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sources for future research in multimodal learning and temporal reasoning, enabling broader advances across a range of video understanding applications. 9 References [1]Satanjeev Banerjee and Alon Lavie. Closer look at summarization evaluations. In Proceedings of the workshop on empirical modeling of semantic equivalen... | https://arxiv.org/abs/2505.18110v1 |
Liu, and Ping Luo. Vtimellm: Compression of time into a latent embedding for efficient video-language modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16694–16704, 2023. 2, 3, 7, 8 [16] Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low... | https://arxiv.org/abs/2505.18110v1 |
enhancement algorithms and tts models. University of Edinburgh. School of Informatics. Centre for Speech Technology Research (CSTR) , 2017. 12, 13 [30] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision... | https://arxiv.org/abs/2505.18110v1 |
. . . . . . . . . . . . . . . . . . 14 B.2 Training data format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 C Additional Experiments. 15 C.1 Ablation study on Segment Captioning . . . . . . . . . . . . . . . . . . . . . . . . 15 C.2 Omni-modal dataset & General understanding dataset . . . . . .... | https://arxiv.org/abs/2505.18110v1 |
12 Stage1 . For the initial stage, we use a combination of the Clotho [6],LLaV A-LCS558K [21], and Valentini-Botinhao Speech Dataset [29] as training dataset: •Clotho is an audio captioning dataset containing 4,981 audio clips, each paired with five unique captions, totaling 24,905 annotations. The audio clips range fr... | https://arxiv.org/abs/2505.18110v1 |
step is omitted during inference to retain full fidelity. This resampling reduces input redundancy and accelerates training. InStage 1 , we train the model with a batch size of 512 and single-frame input, completing within 10 hours using 4 ×A100SXM4-80GB GPUs. Stage 2 and 3 are conducted on 16 ×A100SXM4-80GB GPUs with ... | https://arxiv.org/abs/2505.18110v1 |
""}. Note that only one caption should be provided for each of AVS, AV, and VS. You MUST respond in JSON format.You are a helpful assistant designed to evaluate caption quality. Given three original captions and three generated omni-modal captions, please assess the quality of the generated captions based on the follow... | https://arxiv.org/abs/2505.18110v1 |
as 'a man is singing on stage' takes place in the video, and report this timestamp." }, { "from": "gpt", "value": "<sync><time><time><time><time><time><time><time><time><time><time><time><time><time><time>a man is singing on stage" } <Remaining conversations......> ] "times": [[1070.8, 1081.9], [1070.8, 1081.9], [1070.... | https://arxiv.org/abs/2505.18110v1 |
12.2 26.3 15.4 7.3 12.6 30.7 36.3 TriSense (7B) 128 3.4 10.2 20.2 8.5 3.1 9.9 22.8 11.5 5.4 12.3 26.7 15.4 7.3 12.8 30.8 36.1 C.2 Omni-modal dataset & General understanding dataset We conduct additional experiments on the public Omni-Modal benchmark LongV ALE [ 10]. Long- V ALE is designed for event understanding acros... | https://arxiv.org/abs/2505.18110v1 |
X←reshape (X) ▷[L, B, D ] 5: end for 6: Q←reshape (Q) ▷[L, B, D ] 7: A′, S′, V′←CrossAttn (A, Q),CrossAttn (S, Q),CrossAttn (V, Q) 8: A′, S′, V′←LayerNorm (A′),LayerNorm (S′),LayerNorm (V′) 9: ˆS←AW(A′, S′, V′) ▷Alg. 2 10: ˆS←LayerNorm (ˆS) 11: Z←2xMLP (ˆS) +ˆS ▷ two-layer MLP 12: Z←LayerNorm (Z) 13: return Z 14:end fu... | https://arxiv.org/abs/2505.18110v1 |
in the air, with the speaker saying that mechanical shutter is set to 11 frames per second, while passionate music is playing at the background. The speaker then says \’up to 40 frames per second, mechanical shutter and electronic shutter.\’’, return precise timestaps. Ground Truth: [216.3, 223.4] From 03 to 04 LongVAL... | https://arxiv.org/abs/2505.18110v1 |
Bridging Supervised Learning and Reinforcement Learning in Math Reasoning Huayu Chen1,2Kaiwen Zheng1,2Qinsheng Zhang2Ganqu Cui1Yin Cui2 Haotian Ye2,3Tsung-Yi Lin2Ming-Yu Liu2Jun Zhu1†Haoxiang Wang2 1Tsinghua University2NVIDIA3Stanford University https://research.nvidia.com/labs/dir/Negative-aware-Fine-Tuning Abstract R... | https://arxiv.org/abs/2505.18116v2 |
