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arxiv: 2605.11653 · v1 · submitted 2026-05-12 · 💻 cs.CR · cs.AI

Recognition: no theorem link

Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark

Thibaud Gloaguen , Robin Staab , Mark Vero , Martin Vechev
Authors on Pith no claims yet

Pith reviewed 2026-05-13 01:11 UTC · model grok-4.3

classification 💻 cs.CR cs.AI
keywords LLM watermarkingmultibit watermarkbinomial encodingtext generationpayload robustnessstateful encoderper-bit confidence
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The pith

Binomial encoding embeds every bit of a multibit payload at every token position in LLM text generation.

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

The paper presents a multibit watermarking method for large language models that directly encodes all bits of a payload such as a user ID or timestamp at each generated token. It uses binomial distributions to bias token selection probabilities for each bit independently, then applies a stateful mechanism to shift encoding focus toward bits that have received less pressure so far. This setup is evaluated on payloads up to 64 bits against eight prior methods and shows higher message recovery rates and better resistance to text changes, with the advantage growing as payloads get longer or allowed distortion shrinks. The work also argues that earlier evaluation approaches miss practical signals and proposes per-bit scoring instead. If the claims hold, watermarking systems could carry richer information while keeping generated text close to normal output.

Core claim

The authors claim that binomial encoding combined with stateful redirection during generation produces watermarks that recover the full payload more accurately and more robustly than existing multibit schemes, especially when the payload is large and the text must remain nearly unchanged. They demonstrate this on up to 64-bit messages and introduce per-bit confidence as an evaluation measure that better reflects real usability than aggregate metrics used before.

What carries the argument

Binomial encoding, in which each payload bit is represented by a separate statistical bias in token probabilities at every position, together with a stateful encoder that tracks cumulative bit coverage and redirects selection pressure to lagging bits.

If this is right

  • Message recovery rates stay high for payloads as large as 64 bits even when the allowed change to the text is small.
  • Robustness against common modifications improves relative to prior methods, with the margin largest in the low-distortion regime.
  • Per-bit scoring reveals which parts of the payload are most reliably detected, giving a finer view than whole-message accuracy alone.
  • The stateful redirection prevents any single bit from lagging too far behind, keeping overall payload completeness stable across long outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Watermarking could support practical uses such as embedding timestamps or session identifiers without forcing noticeable changes to the generated text.
  • The same encoding principle might be tested on other sequence models where statistical biases in token choice can be measured.
  • Detection performance under stronger adversarial edits that specifically target binomial biases remains an open question the paper does not fully close.

Load-bearing premise

The statistical biases introduced by the encoding remain detectable and decodable even after the text has been edited or when the detector does not have complete records of the original generation choices.

What would settle it

An experiment that generates watermarked text with 32-bit or 64-bit payloads, applies realistic edits such as synonym swaps or sentence reordering, and then measures whether the recovered message accuracy falls below 70 percent using the proposed detector.

Figures

Figures reproduced from arXiv: 2605.11653 by Mark Vero, Martin Vechev, Robin Staab, Thibaud Gloaguen.

