Pith. sign in

REVIEW 4 major objections 6 minor 68 references

Efficient Alignment of Large Language Models via Data Sampling

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read LLM alignment quality plateaus exponentially with data, so a 10% subset can match the full dataset.

desk verdict The central claim that ISA matches full-data alignment is contradicted by the paper's own tables on two of three datasets; the scaling law is a two-point fit. read the letter →

arxiv 2411.10545 v2 pith:4KD6IGSS submitted 2024-11-15 cs.LG cs.CL

Amrit Khera , Rajat Ghosh , Debojyoti Dutta This is my paper · ORCID
classification cs.LGcs.CL
keywords LLMalignmentdata-efficientexponentialscalinglawKTOentropysamplingGaussianmixturemodeldatasubsamplingGPT-4ojudge
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 show that LLM alignment is data-hungry only in its early phase: alignment performance rises quickly with the fraction of training data and then plateaus, following an exponential law $R(x) = r - (r-a)e^{-bx}$. From that law it concludes that a small, carefully chosen subset of a preference dataset can align a model about as well as the full dataset. To choose that subset it introduces ISA, which fits a two-component Gaussian mixture to embeddings of the data and keeps the points whose removal changes the dataset's entropy the most. The reported experiments on three datasets claim that ISA beats random, density-based, and LLM-based sampling and is comparable to full-data alignment while using 3.5--10% of the data, a >90% saving. The paper also reports that extending the study to several other open models gave no clear gains, which it attributes to possible data leakage.

What carries the argument

The machinery is the combination of the plateau law and the ISA sampler. ISA first models all alignment examples as a two-component Gaussian mixture over embeddings from a Llama-3-8B-Instruct encoder, interpreting the two components as desired and undesired responses. It computes each point's log-likelihood under the mixture, converts it to a probability, computes the dataset entropy $H(X)$, and then scores every point by the drop in entropy its removal would cause. Keeping the highest-drop points yields a subset that is meant to be both diverse, because the mixture separates clusters, and high-quality, because points that most affect the distribution are retained. The law, in turn, tells the user how little data is enough: it is the value of $x$ at which $R(x)$ stops changing.

What would settle it

Run the same KTO alignment on three additional datasets at 40%, 70%, and 100% data fractions and score the outputs with human annotators; if human win rates keep rising past 40% or ISA's sampled models do not match full-data models under human judgment, the plateau law and the >90% savings claim fail.

Watch

Extended reading notes

Core claim

The central discovery is a scaling law for direct alignment with KTO: win rate versus data fraction follows $R(x) = r - (r-a)e^{-bx}$, where $r$ is the ceiling, $a$ the unaligned model's win rate, and $b$ a growth rate tied to dataset complexity. Empirically the curve rises sharply and then flattens, meaning the last 50--90% of the dataset contributes almost nothing to the judge-measured win rate. The paper presents this as evidence that alignment is over-optimized on full data and that KTO is more robust to over-optimization than earlier direct algorithms, which show a hump-shaped rather than plateau pattern.

Load-bearing premise

All measured win rates, including the exponential plateau curve and the ISA advantage, come from a single GPT-4o judge with no reported agreement check or alternative metric, so a systematic bias in that judge would reshape every experimental conclusion.

Editorial extensions

If this is right

  • Alignment runs can be cut to 10% or less of the preference data without a measurable drop in judge-reported win rate, saving most of the compute, memory, and time.
  • The plateau law gives a concrete rule for choosing subset size: stop adding data once $R(x)$ flattens, rather than training on the full set.
  • If the plateau generalizes to other direct alignment algorithms, current RLHF-style pipelines are systematically over-optimized.
  • High-quality subsets can be identified without labels: a two-component Gaussian mixture plus entropy scoring supplies both diversity and quality.
  • The same scaling law could be used to predict the data quantity needed before running a new alignment experiment.