62,15]. At each iteration, an LLM generates answers to questions. A verifier helps reject all negative answers. The remaining positive ones are compiled into a dataset to fine-tune the LLM itself in a supervised manner. RFT has been demonstrated effective by various works [ 3,32,59,43,46,53]. However, it prevents any l... | https://arxiv.org/abs/2505.18116v2 |
modeling negative data, NFT enables direct policy optimization on both positive and negative answers via maximum-likelihood training. Eq. 1 is known as the Policy Gradient (PG) or REINFORCE algorithm [ 51,44]. In sequential decision-making problems such as language reasoning, acan be interpreted as the token decision f... | https://arxiv.org/abs/2505.18116v2 |
this section, we discuss how to leverage negative data D−to directly optimize π+ θ. Despite seemingly impossible at first thought, we find the target policy π+and the negative policy π−are tightly coupled, making feasible training π+ θdirectly from D−. First, we formalize the definition of the negative policy π−similar... | https://arxiv.org/abs/2505.18116v2 |
model copy in memory. The old policy likelihood π(a|q)can be pre-computed during data generation. 3.3 Practical Algorithm We introduce several improvements over Eq. 9 and propose a practical objective of NFT: LNFT D(θ) =−X q,a,rω(q)X t rlogRt θ(q,a) + (1 −r) log max_v (1−ˆrqRt θ(q,a) 1−ˆrq, ϵ) (10) where Rt θ(q,a) =π... | https://arxiv.org/abs/2505.18116v2 |
= ⇒ ∇ θLNFT D(θ) =∇θLGRPO D(θ) Implicit group normalization. Proposition 4.1 shows the “normalized advantage” term is implicitly present within NFT’s loss function. This partially justifies the Group Normalization design choice for GRPO, which was initially introduced only as an empirical technique [ 41]. In Appendix A... | https://arxiv.org/abs/2505.18116v2 |
adapted from the official DAPO codebase, based on the VeRL framework [42]. NFT inherits most of DAPO’s hyperparameters and design choices, including dynamic data sampling, token-level loss normalization, and no KL regularization. RFT is a simple but effective SL baseline that only fine-tunes LLMs on positive answers an... | https://arxiv.org/abs/2505.18116v2 |
DeepSeek-R1 report [ 14] also notes RL offers greater benefits over SFT in larger models. A potential explanation could be the increasing importance of negative data. RFT remains a strong baseline. Although surpassed by numerous algorithms, RFT still deserves attention due to its extreme simplicity. In 32B settings (Fi... | https://arxiv.org/abs/2505.18116v2 |
implicit reward model parameterized by the policy network to allow optimizing policies directly. Recent visual modeling efforts also leverage implicit conditional or residual models parameterized by generation networks to avoid guided sampling [8, 7] or enhance quality [67]. 7 Conclusion In this work, we introduce Nega... | https://arxiv.org/abs/2505.18116v2 |
Xu, Weize Chen, et al. Process reinforcement through implicit rewards. arXiv preprint arXiv:2502.01456 , 2025. [14] DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 , 2025. [15] Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Ch... | https://arxiv.org/abs/2505.18116v2 |
pilot study, 2025. [31] Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding r1-zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783 , 2025. [32] Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Alex Polozov, Christopher Meek, Dragomir Rade... | https://arxiv.org/abs/2505.18116v2 |
Yang, Zhiyuan Zeng, Liliang Ren, Lucas Liu, Baolin Peng, Hao Cheng, Xuehai He, Kuan Wang, Jianfeng Gao, et al. Reinforcement learning for reasoning in large language models with one training example. arXiv preprint arXiv:2504.20571 , 2025. [51] Ronald J Williams. Simple statistical gradient-following algorithms for con... | https://arxiv.org/abs/2505.18116v2 |
Malach. Echo chamber: Rl post-training amplifies behaviors learned in pretraining. arXiv preprint arXiv:2504.07912 , 2025. [67] Kaiwen Zheng, Yongxin Chen, Huayu Chen, Guande He, Ming-Yu Liu, Jun Zhu, and Qinsheng Zhang. Direct discriminative optimization: Your likelihood-based visual generative model is secretly a gan... | https://arxiv.org/abs/2505.18116v2 |
qlogRt θ(q,a+) ∇θLNFT D+=−X q,a+,tA+ q1 Rt θ(q,a+)∇θRt θ(q,a+) (16) When ais a negative answer, r(q,a) = 0 . LNFT D−(θ) =−X q,a−,tω(q) log max_v (1−ˆrqRt θ(q,a−) 1−ˆrq, ϵ) =−X q,a−,ts 1−ˆrq ˆrqlog max_v (1−ˆrqRt θ(q,a−) 1−ˆrq, ϵ) 15 ∇θLNFT D−=−X q,a−,ts 1−ˆrq ˆrq max(1−ˆrqRt θ(q,a−) 1−ˆrq, ϵ)−1·−ˆrq 1−ˆrq· ∇θRt θ(... | https://arxiv.org/abs/2505.18116v2 |
