with the vocabulary size while naturally handling delayed causal effects. Crucially, we prove that minimizing the standard cross-entropy pretraining loss directly minimizes an upper bound on the causal identification error, establishing a duality between sequence prediction and causal discovery. On nonlinear SCMs (|X| = 8000) and real-world vehicle diagnostic logs (|X| = 29100), TRACE is the first applicable method at this scale, outperforming the strongest baseline by over 20 F1 points.

Figures

Figures reproduced from arXiv: 2602.01135 by the authors.

Figure 1
Figure 1. Methodological Shift. (A) Traditional Causal Discov￾ery in Sequences (e.g., PCMCI, Hawkes, Granger) relies on itera￾tive solvers (CI-tests) over long multivariate time series (T → ∞). (B) Our TRACE Approach processes a single sequence (e.g., event logs, user interactions, patient trajectories) through a pre￾trained autoregressive (AR) model as density estimator to compute the Conditional Mutual Information (CMI) in … view at source ↗
Figure 2
Figure 2. TRACE Methodology. Phase 1 (Training): An autoregressive (AR) model (e.g., LM, RNN) is pretrained on a corpus of event sequences via next-token prediction to learn the process dynamics (Pθ). Phase 2 (Inference): A single sequence s is passed through the frozen model. We then estimate conditional mutual information (Parallelized CMI module) to prune non-causal edges and form the Instance Time Causal Graph Gt,s. Final… view at source ↗
Figure 3
Figure 3. Overview of TRACE Parallel CI-tests. We construct a single broadcasted tensor Xdo where each row j incrementally fixes the history x≤j while randomizing the future (staircase pattern). The model processes this tensor in parallel to produce raw probabilities Praw (grey). We then compute the Causal Mutual Information by comparing adjacent rows: the distribution at row j − 1 serves as the baseline (Pbase, blue) for the… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Scalability to High-Dimensional Event Spaces. Eval￾uation of structural identifiability across exponentially growing vocabulary sizes. Top: Evolution of discovery metrics. TRACE exhibits performance invariance, maintaining F1 ≈ 0.81 even as the combinatorial search spa…
Figure 5
Figure 5. Figure 5: Robustness and Scalability Analysis (|X | = 1000, N = 128, τ = 10−4 , L = 64). Evolution of causal discovery performance (F1, Precision, Recall, SHD). (a) Robustness to Generative Error: Performance as a function of the model’s oracle score ϵ. TRACE exhibits a phase tr…
Figure 6
Figure 6. Figure 6: Instance Time Causal Graph. Temporal evolution of a diagnostic defect cascade in a vehicle (|X | ≈ 29, 100). TRACE effectively captures causal relationships, revealing distinct error clusters at different time steps (e.g., initial sensor failures at t = 3 triggering me…
Figure 7
Figure 7. Figure 7: Robustness to Hidden Confounding. Evaluation of TRACE under violations of causal sufficiency. (a) Measurement Error: Random noise is injected into the context. Precision stays high, indicating resistance to hallucination. (b) Temporal Drops: Time steps are randomly del…
Figure 8
Figure 8. Figure 8: Evolution of the causal discovery performance in function of the number of particles N at |X | = 1000, m = 6 D.1.2. EMPIRICAL VALIDATION OF ϵ-STRONG FAITHFULNESS [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Sensitivity Analysis. Classification metrics relative to the filtering threshold τ with |X | = 1000, ϵ = 0.04, c = 6, m = 6, L = 64, N = 128). D.1.3. SCALING LAW FOR CAUSAL IDENTIFIABILITY In the right ϵ-regime ϵ ∈ [0, 0.1], we observe an inverse scaling law τopt ∝ |X …
Figure 10
Figure 10. Figure 10: The Scaling Law of Causal Identifiability. We perform a sensitivity analysis across vocabulary sizes |X | ∈ {100, . . . , 2000} to find the optimal filtering threshold τopt (maximizing F1-score) with ϵ = 0.04. The result reveals a power-law relationship τopt ∝ |X |−0.…
Figure 11
Figure 11. Figure 11: Instance Summary Causal Graph. The global causal structure Gs aggregated from TRACE inferences over the validation set. While the instance graph ( [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