Reading between the lines

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

  • The reported numbers already contain a tension: on OpenAssistant and Ultrafeedback the full-data model beats ISA by 2.9 and 1.6 win-rate points with non-overlapping error bars, so 'comparable' holds only at moderate tolerance; a stricter equivalence test would likely reject equality.
  • The judge is used both to fit the curve and to compare samplers, which can create a feedback loop: if GPT-4o prefers a certain style, the sampler that mimics that style will appear to work best; human evaluation would be needed to rule this out.
  • The exponential fit has only three parameters and five data points per dataset; a model with more flexible curves might fit equally well, so the functional form is a hypothesis to test, not a law.
  • The ISA sampler's reliance on a specific embedding model means its benefit may not transfer to other encoders; a sensitivity analysis across embedding models would clarify which component of the method carries the gain.
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 addresses data-efficient alignment of LLMs. It first claims an empirical scaling law R(x) = r - (r-a)e^{-bx} (Eq. 1) for alignment performance as a function of data fraction, and then proposes Information Sampling for Alignment (ISA), which models alignment data as a 2-component Gaussian mixture and uses entropy-based sampling to select a small subset. The authors claim that ISA outperforms random, density, and LLM-based sampling baselines and is comparable to full-data alignment while using less than 10% of the data, yielding over 90% resource savings.

Significance. If the claims were supported, the paper would make a practically valuable contribution by showing that alignment can be performed with a fraction of the data, and the proposed ISA method is a creative combination of GMM-based diversity and information-theoretic selection. However, the central 'comparable to full data' claim is contradicted by the paper's own winrate tables on two of three datasets, and the scaling-law fit is partly circular because its asymptote and initial value are fixed to observed endpoints. The appendix also reports that the scaling pattern did not appear on five other models. These issues mean the contributions are not yet established; the paper's significance is therefore currently limited.

major comments (4)
  1. [Section 4 / Tables 1 and 2] The abstract and Section 4 claim that ISA performs 'comparable' to full-data alignment. This is contradicted by the reported 95% confidence intervals. On OpenAssistant, full-data KTO wins 24.6601 ± 0.5427 vs ISA 21.73 ± 0.6329; on Ultrafeedback, full-data wins 26.869 ± 0.1756 vs ISA 25.2252 ± 0.7007. In both cases the intervals do not overlap, indicating a statistically significant deficit for ISA under the paper's own statistics. Only the Anthropic Golden dataset shows ISA numerically ahead, but there the full-data result (82.7093) is below the 75% checkpoint (83.1681), so the scaling pattern is non-monotonic. The paper must either weaken the central claim to 'slightly worse but with large resource savings' or provide a pre-specified equivalence margin and show the differences fall within it.
  2. [Section 3 / Table 3] The scaling-law fit in Eq. (1) is not an independent discovery: in Table 3, the asymptote r is set equal to the observed 75%-data winrate (e.g., 83.1681 for Anthropic, 24.6601 for OpenAssistant, 27.4849 for Ultrafeedback) and a is set equal to the 0% baseline from Table 2. Thus only the rate parameter b is fitted to the remaining data, so the exponential-plateau shape is substantially imposed by construction. The paper reports no goodness-of-fit, no uncertainty on b, and no comparison against alternative functional forms such as the hump-shaped scaling found by Rafailov et al. (2024). The non-monotonic Anthropic data (75% vs 100%) further suggests that the exponential plateau is not an adequate description. Please re-fit with all parameters free, report model comparison, and test predictive accuracy on held-out fractions.
  3. [Appendix A.6.2] The appendix states that experiments on Alpaca-7B, Pythia-6.9B, LLaMa-2-7B-Chat, Dolly-6B, and Vicuna-7B 'did not reveal significant performance improvements for alignment.' This is a direct counterexample to the general claim in Section 3 that alignment performance follows the exponential plateau, and to the conclusion that data-efficient alignment is broadly feasible. The paper cannot simply speculate about data leakage without verification; either the claims must be restricted to models where the pattern holds, or the failure must be analyzed to show that the absence of improvement is not due to an incompatible scaling law.
  4. [Section 3 / Evaluation protocol] All empirical conclusions rest on GPT-4o judge winrates, but the paper does not report the evaluation set size, the number of judge calls, judge-human agreement, or any variance due to judge sampling. The reference to 'the same setting as the KTO study' is insufficient for reproducibility, and using the same judge for both fitting the scaling law and evaluating ISA creates a risk that systematic judge bias shapes the comparison. Please provide a detailed evaluation protocol, report judge agreement, and include at least one non-judge metric (e.g., standard benchmark scores) to corroborate the winrate findings.
minor comments (6)
  1. [Section 2] The dataset name 'OpenAssitant1' should be 'OpenAssistant1'.
  2. [Section 3] The word 'intial' should be 'initial'.
  3. [Table 3 / Table 2] The values of a in Table 3 (e.g., 18.2681 for Anthropic) differ slightly from the N/A rows in Table 2 (18.2663). Please ensure consistency.
  4. [Algorithm 1] Algorithm 1 recomputes the entropy over the whole dataset for every candidate removed, giving O(N^2) complexity. Since efficiency is a core claim, a discussion of runtime or a scalable approximation would be helpful.
  5. [Figure 5] The LLM sampling prompt contains the typo 'reposne' instead of 'response'.
  6. [Appendix A.6.2] The 'Limitations' section is only in the appendix; the main text should explicitly reference it when making general claims about the scaling law and the feasibility of efficient alignment.