all-negative questions should be zeroed out automatically by the algorithm itself [ 58]. Other DAPO-related techniques are kept, such as setting positive clipping parameter to a higher value ϵ′ += 0.28. Since GRPO requires less time for sampling data, we train GRPO models for 580+ rollout steps instead of 320 steps, wh... | https://arxiv.org/abs/2505.18116v2 |
arXiv:2505.18121v1 [cs.AI] 23 May 2025PROGRM: Build Better GUI Agents with Progress Rewards Danyang Zhang1,2†Situo Zhang1,2†Ziyue Yang1,2Zichen Zhu1,2 Zihan Zhao1,2Ruisheng Cao1,2Lu Chen1,2,3‡Kai Yu1,2,3‡ 1X-LANCE Lab, School of Computer Science MoE Key Lab of Artificial Intelligence, SJTU AI Institute Shanghai Jiao To... | https://arxiv.org/abs/2505.18121v1 |
collecting trajectories, while Explorer [ 14] further iterates on this process. However, these methods rely on imitation learning (see Figure 1(a)) and are not well-suited for online environments, where content changes dynamically and agents may encounter unseen scenarios. They lack the ability to learn from mistakes o... | https://arxiv.org/abs/2505.18121v1 |
easily and sufficiently exploit the self-explored trajectories of agents. We evaluate the effectiveness of PROGRM on WikiHow task set [ 38], a real-world Android device navigation benchmark. Experimental results demonstrate that PROGRM-trained actors outperform leading proprietary LLMs for GUI tasks, including Claude-3... | https://arxiv.org/abs/2505.18121v1 |
similar core policies are grouped and the common pattern called recipe within each group is extracted by computing the group LCS; (b) Key Step Discovery, in which key steps (matched steps, highlighted in green; best viewed in color) are identified by matching each trajectory to the recipe with the highest completion ra... | https://arxiv.org/abs/2505.18121v1 |
position in Lj, its progress label is given by p∗ kλ=κ/∣Lj∣. For non-key steps ( i.e., steps between two key steps), we assign the progress label of their nearest preceding key step, i.e., for a non-key step kλ<t<kλ+1, its progress label is p∗ t=p∗ kλ. For environments that provide milestone-style intermediate rewards,... | https://arxiv.org/abs/2505.18121v1 |
labels. The Average Cumulative Rewards (Rwd) and Success Rates (SR, %) are displayed. Both global average results and per-category results are listed. Actor Rwd SRCross-Page In-Page QA Rwd SR Rwd SR Rwd SR GPT-4o-mini 1.60 38.00 1.58 52.54 1.63 29.41 1.59 27.50 GPT-4.1-mini 2.16 52.00 2.13 71.19 2.47 47.06 1.81 30.00 C... | https://arxiv.org/abs/2505.18121v1 |
The numbers are percentages (%). Naive ORM holds an evidently higher false positive rate. RM #TP 1#FN;#TN1#FP;Prec1Rec1Acc1 ORM 54.00 4.67 19.33 22.00 71.05 92.04 73.33 PROGRM LCS 57.33 2.00 33.33 7.33 88.66 96.63 90.67 PROGRM Env 57.33 4.67 36.67 1.33 97.73 92.47 94.00 Table 3: Comparison of reward models (ORM, ORM Cl... | https://arxiv.org/abs/2505.18121v1 |
We collect actor trajectories on WikiHow and consider the steps receiving environment milestone rewards to be ground-truth key steps. The progress labels are then assigned to the key steps by assuming the progress gains are even among them. Then we leverage various types of RMs to estimate the progress of these key ste... | https://arxiv.org/abs/2505.18121v1 |
and “useless repetition”. “Article not found” is referred to as the error where the agent fails to figure out the proper search keywords to reach a target article page in WikiHow app. “Useless repetition” indicates that the agent repeats some useless actions without achieving any actual progress to complete the task. S... | https://arxiv.org/abs/2505.18121v1 |
explore step-level RL for GUI tasks, inspired by the success of DeepSeek-R1 DeepSeek-AI et al. [5]on reasoning tasks. In this paper, we propose a new process reward model for GUI interaction tasks, PROGRM, to provide exquisite progress reward in RL training. Ideally, P ROGRM can be combined with any trajectory-level RL... | https://arxiv.org/abs/2505.18121v1 |
Jiayi Pan, Mert Cemri, Alane Suhr, Sergey Levine, and Avi- ral Kumar. Digirl: Training in-the-wild device-control agents with autonomous re- inforcement learning. In Amir Globersons, Lester Mackey, Danielle Belgrave, An- gela Fan, Ulrich Paquet, Jakub M. Tomczak, and Cheng Zhang, editors, Advances in Neural Information... | https://arxiv.org/abs/2505.18121v1 |
Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , pages 5315–5333. Association for Computational Linguistics, 2023. doi: 10.18653/V1/2023.ACL-LONG.291. ... | https://arxiv.org/abs/2505.18121v1 |
Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019 , pages 3980–3990. Association for C... | https://arxiv.org/abs/2505.18121v1 |
April 24-28, 2025 . OpenReview.net, 2025. URL https://openreview.net/forum?id= EEgYUccwsV . [32] An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin,... | https://arxiv.org/abs/2505.18121v1 |
10.48550/ARXIV .2502.18906. URL https://doi.org/10.48550/arXiv.2502.18906 . 13 [42] Yifei Zhou, Qianlan Yang, Kaixiang Lin, Min Bai, Xiong Zhou, Yu-Xiong Wang, Sergey Levine, and Li Erran Li. Proposer-agent-evaluator(pae): Autonomous skill discovery for foundation model internet agents. CoRR , abs/2412.13194, 2024. doi... | https://arxiv.org/abs/2505.18121v1 |
4: Statistics of the collected reward model (RM) training data for WikiHow. Figure 4(a) dis- plays the number of successful and failed trajectories, with a success-to-failure ratio of approximately 1.22. Figure 4(b) presents the number of steps in successful versus failed trajectories, with a step ratio of about 0.63. ... | https://arxiv.org/abs/2505.18121v1 |
Docker images to host the Android Emulator ™ and the replay server for WikiHow environment [ 38]. Flask5is used to build the main server of a remote environment. To reduce the communication latency, only the prompts are transferred on an HTTP (Hyper Text Transport Protocol) flow. To control consumed computing resources... | https://arxiv.org/abs/2505.18121v1 |
and can assign proper credits for steps even in failed trajectories. As illustrated in Figure 5, the agent completed partial task without achieving the final goal. The score predicted by the trivial ORM marks the whole trajectory as failed inadequately penalizes all the steps in the trajectory, although some steps do c... | https://arxiv.org/abs/2505.18121v1 |
negative comment to the article. <1> (Init) <2> CLICK( ) <3> CLICK( ) <4> INPUT( ,‘ how to carve stone’) <5> CLICK( ) <6> CLICK( ) <7> CLICK( ) <8> CLICK( ) <9> CLICK( ) <10> CLICK( ) <11> CLICK( ) <12> CLICK( ) ▲Task Command ▼Execution Trajectory 0 0.5 1Progress(1) Find an article to learn to make a raft. (2) The targ... | https://arxiv.org/abs/2505.18121v1 |
a judgment about the success or failure of this task. User: Instructions: ${instructions} History Actions: ${actions} Current screen: ${screen} Table 9: Prompt for ORM Claude System: You are an expert of mobile use, especially the app of WikiHow. This is a public and popular wiki platform you surf everyday. You know we... | https://arxiv.org/abs/2505.18121v1 |
some examples of actions: ‘‘‘ INPUT(3, "scooter") CLICK(4) SCROLL(DOWN) GOBACK() ‘‘‘ You need to first think about the reasoning process as an internal monologue and then provide the user with an action. Respond in the following format: <think> ... </think> <action> ... </action> . For example: <think> I need to have a... | https://arxiv.org/abs/2505.18121v1 |
arXiv:2505.18122v1 [cs.CL] 23 May 2025UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification Poojah Ganesan1*Rajat Aayush Jha1*Dan Roth2Vivek Gupta1† 1Arizona State University2University of Pennsylvania {pganesa4,rjha16,vgupt140} @asu.edu danroth @seas.upenn.edu Abstract Recent advances in large... | https://arxiv.org/abs/2505.18122v1 |
SQL for single-table schemas, a setting they are more exposed to during train- ing, while multi-table scenarios introduce struc- tural complexity that is harder to handle directly. UNJOINbridges this gap by reframing multi-table Text-to-SQL generation as a simplified single-table task that LLMs can solve more reliably.... | https://arxiv.org/abs/2505.18122v1 |
Appendix 4 In the simplified format, the structure no longer depends on how the original tables were connected, so there is no concept of table joins at this stage. By removing the need to reason about table-to- table relationships, this step eliminates a layer of complexity for the LLM. The transformation relies only ... | https://arxiv.org/abs/2505.18122v1 |
(e.g., table name disp changed to disposition ) are accurately mapped back to their valid schema counterparts. In particular, this step does not alter SQL logic or introduce external constraints; it only enhances schema consistency. UNJOINVaraints: We explore two variations of our proposed method: (a) UNJOIN SP: Perfor... | https://arxiv.org/abs/2505.18122v1 |
