text string | source string |
|---|---|
human feedback in llms. arXiv preprint arXiv:2402.14740 , 2024. [2]Eden Biran, Daniela Gottesman, Sohee Yang, Mor Geva, and Amir Globerson. Hopping too late: Exploring the limitations of large language models on multi-hop queries. arXiv preprint arXiv:2406.12775 , 2024. [3]Peter Clark, Isaac Cowhey, Oren Etzioni, Tusha... | https://arxiv.org/abs/2505.18454v1 |
Zhenrui Yue, Dong Wang, Hamed Zamani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516 , 2025. [19] Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. TriviaQA: A large scale distantly supervised challenge dataset fo... | https://arxiv.org/abs/2505.18454v1 |
Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 , 2024. [34] Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, and Jiuxiang Gu. Efficient reasoning with hidden thinking. arXiv p... | https://arxiv.org/abs/2505.18454v1 |
Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang, Yang Zhang, and Dong Wang. Evidence- driven retrieval augmented response generation for online misinformation. arXiv preprint arXiv:2403.14952 , 2024. [51] Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, and... | https://arxiv.org/abs/2505.18454v1 |
prepending a system message that directs the LLM to perform step-by-step internal reasoning before generating its final answer. The user query is then appended, and the entire input is formatted with the model chat template. Different from 2https://github.com/unslothai/unsloth 14 prior work [ 10,18], we adopt the minim... | https://arxiv.org/abs/2505.18454v1 |
a question, and the assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The final answer is provided after the #### tag, i.e., {reasoning process} #### {answer}.<|im_end|> <|im_start|>user Question: Two people are pushing a car. One person ... | https://arxiv.org/abs/2505.18454v1 |
Qwen 3B backbone. Init RangeKnowledge NQ TriviaQA HotpotQA 2WikiMQA Bamboogle Average [0.95 - 0.999] 0.845 0.613 0.622 0.576 0.820 0.695 [0.98 - 0.999] 0.842 0.600 0.614 0.585 0.813 0.691 [0.99 - 0.999] 0.838 0.606 0.630 0.590 0.817 0.696 Init RangeSTEM GSM8k MATH MATH500 MMLU-ST ARC-C Average [0.95 - 0.999] 0.367 0.59... | https://arxiv.org/abs/2505.18454v1 |
20, Figure 21, where as the mistakes are provided in Figure 22, Figure 23, Figure 24, Figure 25, Figure 26, we show the raw strings and omit the options / contexts in the examples due to space constraints. From these examples, we identify four reasoning patterns that can lead to correct answers: (1) Purely English reas... | https://arxiv.org/abs/2505.18454v1 |
Query: Which religion uses a domed structure called a Stupa as a shrine?HRPO (Tokens in Hybrid Rollout): To determine which religion uses a domed structure called a Stupa as a shrine, let's consider the provided context and the information we need to extract:\n\n- Stupa is described as a \"mound-like or hemispherical s... | https://arxiv.org/abs/2505.18454v1 |
exponent rule %%((-a)ˆ{-1} = -aˆ{-1}%%) but )%nI am_____ (leave the word in the blank) and the study of music will be called ____. {reasons} to____. For this, the Rhythm Method is the only way to teach music.%n{reasoning process} #### To understand the reasoning process behind the given problem, let’s break down the co... | https://arxiv.org/abs/2505.18454v1 |
by 3, so 1391 is not divisible by 3. It is not divisible by 5 either, but it is divisible by 7: 1391 ÷ 7 = 198.3571. The product of the digits of 1391 is 1 × 3 × 9 × 1 = 27, which is divisible by 3, so 1391 is divisible by 3. Therefore, 1391 = 3 × 463. %n%nNow we have the prime factorization of 1391 as 1391 = 3 × 463. ... | https://arxiv.org/abs/2505.18454v1 |
\\(x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}\\).\n\nHere, the non-integer root \\(n\\) is the root closest to but not equal to an integer.\n\nSo, the non-integer root \\(n\\) in the equation, \\(n = \\frac{9 \\pm \\sqrt{81 - 100}}{10}\\), which simplifies to \\(n = \\frac{9 \\pm \\sqrt{-19}}{10}\\).\n\nHowever, \\( \\s... | https://arxiv.org/abs/2505.18454v1 |
arXiv:2505.18456v1 [cs.CL] 24 May 2025Anchored Diffusion Language Model Litu Rout Constantine Caramanis Sanjay Shakkottai The University of Texas at Austin {litu.rout,constantine,sanjay.shakkottai}@utexas.edu Abstract Diffusion Language Models (DLMs) promise parallel generation and bidirectional context, yet they under... | https://arxiv.org/abs/2505.18456v1 |