Circularity Check

1 steps flagged · score 4.0 of 10

Scaling-law plateau is anchored to the observed endpoint winrates; the ISA comparison itself is measured, so circularity is partial and localized to the fitted scaling-law reasoning.

  1. fitted input called prediction [Section 3, Eq. (1); parameter values in Table 3, Appendix A.3]
    "We treat the GPT-4o winrates over the dataset fraction as a proxy of the reward and fit curves using the empirical results over the three datasets mentioned in section 2 and the fitted curves and the data points are present in Figure 1. We observe that the curve takes the form of an exponential growth which decays into a plateau: R(x) = r − (r − a)e−bx (1) where, x represents the percentage of the data used for alignment, R(x) represents the proxy reward of GPT-4o winrates, r represents the asymptote depicting the max reward attainable, a represents the initial reward of the unaligned model"

    The parameters in Table 3 are taken directly from the observed winrate table: r is set to the largest observed winrate (83.1681 for Anthropic 75% data, 24.6601 for OpenAssistant all-data, 27.4849 for Ultrafeedback 75% data) and a is set to the unaligned-model winrate. Thus the exponential-plateau curve is anchored at the observed maximum by construction: the claimed finding that alignment performance 'follows an exponential plateau pattern' and the inference that roughly 10% data is sufficient are restatements of the fitted endpoints rather than independent predictions. The ISA method's winrates in Table 1 are separately measured, so the circularity is partial and confined to the scaling-law reasoning.

full rationale

The paper is largely self-contained: ISA is a concrete two-stage heuristic (GMM log-likelihood plus entropy sampling on Llama-3 embeddings), and its reported winrates are measured against random, density, and LLM sampling rather than derived from the scaling law. No load-bearing self-citation appears; the authors do not rely on their own prior theorems. The main circular element is the scaling-law discussion: Eq. (1) is fit with r equal to the largest observed winrate and a equal to the unaligned baseline, so the 'plateau' and the less-than-10%-data inference are anchored by construction to the same winrate table. The post-hoc selection of 3.5%/10% as the 'minimum percentage required for comparable performance' is a robustness flaw, but because the ISA winrates are actual measurements, it does not by itself make the ISA comparison circular. The paper's own Tables 1 and 2 also contradict the headline 'comparable to full data' on OpenAssistant (24.6601±0.5427 vs 21.73±0.6329) and Ultrafeedback (26.869±0.1756 vs 25.2252±0.7007), with non-overlapping 95% confidence intervals; that is a correctness/falsification issue rather than a circularity. Appendix A.6.2 further notes that several other models 'did not reveal significant performance improvements for alignment,' limiting generality. Overall, the circularity is partial and localized to the fitted scaling-law reasoning, not to the ISA method itself.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims depend on a fitted scaling law with pinned endpoints, an unspecified GMM, a judge-based quality proxy, and post hoc subsample sizes. The number of free parameters is high relative to the evidence, and no independent validation is provided.