metrics: (a.) Query Execution Accuracy (QE) measures the per- centage of generated queries that execute success- fully without runtime errors. It checks only whether the query runs, not whether the result is correct, (b.) Exact Match (EM) captures the percentage of gen- erated queries that not only execute successfully... | https://arxiv.org/abs/2505.18122v1 |
EM scores on SPIDER dataset. ICL vs UNJOIN : The results in Table 2 show that RSL-SQL achieves the highest overall perfor- mance on both SPIDER and BIRD, particularly with strong generalization to BIRD (QE: 90.8, EM: 54.3). However, UNJOINperforms competitively, on SPIDER, where UNJOIN SPachieves an EM of 76.13, slight... | https://arxiv.org/abs/2505.18122v1 |
ARM 31.7 →51.27 (+19.57) Contriever 29.7 →47.99 (+18.29) DTR 30.4 →49.85 (+19.45) JAR (DTR) 36.9 →52.63 (+15.73) JAR (Contriever) 36.2 →51.10 (+14.90) Table 6: Exact Match (EM) scores with UNJOINin open book settings. ∆in parentheses shows the improvement. End2End Table Extraction. Table 7 presents a detailed compariso... | https://arxiv.org/abs/2505.18122v1 |
column names, improving precision and recall. For instance, 6 Retriever ARM JAR (DTR) JAR (Contriever) DTR Contriever P R P R P R P R P R CoT 32.88 74.47 65.60 66.10 64.70 65.40 59.50 59.20 55.90 55.50 UNJOIN 84.30 83.20 72.31 70.79 70.84 70.00 84.55 81.75 82.97 81.88 ∆ +51.42 +08.73 +06.71 +04.69 +06.14 +04.60 +25.05 ... | https://arxiv.org/abs/2505.18122v1 |
in ensuring correct table linking and reducingstructural inconsistencies. How does our approach perform across differ- ent model sizes? From Table 9, we observe that our approach UNJOIN MPperforms reasonably well with different model sizes. But our approach is most effective with larger LLMs due to the their su- perior... | https://arxiv.org/abs/2505.18122v1 |
(Wang et al., 2025) that assign subtasks to dedicated agents. These approaches improve robustness, but add orchestration complexity, and are hard to generalize. SFT End2End methods. SFT-based meth- ods (Li et al., 2024; Yang et al., 2024; Pour- reza and Rafiei, 2024; Talaei et al., 2024; Gorti et al., 2025; Sheng et al... | https://arxiv.org/abs/2505.18122v1 |
coherence, making the findings effectively communicated. Acknowledgments We gratefully acknowledge the Cognitive Compu- tation Group at the University of Pennsylvania and the Complex Data Analysis and Reasoning Lab at Arizona State University for their resources and computational support. References Ion Androutsopoulos... | https://arxiv.org/abs/2505.18122v1 |
Subramanian, Sophia Yang, and 7 others. 2024. Mixtral of experts. Preprint , arXiv:2401.04088. George Katsogiannis-Meimarakis and Georgia Koutrika. 2023. A survey on deep learning approaches for text- to-SQL. The VLDB Journal , 32(4):905–936. Dongjun Lee, Choongwon Park, Jaehyuk Kim, and Heesoo Park. 2025. MCS-SQL: Lev... | https://arxiv.org/abs/2505.18122v1 |
the limits of transfer learning with a unified text-to-text trans- former. Preprint , arXiv:1910.10683. Baptiste Rozière, Antoine Bosselut, Peter J. Liu, Thomas Scialom, and Dani Yogatama. 2024. Codel- lama: Open foundation models for code. Preprint , arXiv:2308.12950. Torsten Scholak, Nathan Schucher, and Dzmitry Bah-... | https://arxiv.org/abs/2505.18122v1 |
, pages 3911–3921, Brussels, Bel- gium. Association for Computational Linguistics. Weijia Zhang, Vaishali Pal, Jia-Hong Huang, Evangelos Kanoulas, and Maarten de Rijke. 2024a. QFMTS: Generating query-focused summaries over multi- table inputs. In Proceedings of the 27th European Conference on Artificial Intelligence (E... | https://arxiv.org/abs/2505.18122v1 |
these steps for translation : 1. Understand the Core Objective from the Question : - Identify the goal of the query (e.g ., aggregate data , filter specific rows , join information across tables ). 2. Map Simplified Schema Columns to the Original Schema : - Identify how the columns in the simplified schema correspond t... | https://arxiv.org/abs/2505.18122v1 |
with the intent described in the question . ### Steps for Translation : 1. ** Understand the Core Objective from the Question **: - Identify the goal of the query (e.g ., aggregate data , filter specific rows , join information across tables ). 2. ** Map Simplified Schema Columns to the Original Schema **: - Identify h... | https://arxiv.org/abs/2505.18122v1 |
