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elements on the current mobile screen. The output should include four parts: 1. Sub-Instruction: Identify the interactive elements and generate natural language instructions for interacting with these elements. The instructions should be concise, clear, and executable, and must include critical details such as filename... | https://arxiv.org/abs/2505.21279v1 |
screen. 2. The type of action currently being executed, which can be one of two types: CLICK or LONG_PRESS. You need to determine whether this action can fulfill the current low-level instruction. 3. The current screen environment, with the position where the action(click and long_press) needs to be executed marked by ... | https://arxiv.org/abs/2505.21279v1 |
RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models Yue Zhang1, Zhiliang Tian1, Shicheng Zhou2, Haiyang Wang1, Wenqing Hou1,Yuying Liu1,Xuechen Zhao3,Minlie Huang4,Ye Wang1, Bin Zhou1 1College of Computer Science and Technology, National University of Defense and Technology. N... | https://arxiv.org/abs/2505.21281v1 |
types of methods rely on textual and semantic match- ing but ignore capturing the logic of judging legal cases, where logic in reasoning is crucial in legal judgment. To address the aforementioned issues, re- searchers applied legal judgment logic to enhance reasoning in traditional deep learning models or large langua... | https://arxiv.org/abs/2505.21281v1 |
to establish seman- tic associations among the knowledge. To improve the accuracy of crime prediction, the prevailing methods primarily focus on refining the attention mechanism (He et al., 2023; Yu and Qiu, 2023; He et al., 2025). Additionally, some methods infuse domain-specific legal knowledge into LLMs (Wang et al.... | https://arxiv.org/abs/2505.21281v1 |
the law article, the charge, and the prison term. We represent the judgment of a case as j= (article, charge, prison _term ), where (article, charge, prison _term )refers to the labels of provisions, accusation, and prison term, respectively. •Precedent is the previous case with a similar fact. For a given judgment lab... | https://arxiv.org/abs/2505.21281v1 |
Initialization Module on the upper left(§3.3), the gray box is the Rule Optimization Module(§3.4) with Confusion-Aware Contrastive Learning, and the green box is the Examination Module(§3.5) for the completion of LJP tasks based on the optimized rules. describe the reasoning rules, we utilize the FOL formalism, which c... | https://arxiv.org/abs/2505.21281v1 |
with CACL. The full procedure of this Optimization is described in Algorithm 1. Specifically, this module works in two-stage steps: construction confusable case set(§3.4.1), and rules optimization(§3.4.2) depends on Confusion-Aware Contrastive Learning(CACL). Algorithm 1: Confusion-Aware Rule Opti- mization Require: Pr... | https://arxiv.org/abs/2505.21281v1 |
the process of tree-splitting. These nodes of the opti- mization tree are different versions rule during opti- mization. In one iteration, we first create some rea- soning quizzes constructed by the confusable cases set, and calculate the score of the judgment rules for best-first splitting. And then, we collect cor- r... | https://arxiv.org/abs/2505.21281v1 |
rule, which simulates students reflecting on the correct and incorrect problem- solving process. The full procedure of the CACL method is described in Algorithm 2, which consists of three core steps as follows. Step 2-1: Construct triplets. CACL con- structs the experience of rule evaluation as con- trast triplets. The... | https://arxiv.org/abs/2505.21281v1 |
2018; Xu et al., 2020; Yue et al., 2021; Wu et al., 2023b). Baselines. We compare with: (1) CNN (LeCun et al., 1989) use different kernel convolutional operations to extract text features for classifica- tion; (2) BERT (Devlin et al., 2019) can be easily 2https://github.com/china-ai-law-challengefine-tuned on downstrea... | https://arxiv.org/abs/2505.21281v1 |
TopJudge(Zhong et al., 2018) 80.46 40.96 40.96 38.24 87.31 88.68 87.84 88.20 35.54 33.55 31.08 32.00 R-Former(Dong and Niu, 2021) 87.82 56.13 56.57 55.81 91.54 91.61 91.96 91.58 40.70 36.09 36.76 35.04 LADAN(Xu et al., 2020) 82.82 42.57 39.00 40.71 88.09 90.12 88.82 89.47 38.03 33.66 30.08 31.77 Neurjudge(Yue et al., 2... | https://arxiv.org/abs/2505.21281v1 |