free parameters (3)
  • Scaling-law parameters r, a, b = Anthropic: r=83.1681, a=18.2681, b=0.3; OpenAssistant: r=24.6601, a=8.4452, b=0.02; Ultrafeedback: r=27.4849…
    r is set to the observed winrate at 75% (or 100% for OA), a to the no-alignment baseline, and b is fit to interpolate. The plateau is an artifact of fixing r to the maximum observed point.
  • GMM parameters (pi, mu1, mu2, Sigma1, Sigma2) = not reported
    The 2-component Gaussian mixture in Eq. 2 is never estimated or specified, so ISA cannot be reproduced.
  • Subsample fraction k = 3.5% (Anthropic), 10% (OpenAssistant, Ultrafeedback)
    The fraction is chosen per dataset after seeing results; no rule for setting k is provided.
assumptions (4)
  • ad hoc to paper Alignment data can be modeled as a 2-component Gaussian mixture
    Stated in Section 4 first paragraph; no evidence that desired/undesired response embeddings are Gaussian or that two components suffice.
  • domain assumption GPT-4o judge winrate is a valid proxy for alignment quality
    Used in Section 3 to measure scaling and Section 4 to compare methods; no judge agreement analysis or alternative metrics.
  • ad hoc to paper The exponential plateau curve R(x) = r - (r-a)e^{-bx} describes alignment scaling
    Fit to 5 points per dataset with two parameters pinned to observed endpoints; the functional form is not compared with alternatives.
  • domain assumption KTO on Mistral-7B for one epoch is representative of LLM alignment
    All main experiments use one DAA, one model, and one epoch; the appendix's negative results on five other models undermine generality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Efficient Alignment of Large Language Models via Data Sampling." pith.science (2026). https://pith.science/paper/4KD6IGSS

@misc{pith2026241110545,
  author       = {Pith},
  title        = {Pith review of: Efficient Alignment of Large Language Models via Data Sampling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4KD6IGSS}},
  note         = {Machine review of arXiv:2411.10545}
}
read the original abstract

LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.

Figures

Figures reproduced from arXiv: 2411.10545 by the authors.

Figure 1
Figure 1. Alignment data performance with Mistral-7B-v0.1. Alignment performance follows an [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. GPT-4o winrates for sampling strategies against alignment on all the dataset for [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Information Sampling for Alignment A.5.1 Density Sampling The Kernel based Density Sampling was proposed by [24] as a methodology focusing on coverage to sub-sample data points in the fine-tuning paradigm. The authors modify the strategy using embedded latents instead of n-grams and a two pass algorithm with better theoretical guarantees. We use the Meta-Llama-3-8B-Instruct model ([1]) to extract the embeddings of s… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Density Sampling and the model used which needs to be tuned based on the dataset. We use a simple approach similar to the stateless approach of Ask-LLM as described in the Algorithm 3 as a baseline, however, we admit that this may not be representative of the capabilit…
Figure 5
Figure 5. Figure 5: LLM Sampling Prompt Creation Algorithm 2 LLM Sampling 1: Input: Dataset D = {x1, x2, ...xN } containing N plain text data points, sub-sample size k, LLM L 2: Sk = [] 3: for i = 1 → N do 4: prompti = createP rompt(xi) {Create the prompt as specified in [PITH_FULL_IMAGE…
Figure 6
Figure 6. Figure 6: Alignment data performance with Llama-30B. The alignment performance follows the [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