low-frequency or semantically important words) are masked early in the forward process, the model lacks sufficient context to accurately reconstruct the original sequence. Drawing on information-theoretic insights and improved sample complexity via anchoring in directed graphical models (DAGs), we propose the Preprint.... | https://arxiv.org/abs/2505.18456v1 |
demonstrate the benefits of anchoring using two different samplers: (a) locked-in (Sahoo et al., 2024) and (b) remasking (Wang et al., 2025a) samplers. With remasking sampler, ADLM outperforms AR models in human-like text generation measured by MAUVE score (§5.1). •Beyond diffusion, we integrate our anchoring mechanism... | https://arxiv.org/abs/2505.18456v1 |
=QL l=1pθ(zl s|zt). Intuitively, given a noisy latent zt, the model predicts a clean token and then re-noises it forward according to the forward dynamics defined in (1). Recall that xdenotes a sequence of K-dimensional one-hot encoded tokens, i.e., x= (xl)L l=1. We slightly overload notation and use xθ= (xl θ)L l=1to ... | https://arxiv.org/abs/2505.18456v1 |
We propose to break the one-step denoising process, widely used in practice (Austin et al., 2021; Sahoo et al., 2024; Wang et al., 2025a; Ou et al., 2025; Nie et al., 2025a,b), into a two-stage anchored denoising framework. This allows latent reasoning over important tokens during pretraining. Since the reverse process... | https://arxiv.org/abs/2505.18456v1 |
Evidence Lower Bound (ANELBO) (seeTheorem 4.1 ): LANELBO (x,y;φ, ψ) =EZ0∼q(·|x)[−logpψ(x|yφ(Z0))] + (7) TX i=1EZt(i)∼q(·|x)" (1−σt(i))αt(i)−αs(i) 1−αt(i)LX l=1log⟨xl ψ(yφ(Zt(i))),xl⟩+γlog⟨yl φ(Zt(i)),yl⟩# , where γcontrols anchor strength. For σt(i)= 0 = γ, we recover the standard MDLM (4). Anchor Token Selection. We i... | https://arxiv.org/abs/2505.18456v1 |
AR and DLM training as learning in directed graphical models (DAGs) and formally analyze our anchoring mechanism. While rooted in classical theory, we demonstrate that anchoring yields practical benefits in both large-scale pretraining (§5.1) and supervised fine-tuning (§5.2) tasks. Assumption 4.3. Suppose the followin... | https://arxiv.org/abs/2505.18456v1 |
ReMDM (Wang et al., 2025a), which allows re-masking with a small σt̸= 0. For fair comparison, we adopt the exact sampler configurations used in the respective baseline implementations. Baselines. We compare against the following baselines: (1) the Autoregressive (AR) architecture from (Sahoo et al., 2024) trained with ... | https://arxiv.org/abs/2505.18456v1 |
524B tokens. At each scale, ADLM consistently outperforms diffusion-based baselines such as MDLM and GIDD, as well as the hybrid (AR+Diffusion) BD3LM. Notably, at 262B tokens, ADLM achieves a PPL of 20.62, narrowing the gap with the AR models, which reach a PPL of 17.54. ADLM∗uses our multi-stage design (anchor and den... | https://arxiv.org/abs/2505.18456v1 |
T=128 T=256 T=512 SEDD (absorb) 0.007 0.007 0.008 119.2 110.1 107.2 5.65 5.63 5.62 MDLM 0.015 0.023 0.031 61.5 55.8 53.0 5.52 5.49 5.48 MDLM+FB 0.064 0.084 0.100 42.8 39.6 37.1 5.44 5.41 5.38 MDLM+DFM 0.041 0.144 0.211 37.9 26.5 23.3 5.31 5.26 5.23 ReMDM 0.057 0.216 0.350 42.5 30.5 21.1 5.43 5.34 5.21 ADLM (ours) 0.140... | https://arxiv.org/abs/2505.18456v1 |
arithmetic reasoning, (2) ProntoQA (Saparov & He, 2023)–rule-based logical reasoning, and (3) ProsQA (Hao et al., 2024)– planning with structured reasoning over graph-based inference traces. Our experimental setup follows the fine-tuning protocols outlined in prior work (Hao et al., 2024), enabling direct comparison wi... | https://arxiv.org/abs/2505.18456v1 |
language modeling by leveraging anchor tokens (e.g., low-frequency or important key words). We provide theoretical justification along with strong empirical evidence supporting our results. Our method bridges the gap between diffusion and AR models in likelihood modeling and generated text quality. ADLM significantly r... | https://arxiv.org/abs/2505.18456v1 |
Levy, and Christopher D Manning. What does bert look at? an analysis of bert’s attention. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP , pp. 276. Association for Computational Linguistics, 2019. URL https://aclanthology.org/W19-4828/ . Karl Cobbe, Vineet Kosara... | https://arxiv.org/abs/2505.18456v1 |
Proc. Workshop on Pattern Recognition in Practice, 1980 , 1980. Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. An introduction to variational methods for graphical models. Machine learning , 37:183–233, 1999. Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. Gen... | https://arxiv.org/abs/2505.18456v1 |