Figure 1
Figure 1. Figure 1: Overview of Our Multibit Watermark Algorithm Encoder and Decoder: Given an m-bit bitstring (here 010), at each step of the generation process, we use the context to seed a pseudorandom number generator (PRNG) that samples m Bernoulli variables (G) for each token in the vocabulary. We encode our bitstring with an XNOR operation and sum the encoded Bernoulli variables to obtain binomial token scores (G˜). We… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of the Message Accuracy-Quality Trade-Off: We compare the trade-off between message accuracy and text quality (log PPL) for various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1-8B using prompts from ELI5. how often a valid message is wrong, and does not necessarily provide information on which … view at source ↗
Figure 3
Figure 3. Figure 3: Message Accuracy with Respect to the Number of Tokens: We compare how the mes￾sage accuracy scales with the number of generated tokens. The hyperparameters of each watermark￾ing scheme have been selected to have a similar impact on quality in the low-distortion regime, and all schemes use a bit string of 32 bits. In this section, we evaluate the performance of our proposed multibit watermark, first with me… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of the Confidence Bit Accuracy-Quality Trade-Off: We compare the trade-off between confidence bit accuracy (BA@1%FPR) and text quality (log PPL) across several multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1-8B using prompts from ELI5. mind. If a given scheme reaches 100% message accuracy for a bi… view at source ↗
Figure 5
Figure 5. Figure 5: Bit Accuracy at 1% FPR with Re￾spect to the Number of Tokens: We compare how BA@1%FPR scales with the number of gen￾erated tokens. The hyperparameters of each wa￾termarking scheme are selected to have a similar impact on quality in the low-distortion regime, and all schemes use a bit string of 32 bits. Per-Bit Confidence As explained in Sec. 4, we introduce a new, more realistic metric to measure multibit … view at source ↗
Figure 6
Figure 6. Figure 6: Zero-Bit Watermark Detection Under the Null: We show the empirical FPR versus the theoretical FPR for various zero-bit watermark detectors. The left side uses the original p-value implementation, whereas the right side uses Monte Carlo-based p-values (except for Cycle-Shift). To compute the p-values, we use 1000 200-token-long texts extracted from the realnewslike subset of C4. For all watermarks, we use a… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of the Detectability-Quality Trade-Off: We compare the trade-off between zero-bit detectability (TPR@1%FPR) and text quality (log PPL) across various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1-8B using prompts from ELI5. where S1, . . . , Sm are sampled i.i.d. from Binomial(|ω|, 0.5). Zero-Bi… view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of the Bit Accuracy-Quality Trade-Off: We compare the trade-off between bit accuracy and text quality (log PPL) for various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1- 8B using prompts from ELI5. For Cycle-Shift, only payloads below log2 |Σ| ≈ 20 bits are supported. For ArcMark, only payloads… view at source ↗
Figure 9
Figure 9. Figure 9: Bit Accuracy with Respect to the Num￾ber of Tokens: We compare how the bit accuracy scales with the number of generated tokens. The hyperparameters of each watermarking scheme have been selected to have a similar impact on quality, and all schemes use a bit string of 32 bits. Improved Bit Accuracy For each completion, we record the bit accuracy, i.e., the percentage of bits that we decode correctly. A bit … view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the Message Accuracy-Quality Trade-Off using LLM Benchmarks: We compare the trade-off between message accuracy and text quality (average benchmark accuracy) for various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1-8B using prompts from ELI5. For Cycle-Shift, only payloads below log2 |Σ| ≈ 2… view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of the Bit Accuracy-Quality Trade-Off with MINISTRAL-3-14B: We compare the trade-off between bit accuracy and text quality (log PPL) for various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by MINISTRAL-3-14B using prompts from ELI5. For Cycle-Shift, only payloads below log2 |Σ| ≈ 20 bits are supported. F… view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of the Message Accuracy-Quality Trade-Off with MINISTRAL-3-14B: We compare the trade-off between message accuracy and text quality (log PPL) for various multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by MINISTRAL-3-14B using prompts from ELI5. 4 5 6 7 8 9 10 ← Quality (Perplexity, log scaled) 0.0 0.2 0.4 0.… view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of the Confidence Bit Accuracy-Quality Trade-Off with MINISTRAL￾3-14B: We compare the trade-off between confidence bit accuracy (BA@1%FPR) and text quality (log PPL) across several multibit watermarks and three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by MINISTRAL-3-14B using prompts from ELI5. Results In Figures 11 and 12, we obs… view at source ↗
Figure 14
Figure 14. Figure 14: Ablation on the Remaining Bits T: We compare the message accuracy of our stateful binomial encoder for different values of T at dif￾ferent token lengths. Experimental Setup To justify the averaged choice used in the main experiments, we ablate the values of T. Specifically, we compute the message accuracy of our stateful encoder with T ∈ {64, 200, 300, 500, 1000, 2000}, as well as when averaging Equation … view at source ↗
Figure 15
Figure 15. Figure 15: Message Accuracy for Different Seg￾ment Sizes: We compare how our method com￾bines with position allocation by splitting the 32 bits into k ∈ {1, 4, 8, 16, 32} segments. Here, we verify whether our method, which en￾codes every bit at once using binomial encoding, improves over position allocation. Experimental Setup We use the same exper￾imental setup as in Sec. 5: for each watermark configuration, we gen… view at source ↗
Figure 16
Figure 16. Figure 16: Comparison of the Detectability of Various SynthID Layers: We compare the BA@1%FPR for various numbers of SynthID layers nlayer across three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated by LLAMA3.1-8B using prompts from ELI5. E SynthID-Text Multibit Watermark In this section, we propose replacing the Soft PPL watermarking scheme (Equation (8)) w… view at source ↗
Figure 17
Figure 17. Figure 17: Comparison of the Message Accuracy-Quality Trade-Off for Unconstrained Soft PPL: We compare the trade-off between message accuracy and text quality (log PPL) for the constrained Soft PPL parametrization (Ours) and the unconstrained parametrization using λ as a scheme parameter (Ours (unc.)), for three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 350 tokens and is generated… view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of the Confidence Bit Accuracy-Quality Trade-Off for Unconstrained Soft PPL: We compare the trade-off between confidence bit accuracy (BA@1% FPR) and text quality (log PPL) for the constrained Soft PPL parametrization (Ours) and the unconstrained parametrization using λ as a scheme parameter (Ours (unc.)), for three bitstring lengths (16, 32, and 64 bits). Each sample contains between 250 and 3… view at source ↗
read the original abstract