68 extracted references · 35 canonical work pages

  1. [1]

    Llama 3 model card

    AI@Meta. Llama 3 model card. 2024. URL https://github.com/meta-llama/llama3/ blob/main/MODEL_CARD.md

    2024

  2. [2]

    Data pruning and neural scaling laws: fundamental limitations of score-based algorithms, 2023

    Fadhel Ayed and Soufiane Hayou. Data pruning and neural scaling laws: fundamental limitations of score-based algorithms, 2023. URL https://arxiv.org/abs/2302.06960

    arXiv 2023

  3. [3]

    Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, ...

    arXiv 2022

  4. [4]

    Pythia: A suite for analyzing large language models across training and scaling, 2023

    Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia: A suite for analyzing large language models across training and scaling, 2023. URL https://arxiv.org/abs/2304. 01373

    work page 2023

  5. [5]

    Ulma: Unified language model alignment with human demonstration and point-wise preference, 2024

    Tianchi Cai, Xierui Song, Jiyan Jiang, Fei Teng, Jinjie Gu, and Guannan Zhang. Ulma: Unified language model alignment with human demonstration and point-wise preference, 2024. URL https://arxiv.org/abs/2312.02554

    arXiv 2024

  6. [6]

    Open problems and fundamental limitations of reinforcement learning from human feedback

    Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv preprint arXiv:2307.15217, 2023

    arXiv 2023

  7. [7]

    Deep reinforcement learning from human preferences

    Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017

    2017

  8. [8]

    Hello dolly: Democratizing the magic of chat- gpt with open models, 2023

    Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Gh- odsi, Patrick Wendell, and Matei Zaharia. Hello dolly: Democratizing the magic of chat- gpt with open models, 2023. URL https://www.databricks.com/blog/2023/03/24/ hello-dolly-democratizing-magic-chatgpt-open-models.html

    work page 2023

Show all 68 references
  1. [9]

    Ultrafeedback: Boosting language models with high-quality feedback, 2023

    Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. Ultrafeedback: Boosting language models with high-quality feedback, 2023

    2023

  2. [10]

    Kto: Model alignment as prospect theoretic optimization

    Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. Kto: Model alignment as prospect theoretic optimization. arXiv preprint arXiv:2402.01306, 2024

    2024 arXiv

  3. [11]

    Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,

    Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfi...

  4. [12]

    Scaling laws for reward model overoptimization

    Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In International Conference on Machine Learning , pages 10835–10866. PMLR, 2023

    2023

  5. [13]

    Deepcore: A comprehensive library for coreset selection in deep learning

    Chengcheng Guo, Bo Zhao, and Yanbing Bai. Deepcore: A comprehensive library for coreset selection in deep learning. In International Conference on Database and Expert Systems Applications, pages 181–195. Springer, 2022. 5

    2022

  6. [14]

    Don’t stop pretraining: Adapt language models to domains and tasks

    Suchin Gururangan, Ana Marasovi´c, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith. Don’t stop pretraining: Adapt language models to domains and tasks. arXiv preprint arXiv:2004.10964, 2020

    2004 arXiv

  7. [15]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021

    2021 arXiv

  8. [16]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas ...

    2023 arXiv

  9. [17]

    Openassistant conversations – democratizing large language model alignment, 2023

    Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and A...

    2023 arXiv

  10. [18]

    What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning

    Wei Liu, Weihao Zeng, Keqing He, Yong Jiang, and Junxian He. What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning. arXiv preprint arXiv:2312.15685, 2023

    2023 arXiv

  11. [19]

    Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models

    Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang. Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models. arXiv preprint arXiv:2108.08877, 2021

    2021 arXiv

  12. [20]

    Training language models to follow instructions with human feedback

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems , 35:277...