The Thirteenth International Conference on Learning Representations , 2025. URL https://openreview.net/forum?id=sMyXP8Tanm . Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The lambada dataset: Word prediction r... | https://arxiv.org/abs/2505.18456v1 |
Lluís Màrquez (eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pp. 4593–4601, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1452. URL https://aclanthology. org/P19-1452/ . Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier... | https://arxiv.org/abs/2505.18456v1 |
>1is straightforward following the standard analysis (Sohl-Dickstein et al., 2015). We start from the standard negative log-likelihood: −logpθ(x) =−logZ pθ(x, Z0, . . . , Z 1)d(Z0, . . . , Z 1) =−logZpθ(x, Z0:1) q(Z0:1|x)q(Z0:1|x)d(Z0:1). Applying Jensen’s inequality yields: −logpθ(x)≤Eq(Z0:1|x)" −logpθ(x|Z0) + logq(Z1... | https://arxiv.org/abs/2505.18456v1 |
bound on the data manifold. When the anchor and denoising networks are properly aligned with the inference posterior, the KL terms decompose nicely under our parameteri- zation. This leads to a variational bound that reflects both token-level reconstruction and anchor-level guidance, enabling effective learning in larg... | https://arxiv.org/abs/2505.18456v1 |
distribution p(xl|·)is modeled as a categorical distribution. •The model is parameterized by Conditional Probability Tables (CPTs); that is, a distinct parameter is assigned to each possible configuration of the conditioning set. •Anchor sets πl⊂ {1, . . . , L } \ {l}are fixed and of bounded size |πl| ≤d, with d≪L. Pro... | https://arxiv.org/abs/2505.18456v1 |
is to estimate the parameters of a conditional probability model p(x|θ), where θ= [ψ, φ], using MLE. The standard MLE objective is defined as: LMLE(ψ, φ) =NX i=1logp(xi|ψ, φ). Since the structure of the underlying graphical model Gis typically unknown, a common modeling assumption is to use a fully autoregressive facto... | https://arxiv.org/abs/2505.18456v1 |
this work, we adopt a simple yet effective information-theoretic strategy: tokens with low marginal frequency in the given sample tend to carry more information (§3). Hence, we treat such low-frequency tokens as candidates for anchoring in language modeling tasks. Our empirical results support this strategy across two ... | https://arxiv.org/abs/2505.18456v1 |
first decode the important tokens before reconstructing the rest of the sequence. Summary. By leveraging inductive biases about the distribution of important tokens within sequences, our anchoring approach achieves substantial improvements in sample complexity. Crucially, it avoids the combinatorial explosion of full-c... | https://arxiv.org/abs/2505.18456v1 |
A.6 guarantees that the anchored EM procedure produces non-increasing negative log-likelihood at each iteration, thereby ensuring stability and convergence to a first-order stationary point. Notably, this convergence behavior emerges even though the anchor tokens are unobserved (Kwon et al., 2024); they are estimated i... | https://arxiv.org/abs/2505.18456v1 |
on the last term: X yr(y|x, φi) logp(y|x, ψi+1, φi+1) p(y|x, ψi, φi) ≤logX yr(y|x, φi)p(y|x, ψi+1, φi+1) p(y|x, ψi, φi) = logX yp(y|x, ψi+1, φi+1) = log 1 = 0 , where we use the fact that r(y|x, φi) =p(y|x, ψi, φi)and that p(·|x,·,·)is a valid probability distribution. Monotonicity. Thus, we conclude: −logp(x|ψi+1,... | https://arxiv.org/abs/2505.18456v1 |
, l∈ {1,2,3} (13) Here, zl tdenotes the corrupted version of token xlat time t, and Cat(·)denotes the categorical distribution over the vocabulary V={0,1, m}. The vector xl∈R3is a one-hot column vector corresponding to the original token xl, andm= [0,0,1]⊤is the one-hot vector for the mask token. To make this concrete,... | https://arxiv.org/abs/2505.18456v1 |
1/3,z2 1/3,z3 1/3 respectively. We now compute the likelihood of decoding the target partial sequence (m,0,m), i.e., correctly unmasking the important token: pθ(z1/3= (m,0,m)|z2/3= (m,m,m)) = (0 .5)·(0.45)·(0.5) = 0 .1125. This probability reflects the chance of correctly decoding only the important token using the sta... | https://arxiv.org/abs/2505.18456v1 |
generative modeling (§5.1) and complex reasoning benchmarks (§5.2) in the main draft (§5) and also in the Appendix C. We hope it offers a compelling justification for the use of anchoring as a general framework for language modeling. B Additional Background and Related Works In this section, we provide extended backgro... | https://arxiv.org/abs/2505.18456v1 |