logical structure and legal terminology in complex facts, which can fo- cus on important logical details to reduce incorrect judgments caused by excessively long texts. 9 MethodLaw Article Charge Prison Term Acc Ma-P Ma-R Ma-F Acc Ma-P Ma-R Ma-F Acc Ma-P Ma-R Ma-F CNN(LeCun et al., 1989) 76.14 35.48 38.55 35.39 74.91 7... | https://arxiv.org/abs/2505.21281v1 |
Acc Ma-F Acc Ma-F Acc Ma-F PLJP(Bert) 38.93 58.87 42.64 44.71 23.26 22.29 ours(RLJP) 41.67 47.62 97.70 91.95 31.70 35.96 5 Conclusion In summary, our proposed RLJP framework intro- duces three key innovations for LJP: (1) A dynamic rule optimization method that formulates judgment rule optimization with CACL as the pro... | https://arxiv.org/abs/2505.21281v1 |
North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) , pages 4171–4186. Association for Computational Linguistics. Qian Dong and Shuzi Niu. 2021. Legal judgment predic- tion via rela... | https://arxiv.org/abs/2505.21281v1 |
Yang. 2024b. Semdr: A semantic-aware dual encoder model for legal judg- ment prediction with legal clue tracing. In 2024 IEEE International Conference on Systems, Man, and Cy- bernetics (SMC) , pages 3447–3453. IEEE. Weicong Qin, Zelin Cao, Weijie Yu, Zihua Si, Sirui Chen, and Jun Xu. 2024. Explicitly integrating judg-... | https://arxiv.org/abs/2505.21281v1 |
Yanqing An, Mingyue Cheng, Biao Yin, and Day- ong Wu. 2021. Neurjudge: A circumstance-aware neural framework for legal judgment prediction. In SIGIR ’21: The 44th International ACM SIGIR Con- ference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , pages 973–982. ACM. Shen... | https://arxiv.org/abs/2505.21281v1 |
ensure efficient data transfer and processing. The oper- ating environment was Linux-based, and we em- ployed CUDA and cuDNN libraries to optimize GPU performance. All these details are crucial for understanding the computational resources that underpinned our experimental results. H The Statistics of Two Extracted Dat... | https://arxiv.org/abs/2505.21281v1 |
arXiv:2505.21288v1 [cs.LG] 27 May 2025GSAT: Graph Structure Attention Networks Farshad Noravesh1, Reza Haffari2, Layki Soon3, Arghya Pal4 1Monash University, Malaysia Farshad.Noravesh@monash.edu 2Monash University, Australia Gholamreza.Haffari@monash.edu 3Monash University, Malaysia soon.layki@monash.edu 4Monash Univers... | https://arxiv.org/abs/2505.21288v1 |
approac h and generalize the graph attention to enforce the graph to attend to differen t structural pat- terns of neighbour nodes. Moreover, our approach uses a prep rocessing based on Word2Vec training that provides the embedding of ARWs. In Graph Convolutional Networks (GCN) [17], the effect of wal k length(Hops) is p... | https://arxiv.org/abs/2505.21288v1 |
number of learnable structures. E ven a big number does not resolve the issue since many of the structures would then have high correlation with each others. The second drawback of [4] is t he lack of modeling for node neighbour structures based on label information si nce [4] has focused on graph classification tasks o... | https://arxiv.org/abs/2505.21288v1 |
/summationtextη i=1ey(wi)(2) whereηis the number of ARWs of length l. [31] combines breath first search(BFS) and anonymous walk(A W) to define the topological AW which provides bijective mapping between em bedding and local structure of node. To consider diverse heterostructures as opposed to homoge- nous graphs, [11] pr... | https://arxiv.org/abs/2505.21288v1 |
high length. This means that the random feature has become a very c onservative variable. This is the motivation behind the modulation func tion that diminishes the effect of long walks. The choice of decaying speed in modul ation function is still not based on theoretical backgrounds. [27] introdu ces quasi-Monte Carlo... | https://arxiv.org/abs/2505.21288v1 |
RW representations w ith message passing methods. To address this research gap, [2] put forwa rd a novel framework that integrates them by aggregating RW embeddings and learn s the encoding of RW end-to-end. However, they neglect the usage of ARW to make their modeling more generalisable. Another drawback of [2] is the... | https://arxiv.org/abs/2505.21288v1 |