With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or timestamps, into the generated text. In this work, we propose a fundamentally new approach for multibit watermarking: introducing binomial encoding to directly encode every bit of the payload at every token position. We complement our approach with a stateful encoder that during generation dynamically redirects encoding pressure toward underencoded bits. Our evaluation against 8 baselines on up to 64-bit payloads shows that our scheme achieves superior message accuracy and robustness, with the gap to baseline methods widening in more relevant settings (i.e., large payloads and low-distortion regimes). At the same time, we challenge prior works' evaluation metrics, highlighting their lack of practical insights, and introduce per-bit confidence scoring as a practically relevant metric for evaluating multibit LLM watermarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a multibit LLM watermarking method based on binomial encoding, which embeds every bit of a payload (e.g., user ID or timestamp) at every token position, paired with a stateful encoder that dynamically redirects encoding effort toward under-encoded bits during generation. Evaluations against 8 baselines on payloads up to 64 bits report superior message accuracy and robustness, with the performance gap increasing for larger payloads and lower distortion levels; the work also critiques existing evaluation metrics and introduces per-bit confidence scoring.

Significance. If the reported gains in accuracy and robustness are reproducible under standard black-box detection (key plus output text only), the binomial-plus-stateful approach would represent a meaningful advance for practical multibit watermarking in deployed LLMs. The per-bit confidence metric could also provide more actionable evaluation than aggregate accuracy alone, particularly when payload bits have unequal importance.

major comments (2)
  1. [Abstract and evaluation description] The central empirical claim of superior message accuracy/robustness (especially the widening gap for 64-bit payloads in low-distortion regimes) rests on the assumption that the stateful redirection can be inverted by a detector given only the secret key and final text. The abstract and evaluation description do not specify whether the detector must reconstruct per-token redirection state (e.g., counters of remaining under-encoded bits or prior-token history) or whether decoding is fully stateless; if the former, the accuracy advantage may not hold in standard black-box settings.
  2. [Evaluation section] The robustness experiments need to clarify the threat model for the reported gains: which specific attacks (e.g., paraphrasing, token substitution, or regeneration) are considered, and whether the stateful component remains recoverable after each attack. Without this, it is difficult to assess whether the binomial encoding itself or the redirection mechanism drives the improvement.
minor comments (2)
  1. [Abstract] The abstract refers to 'binomial encoding' without a brief inline definition or pointer to the formal construction; adding one sentence would improve accessibility for readers unfamiliar with the scheme.
  2. [Evaluation section] The paper should include a short table or paragraph comparing the new per-bit confidence metric against the prior aggregate metrics it critiques, with a concrete example on a small payload.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed comments. These highlight important points about detector requirements and evaluation clarity that we will address through revisions. Below we respond point by point.

read point-by-point responses

  1. Referee: [Abstract and evaluation description] The central empirical claim of superior message accuracy/robustness (especially the widening gap for 64-bit payloads in low-distortion regimes) rests on the assumption that the stateful redirection can be inverted by a detector given only the secret key and final text. The abstract and evaluation description do not specify whether the detector must reconstruct per-token redirection state (e.g., counters of remaining under-encoded bits or prior-token history) or whether decoding is fully stateless; if the former, the accuracy advantage may not hold in standard black-box settings.