    2022

  13. [21]

    Improving language understanding by generative pre-training

    Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training

  14. [22]

    Scaling laws for reward model overoptimization in direct alignment algorithms, 2024

    Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, Bradley Knox, Chelsea Finn, and Scott Niekum. Scaling laws for reward model overoptimization in direct alignment algorithms, 2024. URL https://arxiv.org/abs/2406.02900

    2024 arXiv

  15. [23]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36, 2024

    2024

  16. [24]

    How to train data-efficient llms

    Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H Chi, James Caverlee, Julian McAuley, and Derek Zhiyuan Cheng. How to train data-efficient llms. arXiv preprint arXiv:2402.09668, 2024

    2024 arXiv

  17. [25]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017

    2017 arXiv

  18. [26]

    Hashimoto

    Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca, 2023

    2023

  19. [27]

    Fine- tuning language models for factuality

    Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D Manning, and Chelsea Finn. Fine- tuning language models for factuality. arXiv preprint arXiv:2311.08401, 2023

    2023 arXiv

  20. [28]

    Llama: Open and efficient foundation language models, 2023

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation langua...

    2023 arXiv

  21. [29]

    Llama 2: Open foundation and fine-tuned chat models, 2023

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, W...

    2023 arXiv

  22. [30]

    Rush, and Thomas Wolf

    Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. Zephyr: Direct distillation of lm alignment...

    2023 arXiv

  23. [31]

    Gpt-re: In-context learning for relation extraction using large language models

    Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, and Sadao Kurohashi. Gpt-re: In-context learning for relation extraction using large language models. arXiv preprint arXiv:2305.02105, 2023

    2023 arXiv

  24. [32]

    Pandora’s white-box: Increased training data leakage in open llms

    Jeffrey G Wang, Jason Wang, Marvin Li, and Seth Neel. Pandora’s white-box: Increased training data leakage in open llms. arXiv preprint arXiv:2402.17012, 2024

    2024 arXiv

  25. [33]

    Gpt-ner: Named entity recognition via large language models

    Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, and Guoyin Wang. Gpt-ner: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428, 2023

    2023 arXiv

  26. [34]

    Finetuned language models are zero-shot learners

    Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652, 2021

    2021 arXiv

  27. [35]

    Zero-shot information extraction via chatting with chatgpt

    Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, et al. Zero-shot information extraction via chatting with chatgpt. arXiv preprint arXiv:2302.10205, 2023

    2023 arXiv

  28. [36]

    Xing, Hao Zhang, Joseph E

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. URL https://arxiv.org/ abs/2306.05685

    2023 arXiv

  29. [37]

    Yes" if the interaction contains an informative signal for alignment and

    Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. Lima: Less is more for alignment. Advances in Neural Information Processing Systems, 36, 2024. A Appendix / supplemental material A.1 Alignment Prelimin...

    2024

  30. [39]

    Drinking on an empty stomach can lead to faster absorption of alcohol and more severe hangover symptoms the next day

    Eat before and during your drinking session. Drinking on an empty stomach can lead to faster absorption of alcohol and more severe hangover symptoms the next day. Eating a balanced meal before and during drinking can help slow the absorption of alcohol and reduce the severity ...

  31. [40]

    Different types of alcohol can have varying levels of congeners, which are impurities that can worsen hangover symptoms

    Choose your drinks wisely. Different types of alcohol can have varying levels of congeners, which are impurities that can worsen hangover symptoms. Generally, darker liquors like whiskey and red wine have higher levels of congeners. Clear liquors like vodka and gin tend to hav...