repeated wo rds. Before combining the SR with OA, we do preprocessing to calculate wo rd embedding through skipGram algorithm. The sampling of RW is different f rom pretrained model which is done in preprocessing step. 8 Farshad Noravesh1, Reza Haffari2, Layki Soon3, Arghya Pal4 3.4 Sampling Random Walks The length of th... | https://arxiv.org/abs/2505.21288v1 |
v])) /summationtext v′∈Nuexp(Relu(aT[Wh(s) u||Wh(s) v′]))(8) GSAT: Graph Structure Attention Networks 9 Finally, the nodes are updated using the following combine r ule: h(k+1) u=ReLU(V(k)m(k) N(u)+b(k)) (9) whereV(k)denotes a trainable weight matrix and b(k)is bias term. Note that the present work is only using ARW to... | https://arxiv.org/abs/2505.21288v1 |
. Assume the structural size ha s dimension F and H be the number of attention heads , E be the number of edges and N is the number of nodes. Then GSAT has computational complexity ofO(HEF)+ O(NlogN). 4 Experiments Note that all experiments in the present work do not concaten ate the structural features with original f... | https://arxiv.org/abs/2505.21288v1 |
pooling [1]. Some other impo rtant hierarchical pooling methods are [9],[25], [18], [33]. Table 3. Graph classification accuracies on five benchmarks (percent age). The shown accuracies are mean and standard deviation over 10 different runs. We use bold to highlight wins and underline to highlight the second best. Model M... | https://arxiv.org/abs/2505.21288v1 |
walks that balances local and global information that provides more co ntext about neigh- borhood connectivity and helps capture structural variati ons at a mesoscopic scale. Thus, it works well for graphs where medium scale topo logy is important like the case for graph classification for PROTEIN dataset. T he third ex... | https://arxiv.org/abs/2505.21288v1 |
for graph pooling. In: Proceedings of the 37th Inte rnational Conference on Machine Learning. ICML’20, JMLR.org (2020) 2. Chen, D., Schulz, T., Borgwardt, K.: Learning long range d ependencies on graphs via random walks (06 2024) 3. Choromanski, K.: Taming graph kernels with random featur es (04 2023) 4. Cosmo, L., Min... | https://arxiv.org/abs/2505.21288v1 |
Dean, J.: Efficient e stimation of word rep- resentations in vector space. In: International Conferenc e on Learning Representa- tions (2013) 23. Nguyen, K., Nguyen, T., Ho, N., Nguyen, K., Nong, H., Nguy en, V.: Revisiting over-smoothing and over-squashing using ollivier’s ricci curvature (11 2022) 24. Perozzi, B., Al-R... | https://arxiv.org/abs/2505.21288v1 |
1 Complex System Diagnostics Using a Knowledge Graph -Informed and Large Language Model -Enhanced Framework Saman Marandi1, Yu-Shu Hu2, Mohammad Modarres1 1Center for Risk and Reliability U niversity of Maryla nd, MD, USA; 2DML Inc., Hsinchu, Taiwan Correspond ing Author : smarandi@umd.edu Abstract In this paper, we pr... | https://arxiv.org/abs/2505.21291v1 |
system interdependencies, and fault propagation mechanisms to be systematically analyzed. This hierarchical framework supports the tracing of failures from system -level objectives down to elemental components, offering a powerful tool for diagnostic analysis. Although DML models provide a robust framework for system d... | https://arxiv.org/abs/2505.21291v1 |
If a particular sequence is not anticipated during model development, the system may fail to diagnose it. Moreover, as systems grow in complexity, tracing failure pathways become increasingly infeasible using event -based logic alone . Functional modeling reflects a design er’s intent by capturing system goals and func... | https://arxiv.org/abs/2505.21291v1 |
be achieved or a subsystem can successfully operate. It applies reductionist principles, where qualities represent functions and goals, while objects and relationships are structured through success trees and logical modeling including Boolean, physical, and fuzzy logic [14]. This integration enhances DML’s ability to ... | https://arxiv.org/abs/2505.21291v1 |
sequences or summaries. Fine -tuned LLaMA models were employed, achieving superior performance compared to traditional deep learning models and demonst rating that LLMs can effectively process non -textual diagnostic information when appropriately structured. Improvements were reported in accurately identifying fault r... | https://arxiv.org/abs/2505.21291v1 |