    Authors: We appreciate this observation and agree that the detector specification requires explicit clarification. Our detector is fully black-box and stateless in operation: it requires only the secret key and output text. The redirection state (counters for under-encoded bits) is reconstructed deterministically during detection by sequentially processing the text, decoding each token's contribution to the payload bits, and updating the same counters the encoder would have used. This reconstruction uses the decoded bits themselves to drive the state transitions, ensuring no additional information beyond key and text is needed. All reported accuracy and robustness numbers were obtained with this exact detector. We will revise the abstract and evaluation section to describe this reconstruction procedure in detail and to confirm that the black-box setting was used throughout. revision: yes

  2. Referee: [Evaluation section] The robustness experiments need to clarify the threat model for the reported gains: which specific attacks (e.g., paraphrasing, token substitution, or regeneration) are considered, and whether the stateful component remains recoverable after each attack. Without this, it is difficult to assess whether the binomial encoding itself or the redirection mechanism drives the improvement.

    Authors: We agree that the threat model and attack details should be stated more explicitly. Our robustness evaluation considers three attacks: (1) paraphrasing via an auxiliary LLM, (2) random token substitution at varying rates, and (3) regeneration from a modified prompt. For each, we measure end-to-end message accuracy and per-bit confidence after the attack. The stateful redirection is recovered post-attack by the same sequential reconstruction process used in clean detection; attacks that corrupt tokens naturally lower the per-bit confidence scores for affected bits, which is precisely why we report per-bit metrics. The performance gains arise from the combination of binomial encoding (which places every bit at every position) and redirection (which balances encoding effort), both of which remain beneficial even after state reconstruction on attacked text. We will add a new subsection detailing the threat model, the three attacks, and the post-attack state reconstruction procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity in binomial multibit watermarking proposal

full rationale

The paper introduces a new encoding scheme (binomial per-bit encoding at every token plus stateful redirection) and evaluates it empirically against baselines on message accuracy and robustness. No derivation step reduces by construction to a fitted parameter, self-definition, or prior self-citation; the central claims rest on the explicit algorithmic description and reported experimental outcomes rather than tautological renaming or imported uniqueness theorems. The introduced per-bit confidence metric is presented as an alternative evaluation tool without depending on the scheme's own fitted results. The derivation chain from the proposed encoder to the accuracy claims is self-contained and externally falsifiable via the described black-box detection procedure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on specific free parameters, axioms, or invented entities; none can be identified.

pith-pipeline@v0.9.0 · 5456 in / 949 out tokens · 43400 ms · 2026-05-13T01:11:18.985442+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 3 internal anchors

  1. [1]

    Generative ai transparency: Identifi- cation of machine-generated content

    Hamon R, Sanchez I, Fernandez Llorca D, and Gomez E. Generative ai transparency: Identifi- cation of machine-generated content. Scientific analysis or review, Ispra (Italy), 2024

    work page 2024

  2. [2]

    Advancing beyond identification: Multi-bit watermark for large language models, 2024

    KiYoon Yoo, Wonhyuk Ahn, and Nojun Kwak. Advancing beyond identification: Multi-bit watermark for large language models, 2024. URLhttps://arxiv.org/abs/2308.00221

    work page arXiv 2024

  3. [3]

    Provably robust multi-bit watermarking for ai-generated text,

    Wenjie Qu, Wengrui Zheng, Tianyang Tao, Dong Yin, Yanze Jiang, Zhihua Tian, Wei Zou, Jinyuan Jia, and Jiaheng Zhang. Provably robust multi-bit watermarking for ai-generated text,

    work page

  4. [4]

    URLhttps://arxiv.org/abs/2401.16820

    work page arXiv

  5. [5]

    MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking

    Ya Jiang, Massieh Kordi Boroujeny, Surender Suresh Kumar, and Kai Zeng. Mirrormark: A distortion-free multi-bit watermark for large language models, 2026. URL https://arxiv. org/abs/2601.22246

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  6. [6]

    Atefeh Gilani, Carol Xuan Long, Sajani Vithana, Oliver Kosut, Lalitha Sankar, and Flavio P. Calmon. Arcmark: Multi-bit llm watermark via optimal transport, 2026. URL https://arxiv. org/abs/2602.07235

    work page arXiv 2026

  7. [7]

    Three bricks to consolidate watermarks for large language models.2023 IEEE International Workshop on Information Forensics and Security (WIFS), 2023

    Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier, and Teddy Furon. Three bricks to consolidate watermarks for large language models.2023 IEEE International Workshop on Information Forensics and Security (WIFS), 2023

    work page 2023

  8. [8]

    A watermark for large language models

    John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. A watermark for large language models. InInternational Conference on Machine Learning, pages 17061–17084. PMLR, 2023

    work page 2023

  9. [9]

    Scalable watermarking for identifying large language model outputs.Nature, 634(8035):818–823, 2024