that gradually introduces continuous latent “thoughts” between the question and answer. When integrated with our anchoring mechanism, this approach also yields strong performance on symbolic reasoning benchmarks like ProsQA (see §C.2). CODI (Continuous Chain-of-Thought via Self-Distillation). CODI (Shen et al., 2025) d... | https://arxiv.org/abs/2505.18456v1 |
and autoregressive models in §C.2. We present benchmark datasets, training procedure, ablation studies, and additional quantitative and qualitative results to support the findings discussed in the main paper. Broader Impact. This work introduces an anchoring framework that improves likelihood modeling and generation qu... | https://arxiv.org/abs/2505.18456v1 |
and uniform noising in discrete diffusion models. As a concurrent work to ReMDM, it addresses a key limitation of MDLM (i.e., the inability to revise tokens once unmasked) by allowing previously unmasked tokens to be updated during inference. This flexibility enables the model to iteratively correct its own mistakes an... | https://arxiv.org/abs/2505.18456v1 |
We measure the perplexity on the validation sets of the following benchmarks: •Lambada (Paperno et al., 2016): This benchmark evaluates the ability of language models to predict a target word based on a broad context. Each example consists of a short narrative (context ) followed by a target sentence with its final wor... | https://arxiv.org/abs/2505.18456v1 |
in Algorithm 1 , which employs the standard locked-in sampler (Sahoo et al., 2024) with a remasking schedule σt(Wang et al., 2025a). The locked-in sampler is a special case obtained by setting σt= 0. Implementation Details for OWT. As OWT dataset does not provide an official train/test split, we use the splits used in ... | https://arxiv.org/abs/2505.18456v1 |
configuration, the training loss and validation PPL per iteration is shown in Figure 2. Implementation Details for LM1B. The experimental setup follows prior works (Sahoo et al., 2024; Wang et al., 2025a). For LM1B, we follow the same setup as OWT, except a shorter sequence length of 128 tokens and the BERT-base-uncase... | https://arxiv.org/abs/2505.18456v1 |
and outperforming BERT-Large+Gibbs while using at least 40M fewer parameters. This indicates that anchoring enables more efficient denoising compared to prior diffusion language models. Moreover, ADLM demonstrates a consistent reduction in perplexity as the number of sampling steps increases, validating the role of ite... | https://arxiv.org/abs/2505.18456v1 |
was everyone else.” Probst Steve Watson. Never as good as our pundit, we’ll repair the damage we’ve done! Tell a Random Story A large facet of the new season will be to tell a random story, not sure if it’s how the players are portrayed. But it’s fairly obvious that this specificity will be replaced by community. The p... | https://arxiv.org/abs/2505.18456v1 |
school group on the controversial side of history. Now Jay and the photographer will have an unique way of representing the tribe while keeping the story relevant. Though the cast will have something to do with it (gohan, skinnyfoots), there will be some mismatched players who generally won’t fill up the ample, talente... | https://arxiv.org/abs/2505.18456v1 |
participant had to remain in cruel conditions, including medical experimentation, electric lashings, etc., while pregnancy rates were very low[ [ ]. The sterilized prisoners were not only much more likely to have babies than the prisoners with no pregnancy; they were less likely to have abortions. Researchers found tha... | https://arxiv.org/abs/2505.18456v1 |
we use 4096 sampling steps for ADLM with remasking sampler. The generated text demonstrates strong discourse-level coherence, well-structured paragraphs, and natural transitions between topics. For instance, the model begins with a detailed sports commentary and transitions smoothly into a socio-political news report. ... | https://arxiv.org/abs/2505.18456v1 |
down high and out of bounds. 37 Oklahoma City’s bench never stopped suiting as the game went on. This is a team that is making a leap in the NBA, with DeMarcus Cousins and Chauncey Billups leading the way, and it’s just the right thing to do in such a crazy situation.<|endoftext|>(CN) Hundreds of Seattle residents Tues... | https://arxiv.org/abs/2505.18456v1 |