  32. [41]

    Drinking water between alcoholic drinks is a good start, but it’s also important to hydrate throughout the day and night

    Stay hydrated. Drinking water between alcoholic drinks is a good start, but it’s also important to hydrate throughout the day and night. Drinking a glass of water before bed can also help prevent dehydration and alleviate hangover symptoms the next day

  33. [42]

    Alcohol can disrupt your sleep, so getting a good night’s rest can help your body recover and reduce the severity of hangover symptoms

    Get enough sleep. Alcohol can disrupt your sleep, so getting a good night’s rest can help your body recover and reduce the severity of hangover symptoms

  34. [43]

    It’s worth noting that everyone’s tolerance for alcohol and their hangover symptoms can vary, so it’s important to pay attention to how your body reacts and adjust accordingly

    Consider taking an over-the-counter pain reliever like ibupro- fen or aspirin to help alleviate headaches. It’s worth noting that everyone’s tolerance for alcohol and their hangover symptoms can vary, so it’s important to pay attention to how your body reacts and adjust accord...

  35. [44]

    Try to drink at least 8-10 glasses of water a day

    Hydration: Drinking plenty of water throughout the day and before bed can help prevent dehydration, which is one of the main causes of headaches and nausea after drinking. Try to drink at least 8-10 glasses of water a day

  36. [45]

    Try to eat a meal rich in protein and complex carbohydrates before you start drinking

    Eat beforehand: Eating a healthy meal before drinking can slow down the absorption of alcohol into your bloodstream, reducing its effects on your body. Try to eat a meal rich in protein and complex carbohydrates before you start drinking

  37. [46]

    Alternate drinks with water or non-alcoholic beverages: Drinking water or other non-alcoholic beverages between each alcoholic drink can help keep you hydrated and reduce your overall consumption of alcohol, which will reduce its negative effects on your body Random Sampling T...

  38. [47]

    Try to drink a glass of water for every alcoholic beverage you consume

    Stay hydrated: Drinking plenty of water before, during, and after drinking alcohol can help prevent dehydration, which can contribute to headaches and nausea. Try to drink a glass of water for every alcoholic beverage you consume

  39. [48]

    Eat high-quality food: Eating nutritious meals with healthy fats and proteins can help keep your blood sugar levels stable and reduce the likelihood of experiencing low blood sugar or other imbalances that may lead to symptoms like nausea or headaches. Aim for a balanced diet ...

  40. [49]

    This will help prevent dehydration which can contribute to nausea and headaches

    Stay hydrated: Drink plenty of water before, during, and af- ter drinking alcoholic beverages. This will help prevent dehydration which can contribute to nausea and headaches. Electrolytes like sodium and potassium can also help with hydration so consider adding an electrolyte...

  41. [50]

    This can help prevent dehydration and reduce the severity of hangover symptoms

    Stay hydrated: Drink plenty of water before, during, and af- ter drinking alcohol. This can help prevent dehydration and reduce the severity of hangover symptoms. You can also try adding electrolytes to your water by squeezing in some fresh lemon or lime juice or mixing in som...

  42. [51]

    Take vitamins: Some people find that taking certain vitamins before going out helps prevent hangovers or reduces their severity. For example, Vitamin B complex supplements may help by replenishing essential nutrients lost during alcohol consumption, while milk thistle suppleme...

  43. [52]

    Aim for at least 8-10 glasses of water per day, including during your party

    Hydrate: Drinking plenty of water before, during, and after consuming alcohol can help prevent dehydration, which can contribute to nausea and headaches. Aim for at least 8-10 glasses of water per day, including during your party. You can also sip on non-alcoholic beverages li...

  44. [53]

    Eat beforehand: Having a meal before drinking can help slow down the absorption of alcohol into your bloodstream and reduce its effects on your body. Choose foods that are high in protein or complex carbohydrates (e.g., whole grains), as these will help stabilize your blood su...