    Sumanth Dathathri, Abigail See, Sumedh Ghaisas, Po-Sen Huang, Rob McAdam, Johannes Welbl, Vandana Bachani, Alex Kaskasoli, Robert Stanforth, Tatiana Matejovicova, et al. Scalable watermarking for identifying large language model outputs.Nature, 634(8035):818–823, 2024

    work page 2024

  10. [10]

    Watermarking of large language models

    Scott Aaronson. Watermarking of large language models. InWorkshop on Large Language Models and Transformers, Simons Institute, UC Berkeley, 2023

    work page 2023

  11. [11]

    Dipmark: A stealthy, efficient and resilient watermark for large language models

    Yihan Wu, Zhengmian Hu, Hongyang Zhang, and Heng Huang. Dipmark: A stealthy, efficient and resilient watermark for large language models. 2023

    work page 2023

  12. [12]

    Improved unbiased watermark for large language models.CoRR, 2025

    Ruibo Chen, Yihan Wu, Junfeng Guo, and Heng Huang. Improved unbiased watermark for large language models.CoRR, 2025

    work page 2025

  13. [13]

    Robust distortion- free watermarks for language models.TMLR, 2024

    Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang. Robust distortion- free watermarks for language models.TMLR, 2024

    work page 2024

  14. [14]

    Stealthink: A multi-bit and stealthy watermark for large language models, 2025

    Ya Jiang, Chuxiong Wu, Massieh Kordi Boroujeny, Brian Mark, and Kai Zeng. Stealthink: A multi-bit and stealthy watermark for large language models, 2025. URL https://arxiv.org/ abs/2506.05502

    work page arXiv 2025

  15. [15]

    Bimark: Unbiased multilayer watermarking for large language models, 2025

    Xiaoyan Feng, He Zhang, Yanjun Zhang, Leo Yu Zhang, and Shirui Pan. Bimark: Unbiased multilayer watermarking for large language models, 2025. URL https://arxiv.org/abs/ 2506.21602

    work page arXiv 2025

  16. [16]

    Mc 2mark: Distortion-free multi-bit watermarking for long messages, 2026

    Xuehao Cui, Ruibo Chen, Yihan Wu, and Heng Huang. Mc 2mark: Distortion-free multi-bit watermarking for long messages, 2026. URLhttps://arxiv.org/abs/2602.14030

    work page arXiv 2026

  17. [17]

    A unified framework for llm watermarks, 2026

    Thibaud Gloaguen, Robin Staab, Nikola Jovanovi´c, and Martin Vechev. A unified framework for llm watermarks, 2026. URLhttps://arxiv.org/abs/2602.06754

    work page arXiv 2026

  18. [18]

    Eli5: Long form question answering, 2019

    Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. Eli5: Long form question answering, 2019. URLhttps://arxiv.org/abs/1907.09190

    work page arXiv 2019

  19. [19]

    W. W. Peterson and D. T. Brown. Cyclic codes for error detection.Proceedings of the IRE, 49 (1):228–235, 1961. doi: 10.1109/JRPROC.1961.287814. 10

    work page doi:10.1109/jrproc.1961.287814 1961

  20. [20]

    Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, and Philip S. Yu. MarkLLM: An open-source toolkit for LLM watermarking. In Delia Irazu Hernandez Farias, Tom Hope, and Manling Li, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processin...

    work page 2024

  21. [21]

    Gonzalez, Hao Zhang, and Ion Stoica

    Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large lan- guage model serving with pagedattention. InProceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023

    work page 2023

  22. [22]

    Watermarking diffusion language models

    Thibaud Gloaguen, Robin Staab, Nikola Jovanovi´c, and Martin Vechev. Watermarking diffusion language models. InThe Fourteenth International Conference on Learning Representations,

    work page

  23. [23]

    URLhttps://openreview.net/forum?id=3aBWTYGcaT

    work page

  24. [24]

    The language model evaluation harness, 07 2024.https://zenodo.org/records/12608602

    Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. The languag...

    work page arXiv 2024

  25. [25]

    Training Verifiers to Solve Math Word Problems

    Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  26. [26]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian...

    work page 2021

  27. [27]

    Program Synthesis with Large Language Models

    Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models.arXiv preprint arXiv:2108.07732, 2021. 11 A Experimental Details In this section, we describe precisely the experimental setup used in our evaluation (Sec. 5), with ...

    work page internal anchor Pith review Pith/arXiv arXiv 2021