Pulfres; Erie it’s Duane Wright than Vin Diesel) and he’s the current fourth student at the entire campus, as far as entrance per pupil is well. According to Wright, Shaw was about to thirsty with traders celebrating with an invented flavoral beer and the extra muggers when he approached the team with a shot on Irving,... | https://arxiv.org/abs/2505.18456v1 |
understood that the load followed the closure of the university’s reserve vice chancellor subsequently who died of Parkinson’s syndrome as his wife’s tombstone was on fire. Catherine Baird of Parliamentary Coalition for Government (Scrabble) leader Lassie Mann joined further the condemnation of the stalls. Ms Baird sai... | https://arxiv.org/abs/2505.18456v1 |
the standard next-token prediction objective, but conditioned on the output logits from the anchor network. Rather than sampling tokens and re-embedding them, we project the anchor logits through a linear layer and feed the resulting representations directly into the LLM transformer, similar to the ADLM setup discussed... | https://arxiv.org/abs/2505.18456v1 |
the AR setting. In this setting, the model does not have access to structured reasoning traces as in the Math and Logic benchmarks described below. The training and validation splits follow the same setup used in the diffusion experiments (§C.1.2). Math (GSM8K). The GSM8K dataset (Cobbe et al., 2021) contains grade-sch... | https://arxiv.org/abs/2505.18456v1 |
traces contain redundant information, increasing entropy and making the reasoning process harder to learn. By supervising the model through a small set of important tokens extracted from the reasoning trace, ACoT encourages more structured intermediate computations, guiding the model to reason in a more targeted and in... | https://arxiv.org/abs/2505.18456v1 |
Test perplexities (PPL; ↓) for standard AR models and our anchored variant (A2R) at various training scales.†Results from (Sahoo et al., 2024). A2R consistently improves perplexity by introducing a two-stage prediction process: anchor tokens are first predicted, then used to guide next-token prediction. Model PPL ( ↓) ... | https://arxiv.org/abs/2505.18456v1 |
16 - 3 - 4 = 9 , and then 9 * 2 = 18 ,following the order in which quantities appear in the question . In contrast, ACoT introduces [ANT] to capture important tokens, which allows it to reason more globally. Specifically, ACoT first computes 3 + 4 = 7 to aggregate all consumption before subtracting from the total ( 16 ... | https://arxiv.org/abs/2505.18456v1 |
that gradually removing reasoning steps during multi-stage training provides a significant performance boost on ProsQA. Following this recommendation, we integrate reasoning step removal into our ACoT fine-tuning. This modification improves our model’s accuracy from 81% to 97.3%. Importantly, ACoT generates much less t... | https://arxiv.org/abs/2505.18456v1 |
meal of the day if the size of Wendi’s flock is 20 chickens? CoT «3*20=60» «60-15-25=20» Answer 20 Full Output [ANT][ANT] «15+25=40» «40-20=20» Extracted Output 20 45 Table 10: Examples of logical reasoning tasks with symbolic reasoning traces from Pron- toQA (Saparov & He, 2023). Each row shows the input question, gro... | https://arxiv.org/abs/2505.18456v1 |
al., 2022) 77.5 ±1.9 49.4 iCoT (Deng et al., 2024) 98.2±0.3 8.2 COCONUT†(Hao et al., 2024) 97.0 ±0.3 14.2 - Pause†96.6±0.8 8.2 ACoT (ours) 97.3±0.2 8.2 47 Table 12: Examples of logical reasoning tasks with symbolic reasoning traces from ProsQA (Hao et al., 2024). Each row shows the input question, groundtruth reasoning... | https://arxiv.org/abs/2505.18456v1 |
is a impus. Every jompus is a gerpus. Every boompus is a rompus. Fae is a boompus. Every boompus is a kerpus. Every zumpus is a bompus. Max is a rempus. Every rompus is a kerpus. Max is a impus. Every rempus is a impus. Every wumpus is a yumpus. Every grimpus is a terpus. Every tumpus is a jompus. Every yumpus is a fel... | https://arxiv.org/abs/2505.18456v1 |
arXiv:2505.18458v2 [cs.DB] 27 May 20251 A Survey of LLM×DATA Xuanhe Zhou∗¶, Junxuan He∗¶, Wei Zhou∗¶, Haodong Chen∗¶, Zirui Tang∗¶, Haoyu Zhao∗¶, Xin Tong∗, Guoliang Li†, Youmin Chen∗, Jun Zhou∗, Zhaojun Sun∗, Binyuan Hui‡, Shuo Wang†, Conghui He§, Zhiyuan Liu†, Jingren Zhou‡, Fan Wu∗ ∗Shanghai Jiao Tong University†Tsi... | https://arxiv.org/abs/2505.18458v2 |