primarily trained on SQL generation rather than structured multi-table reasoning. End2End Tables Extraction Sensitivity. Across models, the gap between the best and worst- performing baselines is more pronounced for col- umn precision and recall than for table precision/re- call. For instance, in SPIDER, GPT-4o’s colum... | https://arxiv.org/abs/2505.18122v1 |
TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations Alan Arazi Eilam Shapira Roi Reichart {alanarazi7, eilam.shapira, roireichart}@gmail.com Technion - IIT Abstract While deep learning has achieved remarkable success across many domains, it has historically underperformed on tabular learn... | https://arxiv.org/abs/2505.18125v1 |
adverse reactions. Released 55 Neurology Reports episodes of vertigo and memory lapses. Hospitalized The emerging field of Tabular Foundation Models (TFMs) has begun addressing these shortcomings, introducing powerful cross-dataset learning strategies [ 80,45,37]. However, the flagship model TabPFN-v2 [ 37] still handl... | https://arxiv.org/abs/2505.18125v1 |
categorical features into a transformer [ 76]. Other novel approaches leveraged inter-example information at inference time, with SAINT [66] proposing row- 1High-cardinality features are categorical columns with a large number of unique values. 2Code is available at https://github.com/alanarazi7/TabSTAR . 3Pretraining ... | https://arxiv.org/abs/2505.18125v1 |
dataset-specific output layers limits scalability and complicates multi-task learning. Consequently, they trained two separate models,5wasting potential for better cross-dataset learning. Notably, none of these approaches finetune semantic representations during downstream task training. In our work, we demonstrate tha... | https://arxiv.org/abs/2505.18125v1 |
enable effective cross-dataset learning by applying supervised learning on the target variable in both stages. At its core, is uses an unfrozen encoder-only language model, which can invoke world knowledge acquired during the language model pretraining.8The encoder is combined with a tabular-specific architecture tailo... | https://arxiv.org/abs/2505.18125v1 |
±3 standard deviations. In addition, they are verbalized using quantile-based binning into 10 bins, a novel approach to mitigate the precision loss inherent in language models [ 72]. Appendix A.1 shows a precise example and §6 discusses different verbalization strategies. In contrast, semantic features are directly ver... | https://arxiv.org/abs/2505.18125v1 |
pretraining, but also provides a better initialization for finetuning. In our example, both the Released andHospitalized tokens go through the same classification head, which maps them from representations to logits. Applying softmax yields predicted probabilities. For regression tasks, a single target token is project... | https://arxiv.org/abs/2505.18125v1 |
at most 10,000 examples. Although CARTE imposes no size cap,12it suffers from inefficiency and no preset configuration, requiring six learning-rate trials and totalling 6,000 slow GPU runs. Because of these important limitations, we consider two experiment conditions: (1)10K: Each model is trained13over at most 10,000 ... | https://arxiv.org/abs/2505.18125v1 |
For the Unlimited condition, TabSTAR- Unlimit achieves a 0.874 score, significantly above the second-best CatBoost-Tuned-Unlimit with 0.734. Importantly, all Unlimit variants surpass the 10K ones, emphasizing the importance of scaling. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Normalized Score CatBoost-Tuned XGB-Tuned CatBoo... | https://arxiv.org/abs/2505.18125v1 |
and the normalized score on the downstream tasks (right) as a function of the number of unfrozen encoder layers. Notably, unfreezing even a single encoder layer significantly outperforms using static embeddings. Further substantial improvements are observed as more layers are tuned, with the best results achieved when ... | https://arxiv.org/abs/2505.18125v1 |
through an unfrozen pretrained encoder. In addition, its novel target-aware tokens enable efficient cross-dataset generalization without dataset-specific parameters. Despite limited pretraining data and a relatively small text encoder [ 77], TabSTAR sets the SOTA in tabular classification with textual features, signifi... | https://arxiv.org/abs/2505.18125v1 |