  45. [54]

    You can do this at a Ministry of Interior office or by mail

    Check the expiration of your Israeli identity card: If your identity card has expired, you will need to renew it as soon as possible. You can do this at a Ministry of Interior office or by mail

  46. [55]

    This can be done online or at a local municipal office

    Update your address: If you have a new address in Israel, you will need to update your address with the Ministry of Interior. This can be done online or at a local municipal office

  47. [56]

    This can be done online or at a local municipal office

    Register your foreign residence: If you have lived outside of Israel for an extended period, you may be required to register your foreign residence with the Ministry of Interior. This can be done online or at a local municipal office

  48. [57]

    Apply for an Israeli tax number: You will need to apply for an Israeli tax number (available at the Israeli tax authority website) to prove your residency and file taxes

  49. [58]

    You can register for National Insurance at a local municipal office or online

    Register with National Insurance: As a returning resident, you may be eligible for National Insurance benefits. You can register for National Insurance at a local municipal office or online. It is important to note that this list of steps may not be compre- hensive, and the sp...

  50. [59]

    If you do not have an Israeli passport, apply for one through the Ministry of Foreign Affairs website or at an Israeli embassy/consulate near you

    Obtain your passport: Ensure that your Israeli passport is valid and up-to-date, as it will be required for re-entry into the country. If you do not have an Israeli passport, apply for one through the Ministry of Foreign Affairs website or at an Israeli embassy/consulate near you

  51. [60]

    Make sure your Aliyah (immigration) file is active: If you made Aliyah (immigrated) to Israel within the last seven years, make sure that your immigration file with the National Population Registry Authority (NPRA) remains active by informing them of any changes in address or ...

  52. [61]

    However, with the right guidance and preparation, it can be a straightforward journey back home

    Register with Magen David Adom (MDA): You will need a MDA card which acts as a national health insurance card when returning to Israel from abroad; register online on MDA’s website before leaving on your trip back home if possible; otherwise sign up upon arrival at Ben Gurion ...

  53. [62]

    Gather your documents: Before beginning the process of re- establishing your residency in Israel, ensure that you have all necessary documents ready. These may include your passport or national ID card (teudat zehut), birth certificate, marriage certificate (if applicable), an...

  54. [63]

    This type of visa grants new immigrants certain benefits such as subsidized housing options and tax exemptions during their first 10 years in the country [2]

    Apply for a visa: If you have been living outside of Israel for more than five years or if you plan on staying in Israel for longer than three months within one year after arrival (regardless of how long ago your initial departure was), then applying for an Oleh Chadash visa i...

  55. [64]

    Israeli Citizenship and Residence: If you are an Israeli citizen, you do not need to apply for a visa or residence permit to enter Israel

    Register with local authorities upon arrival: Once arriving back into Israeli territory make sure that all family members register themselves at their nearest municipality office within seven days fol- lowing entry into country using existing teudat zehut cards if available; o...

  56. [65]

    If necessary, renew your passport before traveling back to Israel by contacting the relevant embassy or consulate of your country of citizenship abroad

    Passports: Check Your Expiration Date: Before returning to Israel, make sure that your passport is valid for at least 6 months from the date of arrival in Israel and has sufficient blank pages for entryexit stamps (at least two blank pages). If necessary, renew your passport b...

  57. [66]

    If you do not have an Israeli identity card, you may need to apply for one at an embassy or consulate in your country of residence

    Preparation: Gather all necessary documents, such as your Is- raeli identity card (teudat zehut), passport, and any relevant immigration documents (e.g., B-1 visa). If you do not have an Israeli identity card, you may need to apply for one at an embassy or consulate in your co...

  58. [67]

    Make sure your travel documents are valid and up-to-date before booking the flight

    Booking a flight: Purchase a ticket for a direct or connecting flight that will take you back to Israel. Make sure your travel documents are valid and up-to-date before booking the flight

  59. [68]

    Travel arrangements: Plan your transportation from the air- port in Israel to your final destination in the country, whether it’s by taxi, public transport, or private vehicle rental services like UberLyftGettMyCarsRental). You may also want to consider purchasing travel insur...

  60. [2022]

    URL https://arxiv.org/abs/2209.07858

    arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.