7 extracted references · 2 canonical work pages

  1. [5]

    Math, H., Lienhart, R., and Sch ¨on, R

    URL https://openreview.net/forum?id= 1HZfpuDVeW. Math, H., Lienhart, R., and Sch ¨on, R. Harnessing event sen- sory data for error pattern prediction in vehicles: A lan- guage model approach. Proceedings of the AAAI Conference on Artificial Intelligence, 39(18):19423–19431, Apr. 2025a. doi: 10.1609/aaai.v39i18.34138. URL https://ojs.aaai. org/index.php/AA...

    work page doi:10.1609/aaai.v39i18.34138 2025

  2. [1991]

    URL https:// doi.org/10.1177/089443939100900106

    doi: 10.1177/089443939100900106. URL https:// doi.org/10.1177/089443939100900106. Spirtes, P., Glymour, C., and Scheines, R. Causation, pre- diction, and search, 2nd edition. In Causation, Prediction, and Search (Second Edition), 2001. URL https://api. semanticscholar.org/CorpusID:124969922. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-...

    arXiv 2001

  3. [1996]

    doi: 10.1007/ 978-1-4612-2404-4 12

    ISBN 978-1-4612-2404-4. doi: 10.1007/ 978-1-4612-2404-4 12. URL https://doi.org/10. 1007/978-1-4612-2404-4_12. Cover, T. Elements of Information Theory. Wiley series in telecommunications and signal processing. Wiley-India, 1999. ISBN 9788126508143. URL https://books.google. de/books?id=3yGJrqyanyYC. C¨uppers, J., Xu, S., Ahmed, M., and Vreeken, J. Causal...

    arXiv 1999

  4. [2000]

    Li, J., Wu, B., Sun, X., and Wang, Y

    Morgan Kaufmann. Li, J., Wu, B., Sun, X., and Wang, Y . Causal hidden markov model for time series disease forecasting. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12100–12109, 2021. URL https://api. semanticscholar.org/CorpusID:232417085. Liu, M., Lee, C.-W., Sun, X., Yu, X., QIAO, Y ., and Wang, Y . Learning causal al...

    2021

  5. [2017]

    Assaad, C

    ISSN 1532-4435. Assaad, C. K., Devijver, E., and Gaussier, E. Survey and evaluation of causal discovery methods for time series. J. Artif. Int. Res., 73, May 2022. ISSN 1076-9757. doi: 10.1613/jair.1.13428. URLhttps://doi.org/10.1613/jair.1.13428. Balazadeh, V ., Kamkari, H., Thomas, V ., Ma, J., Li, B., Cress- well, J. C., and Krishnan, R. CausalPFN: Amo...

    doi:10.1613/jair.1.13428 2022

  6. [2019]

    doi: 10.3390/make1010019

    ISSN 2504-4990. doi: 10.3390/make1010019. Pamfil, R., Sriwattanaworachai, N., Desai, S., Pilgerstorfer, P., Georgatzis, K., Beaumont, P., and Aragam, B. Dynotears: Structure learning from time-series data. In Chiappa, S. and Ca- landra, R. (eds.), Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 10...

    arXiv 2020

  7. [2025]

    Bastings, J

    URL https://openreview.net/forum?id= RblaNJGx8C. Bastings, J. and Filippova, K. The elephant in the interpretabil- ity room: Why use attention as explanation when we have saliency methods? In Alishahi, A., Belinkov, Y ., Chrupała, G., Hupkes, D., Pinter, Y ., and Sajjad, H. (eds.), Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpretin...

    work page doi:10.18653/v1/2020.blackboxnlp-1.1 2020

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.