: End-to-End Speech Recognition in English and Mandarin. In Proceedings of The 33rd International Conference on Machine Learning , pages 173–182. PMLR, June 2016. URL https://proceedings.mlr.press/v48/amodei16.html . ISSN: 1938-7228. [2]Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado,... | https://arxiv.org/abs/2505.18125v1 |
the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-1423. URL https:// aclanthology.org/N19-... | https://arxiv.org/abs/2505.18125v1 |
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, and et al. The Llama 3 Herd of Models, November 2024. URL http://arxiv.org/abs/2407.21783 . arXiv:2407.21783 [cs]. [30] Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux. Why do tree-based models still outper- form deep learning on typical tabular data? Advances in ... | https://arxiv.org/abs/2505.18125v1 |
2020. URL http://arxiv.org/abs/2001.08361 . arXiv:2001.08361 [cs]. [44] Liran Katzir, Gal Elidan, and Ran El-Yaniv. Net-DNF: Effective Deep Modeling of Tabular Data. October 2020. URL https://openreview.net/forum?id=73WTGs96kho . [45] Myung Jun Kim, Léo Grinsztajn, and Gaël Varoquaux. CARTE: pretraining and transfer fo... | https://arxiv.org/abs/2505.18125v1 |
Deep Learning, July 2022. URL http://arxiv.org/abs/2207. 03208 . arXiv:2207.03208 [cs]. [60] Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alex Smola. Benchmarking Multimodal AutoML for Tabular Data with Text Fields. August 2021. URL https://openreview.net/ forum?id=Q0zOIaec8HF . [61] Assaf Shmuel, Oren Glickm... | https://arxiv.org/abs/2505.18125v1 |
Newsl. , 15(2):49–60, June 2014. ISSN 1931-0145. doi: 10.1145/2641190.2641198. URL https://doi.org/10.1145/2641190.2641198 . [76] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In I. Guyon, U. V on Luxburg, S. Beng... | https://arxiv.org/abs/2505.18125v1 |
the verbalization module; next, we formally describe the architecture step-by-step; and finally, we present selected experiments on the TabSTAR architecture. A.1 The Verbalization Module TabSTAR’s verbalization module standardizes heterogeneous tabular inputs by converting each column, whether predictive feature or tar... | https://arxiv.org/abs/2505.18125v1 |
17 Target Variables The verbalization templates for the target values are prepended to every example. For classification tasks, each possible label is verbalized, while for regression we verbalize a single element consisting solely of the feature name. Employing a binning strategy to treat regression as a classificatio... | https://arxiv.org/abs/2505.18125v1 |
dropout 0.1, and ReLU activation. 18 we stack its d-dimensional text and numeric embeddings and feed them through the encoder layer. For every element, the attention is applied between its two representations, and we average the two outputs to produce one fused d-dimensional embedding. This yields a final sequence of l... | https://arxiv.org/abs/2505.18125v1 |
are also employed by models that get the input the whole dataset, labels included [ 37,47]. We run experiments with 2, 4 and 6 layers as they are roughly equivalent in parameter count to 3, 6, and 9 layers without row-attention. We observe no substantial gain, and thus we prioritize the simpler solution, as row-level a... | https://arxiv.org/abs/2505.18125v1 |
and a max learning rate of lr= 5×10−5is applied uniformly across all layers. Appendix B.3.3 discusses experiments with differential learning rate. Pretraining running time varies depending on the number of epochs and the included datasets. The full models (390 datasets) reported in §5 train for less than 48 hours on a ... | https://arxiv.org/abs/2505.18125v1 |
exposed to more datasets. We decrease the batch size to 16 and 8 and observe an improvement at the cost of slower training. An interesting direction for future work is moving to mixed-datasets batches, which require more complex implementation but might benefit from more regularized learning. Such approach, however, go... | https://arxiv.org/abs/2505.18125v1 |
Classification Datasets of the Pretraining Corpus, with their nexamples, mfeatures, Cclasses, presence in a benchmark ( B) and whether they are textual ( T). Dataset n m C B T KDDCup99 4,898,422 40 20 ✓ mimic_extract_los_3 4,155,270 17 68 ✓ Online-P2P-Lending 2,875,146 16 5 sf-police-incidents 2,215,023 8 2 ✓ ✓ physion... | https://arxiv.org/abs/2505.18125v1 |