LLMs ’ full potential in these applications (DATA4LLM ). It includes efficient and scalable solutions for data processing, storage, and serving across the LLM lifecycle, as evidenced in recent academic studies [159], [287], [256] and industry reports [329], [442], [69], [39]. Conversely, LLM -powered techniques are inc... | https://arxiv.org/abs/2505.18458v2 |
with com- plex data samples [245], [78], [74]. For instance, standardizing date formats (e.g., “Fri Jan 1st 10:36:28 2021” vs. “1996.07.10AD at 15:08:56”) or resolving textual inconsistencies (e.g., “Monticello VA, Jasper” vs. “Monticello VAA”) typically requires intricate programming scripts or handcrafted con- strain... | https://arxiv.org/abs/2505.18458v2 |
such as complex layout analysis [204], [18], [399], [182], [398], [415], [259], [328], [414]. •Data Deduplication. Data deduplication aims to identify du- plicates in large-scale textual or multi-modal data, including exact string matching [123], [301], hash identification [88], [81], [123], [301], [352], [363], [209],... | https://arxiv.org/abs/2505.18458v2 |
Data Storage for LLMs ( §2.3). We review data storage techniques for LLMs from the following main aspects. •Data Formats. We review commonly-used dataset and model data formats for LLMs . Dataset formats include TFRecord [44], MindRecord [40] for multimodal data, and tf.data.Dataset that can be directly fed into LLMs [... | https://arxiv.org/abs/2505.18458v2 |
and re-ranking [129], [12], [320], [47]. •LLM Data Compression. LLM data compression aims to compress the model’s input data to stay within the context window limit or to facilitate model understanding. Relevant techniques include: (1) RAG knowledge compression (e.g., rule-based [436], [353], [202] and model-based meth... | https://arxiv.org/abs/2505.18458v2 |
queries with GQL generation (e.g., R3-NL2GQL [501]) and knowledge-aware QA by retrieving or reasoning over relevant subgraphs [433]. •Semi-Structured Data Analysis. Meanwhile, handling semi- structured data (e.g., JSON and spreadsheets) remains chal- lenging. Recent benchmarks (e.g., TEMPTABQA [167] and SPREADSHEETBENC... | https://arxiv.org/abs/2505.18458v2 |
This is a list of characters A Aglain t... Agravaine... C4 14.8% 74.2% 2.3% 5.0% 1.7% Data Sources (More than 1T Unlabeled samples) The match between Manchester City and AFC Bournemouth ended with ... Lung cancer is a type of malignant tumor that originates in the lungs.... Q: Is there anyway I can get this to keep the... | https://arxiv.org/abs/2505.18458v2 |
•Weintroduce recent advances in LLM4DATA , outlining key components of LLM -driven data optimization. While earlier work [496] has investigated the application of classical ma- chine learning in data management, it largely neglects the distinctive strengths and limitations of LLMs , particularly in manipulating data fo... | https://arxiv.org/abs/2505.18458v2 |
Law (SFT)[456], (d) Law (Eval)[116], (e) Code (SFT) [296], (f) Code (Eval)[210]. in these characteristics across stages, distinct techniques for data processing, storage, and serving are required (Table 1). Data for Pretraining. In the pre-training stage, LLMs rely on TB-scale, diverse datasets to acquire broad languag... | https://arxiv.org/abs/2505.18458v2 |
Similarly, Medical-SFT [438] is a medical SFT dataset (totaling 2,060k pieces), composed of medical inquikry data (790k), online medical encyclopedia QA data (360k), English medical in- quiry data (110k), medical knowledge graph QA data (79k). For tasks such as legal question-answering and legal judgment prediction, th... | https://arxiv.org/abs/2505.18458v2 |
more than 800 national and local laws, regulations, and rules, as well as 24,000 legal-related exam questions. Besides, RAG data can include users’ historical conversation records or personal information, in order to build a user-personalized LLM [355], [460], [462]. Data for LLM Evaluation. Suitable evaluation dataset... | https://arxiv.org/abs/2505.18458v2 |
data for sentiment analysis and sentence sim- ilarity estimation), data acquisition for LLMs typically (1) relies on large-scale web scraping to collect extensive data across diverse domains for unsupervised pretraining and (2) employs techniques such as layout analysis and entity linking to extract additional data fro... | https://arxiv.org/abs/2505.18458v2 |
hand-crafted rules (e.g., match HTML DOM nodes with the class equal to “navbar” to filter the navigation bar). BET [145] employs the cumulative HTML tag distribution to find the largest region of fewest tags per text and extracts the corresponding text as the main content. •ML-based Crawling. Since many website regions... | https://arxiv.org/abs/2505.18458v2 |