nursery 12,958 8 4 online-shoppers-intention 12,330 17 2 Disaster-Tweets 11,370 4 2 ✓ mammography 11,183 6 2 PhishingWebsites 11,055 30 2 ✓ Binary-Dataset-of-Phishing-and-Legitimate-URLs 11,000 14 2 pendigits 10,992 16 10 WBCAtt 10,298 11 5 artificial-characters 10,218 7 10 ✓ internet_usage 10,108 71 46 ✓ robert 10,000... | https://arxiv.org/abs/2505.18125v1 |
Classification Datasets of the Pretraining Corpus. Dataset n m C B T vowel 990 12 11 The-Estonia-Disaster-Passenger-List 989 6 2 ✓ xd6 973 9 2 tokyo1 959 42 2 tic-tac-toe 958 9 2 Tour-and-Travels-Customer-Churn-Prediction 954 6 2 acp-breast-cancer 949 1 4 ✓ oil_spill 937 48 2 anneal 898 18 5 Cervical_Cancer_Risk_Factor... | https://arxiv.org/abs/2505.18125v1 |
8,192 32 Pollen-Luxembourg-1992-2018 7,784 36 colleges 7,063 44 ✓ ✓ wind 6,574 14 QSAR-TID-10980 5,766 1,024 ✓ QSAR-TID-11 5,742 1,024 ✓ Myanmar-Air-Quality 5,122 10 Santander_transaction_value 4,459 4,735 ✓ SAT11-HAND-runtime-regression 4,440 114 ✓ Mercedes_Benz_Greener_Manufacturing 4,209 364 ✓ abalone 4,177 8 ✓ poll... | https://arxiv.org/abs/2505.18125v1 |
in multiple forms. When LLMs are involved, there is a risk of memorization [ 8], and models trained on synthetic datasets [ 37] which try to mimic real-world distributions, can be unintentionally biased towards popular benchmarks. While these two forms of leakage are subtle and hard to detect, a more direct form must b... | https://arxiv.org/abs/2505.18125v1 |
and downstream task datasets (e.g., airlines ,sf police anddiabetes ). We believe that this questions the validity of their evaluation, and that such contamination poses a serious challenge for the TFM community which could be substantially addressed by better tabular data repositories [73]. CM2 CM2 was pretrained over... | https://arxiv.org/abs/2505.18125v1 |
Duplicate used-car-prices-in-pakistan Whisky CRT Pretraining Wine.com CRT Duplicate wine_reviews WineEnthusiasts CRT Duplicate wine_reviews baseline. Interestingly, their repository27lists TP-BERTa as a method trained on a subset of OpenTabs, reinforcing that the leakage is shared between the models. E.2 Baselines Impl... | https://arxiv.org/abs/2505.18125v1 |
XGBoost using the xgboost package35and follow the same procedure as for CatBoost, except additional preprocessing (e.g., transforming categorical variables into one-hot encodings). For the default configuration, we follow the suggestion of [ 25] and use: booster = ”gbtree ”, early _stopping _rounds = 50 ,n_estimators =... | https://arxiv.org/abs/2505.18125v1 |
datasets are referred by their ID from Appendix D. As discussed in §5, TabPFN-v2 is unable to run over 4 datasets: C12, because it is a multiclass problem with more than 10 classes, and C02, C14 and R01 because they support inference for up to 500,000 cells. Attempts to run the model over a subset of the examples led t... | https://arxiv.org/abs/2505.18125v1 |
93.9±1.0 95.6±0.9 95.3±0.9 96.2±0.5 R21 92.3±1.2 92.5±1.1 92.6±1.1 91.8±1.1 93.3±1.1 92.3±1.0 91.6±1.0 92.6±1.0 R22 45.2±5.5 45.2±5.9 39.1±6.4 43.8±5.5 39.7±5.5 36.1±7.1 46.8±5.8 R23 85.0±1.8 85.4±1.2 85.5±1.1 81.8±1.5 84.8±1.6 83.0±1.6 83.3±1.2 85.1±1.2 R24 86.0±0.6 84.0±0.8 85.8±0.7 79.7±1.0 70.3±1.6 81.8±1.8 82.9±0.... | https://arxiv.org/abs/2505.18125v1 |
times are significantly lower than 14,400 seconds (4 hours), used for CatBoost-Tuned and XGBoost-Tuned, although a single run of any tree model without 5-fold cross-validation is faster. The running times of TabPFN-v2 are considerably better. However, since they use ICL, they are limited to run only up to 10,000 exampl... | https://arxiv.org/abs/2505.18125v1 |
§6. G.1 Evaluation Datasets for Analysis All the experiments are evaluated over 20 datasets from the benchmark in Appendix D. Each experiment reports the performance with AUROC for classification and R2for regression. For conciseness, each tables reports both regression and classification tasks, distinguishable by thei... | https://arxiv.org/abs/2505.18125v1 |
The Effect of Pretraining. Results for 20 datasets with 95% CI, for varying number of pretraining datasets. The top performance score is bolded first, and then all scores are rounded. We report AUROC for classification and R2for regression. ID 0 16 64 256 C01 90.8±0.4 90.7±0.4 90.7±0.3 91.0±0.4 C02 98.0±0.4 97.4±0.8 97... | https://arxiv.org/abs/2505.18125v1 |
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