Sandbox Dataverse CC_Cleaner MDR LP Model-specific Pipelines Inference Stage Requirement Safe Content Content Privacy Exact Substring Matching Hash Identification Embedding- based Clustering Frequency Analysis Similarity-Based Selection Optimization-Based Selection Lexicon Set Overlap Bayes-based Selection Kernel Densi... | https://arxiv.org/abs/2505.18458v2 |
English and other languages), Alignment-Augmented Consistent Translation (AACTRANS) model [217] uses a Seq2Seq framework that 9 TABLE 3: Data Deduplication for LLMs . Method Objective Modality Work Exact substring matchingDeduplicate samples with identical substringsTextMD5 [123] Suffix Array [301] Hashing identificati... | https://arxiv.org/abs/2505.18458v2 |
with identical MD5 values. •Sentence-Level. [301] performs sentence-level deduplication by using Suffix Array, which combines all the samples into one sentence, computes the sentence Suffix Array, and dedu- plicates samples with common prefixes in the Suffix Array. Suffix Array [285] is a data structure that stores the... | https://arxiv.org/abs/2505.18458v2 |
for each sample depends on this shared vocabulary, it is difficult to fully parallelize the process. •SimHash [88]. To address MinHash’s issues, SimHash [88] generates a sample’s feature vector solely from the words it contains , converts each sample into a fixed-dimensional binary vector for similarity comparison. Spe... | https://arxiv.org/abs/2505.18458v2 |
each other in the vector space) for deduplication. SemDeDup [46] identifies semantic duplicates by cluster- ing embeddings and deduplicating those with high cosine similarities. It first encodes each sample into an embedding by leveraging the OPT [471] text encoder and the CLIP [327], [184] image encoder, and clusters ... | https://arxiv.org/abs/2505.18458v2 |
= 0.7 No Previous tokens Previous tokens Next token Next token IFD Score IFD Score Average Inter- Average Inter- cluster Distance cluster Distance Average Intra- Average Intra- cluster Distance cluster Distance Original Dataset Filtered Dataset Score the (enhanced) samples Train High Low Original Dataset Filtered Datas... | https://arxiv.org/abs/2505.18458v2 |
data selection. To enhance efficiency, [61] leverages a smaller-sized surro- gate model to select high-quality pre-training subsets via per- plexity score for training larger-sized models, greatly reducing the computational overhead in model training while still achieving the same performance as with the full dataset. ... | https://arxiv.org/abs/2505.18458v2 |
work [241], the adoption of sur- rogate model simplifies the procedure and accelerates the filtering process. •Influence Assessment. Another data filtering approach is to assess the influence of a sample on LLM model performance or learning process by measuring how the metrics change when the sample is upweighted or re... | https://arxiv.org/abs/2505.18458v2 |
the two distances C=dintra×dinter. The cluster complexity is later converted to probability using softmax to resample the samples across clusters, where clusters with higher complexity have higher weights. Rather than the sample embedding itself, SmallTo- Large [445] selects a diverse subset by clustering the samples b... | https://arxiv.org/abs/2505.18458v2 |
these methods or parameters to find the best combination of methods or parameters that further boosts model performance. [287] selects high-quality pre-training data based on three metrics: (i) Perplexity, ( ii) EL2Nχ(xi,yi) =E∥f(xi)−yi∥2 for measuring the prediction probability discrepancy between the reference model ... | https://arxiv.org/abs/2505.18458v2 |
(e.g, user identity details or clinical health data) from datasets during pre-training and fine-tuning, which can be leaked through specially crafted prompts, thereby posing significant privacy risks. [277] demonstrates that it is possible to extract, reconstruct, and in- fer personally identifiable information (PII) f... | https://arxiv.org/abs/2505.18458v2 |
well- cleaned data samples in order to adapt LLMs to specific domains (e.g., medical or legal LLMs ). Principles Unlike traditional ML data selection, LLM data selec- tion focuses on aligning the topics of the text samples, requiring encoding semantic topics into measurable distributions. However, managing computationa... | https://arxiv.org/abs/2505.18458v2 |
datamodel selects the subset Sof the size kthat minimizes the loss ˆLDtarg(S) =1 n/summationtextn i=1τθxi(1S) for the target task. •Gradient-Influence Search. Low-rank Gradient Similarity Search (LESS) [425] identifies the most impactful subset of data for fine-tuning LLMs by analyzing gradient simi- larities. It first... | https://arxiv.org/abs/2505.18458v2 |
mixing ensures that the model captures broad generalization capabilities while balancing performance across tasks and domains [141]. Existing data mixing methods can be classified into two main categories:Principles Unlike traditional ML models like BERT (trained on smaller, domain-specific data with homogeneous distri... | https://arxiv.org/abs/2505.18458v2 |
like LLaMA-3. ScaleBiO initialize the weights equably for all data sources. In each iteration, it randomly selects a subset of data sources to update their weights: for the selected data sources, it adjusts the weights by optimizing the gradient of the validation loss, prioritizing the increase of weights for data that... | https://arxiv.org/abs/2505.18458v2 |
weights are normalized to form a new sampling distribution and repeat the process to get final data distribution. Model-Based Optimization. This category of methods design linear or non-linear models that depict ( i) the relation between the distribution of each domain, ( ii) validation loss, and (iii) some other varia... | https://arxiv.org/abs/2505.18458v2 |
adjust weights by Multiple applications like multilingual training, gradient alignment values [304] instruction following, large-scale data reweighting Distributionally Robust OptimizationPre-training Group DRO [429]Only need one proxy model compared to [280] which uses a proxy model and a reference model Fine-tuning T... | https://arxiv.org/abs/2505.18458v2 |
mixing ratio that balances be- tween (1) significantly reducing domain loss while (2) keeping the increase in general loss within a pre-defined tolerance range. Based on the two aspects, the ratio is defined as R∗= max{R|R∈F}, whereRis the ratio of generic dataset and domain-specific dataset, Fis feasible mixture ratio... | https://arxiv.org/abs/2505.18458v2 |
steps via semantic alignment scoring (e.g., cosine similarity) to prevent error propagation. PaD replaces flawed CoT steps with verifiable program logic, enhancing small models’ rea- soning robustness through code-based distillation and self- correction mechanisms. •Multi-stage Collaboration Distillation Between Studen... | https://arxiv.org/abs/2505.18458v2 |
to different styles of texts like Q&A or concise defini- tion. WRAP [284] leverages instruction-tuned models (e.g., Mistral-7B) to rephrase web text (C4) into four formats: (i) simple vocabulary and sentence structures that are under- standable to young children. (ii) Standardized encyclopedia- style expression. (iii) ... | https://arxiv.org/abs/2505.18458v2 |
benchmarks like HellaSwag [458] and ARC-Challenge [143]. •LLM Prompting for Multimodal Image-Text Synthesis. Cur- rent approaches for synthesizing multimodal pre-training data typically employ two main approaches: (1) the generation of images conditioned on textual input using text-to-image models, and (2) the augmenta... | https://arxiv.org/abs/2505.18458v2 |
theme co-occurrence probabilities to guide logical problem generation. GPT-4 synthesizes new questions based on these themes and solutions, which are evaluated for quality (clarity, coherence) and refined via GPT-4 voting. The method further diversifies questions through variations and applies iterative voting to optim... | https://arxiv.org/abs/2505.18458v2 |
formal solutions are verified using mathematical proof tools to ensure the correctness of the reasoning and answers. For content that fails verification, the model adjusts based on feedback and re-verifies until a correct result is generated. •CoT Data Synthesis By LLM Exploring. Works mentionedabove highly rely GPT-4 ... | https://arxiv.org/abs/2505.18458v2 |
assess the step’s ability to derive the correct answer. •High Quality and Well Format Data Are The Keys To Better Reasoning. Moreover, LIMO [451] and [232] state that high quality and well-formatted reasoning data are keys to high 21 performance. [451] emphasizes stimulating complex reasoning capabilities in LLMs throu... | https://arxiv.org/abs/2505.18458v2 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.