reasoning processes. Their integration shows promise for enhancing fault retrieval and root cause analysis across various technical domains. 3. Research Overview As discussed in Section 2 , although LLMs and KGs show promise for assisting with system diagnostic s from textual documentation, their role in structured fun... | https://arxiv.org/abs/2505.21291v1 |
a structured repository to support querying and reasoning for diagnostics and decision -making. In this research, the KG is deployed using Neo4j [33], a graph database platform that represents entities and their interconnections as property graphs, with attributes stored as node and relationship properties. Cypher enab... | https://arxiv.org/abs/2505.21291v1 |
The system is structured with the main goal defined as "Ensure safe and effective operation of the system". This goal is supported by four primary functions: "Supply Feedwater", "Control Water Flow", "Manage System Integration and Response", and "Provide Emergency and Automated Response". Figure 3. P&ID of Simplified A... | https://arxiv.org/abs/2505.21291v1 |
intended. Attributes are stored within the nodes themselves and may include expert knowledge or informatio n derived from ML or DL models based on operational data or manual inspections. For example, a component such as a turbine -driven pump may contain attributes indicating the probability of being in various states ... | https://arxiv.org/abs/2505.21291v1 |
data indicates a high probability that the CST is in a failed state, the likelihood of satisfying this success condition would be correspondingly low. This affects the overall success probability of the subfunction "Manage Condensation Tanks," which depends on all CSTs through an AND gate. Thus, the fail ure of even on... | https://arxiv.org/abs/2505.21291v1 |
[35], [36] . For downward propagation, given an upper -level node, the tool traces the KG downward to determine the required paths for achieving that node’s success. Using the defined gates, it identifies the necessary dependencies at each level. The path -set generation method determines the minimal components require... | https://arxiv.org/abs/2505.21291v1 |
structurally consistent with the source material. Elements were labeled as hallucinated if they introduced information that was not present in the documentation or if they misrepresented relationships. The average results across the five runs are summarized in Table 1, which reports the ground truth element counts, the... | https://arxiv.org/abs/2505.21291v1 |
distinguishing between diagnostic and interpretive tasks and its effectiveness in performing structured reasoning and knowledge retrieval based on the syste m model. 7. Conclusion and Outlook 7.1. Limitations and Challenges Despite the demonstrated effectiveness of the proposed LLM - and KG -based diagnostic framework ... | https://arxiv.org/abs/2505.21291v1 |
users to interact with system behavior through intuitive queries, lowering the technical barrier to advanced diagnostics. It extends the utility of functional modeling techniques such as DML, which h ave traditionally required extensive domain expertise and manual effort. While demonstrated on a nuclear power applicati... | https://arxiv.org/abs/2505.21291v1 |
support larger token capacities. Together, these enhancements will further align the framework with the demands of real -time diagnostics in safety -critical, complex engineered systems. 18 References [1] U. Yildirim, F. Campean, and H. Williams, “Function modeling using the system state flow diagram,” Artif. Intell. E... | https://arxiv.org/abs/2505.21291v1 |
in Studies in Fuzziness and Soft Computing, vol. 38. , Heidelberg: Physica -Verlag HD, 2000, pp. 364 –395. doi: 10.1007/978 -3-7908 -1866 -6_17. 19 [17] M. Modarres, “Functional modeling of complex systems with applications,” in Annual Reliability and Maintainability. Symposium. 1999 Proceedings (Cat. No.99CH36283) , W... | https://arxiv.org/abs/2505.21291v1 |
doi: 10.1109/TII.2024.3366977. [31] T. Sun, F. Zeng, and X. Liu, “A Fault Analysis and Reasoning Method for Vehicle Information Systems Based on Knowledge Graphs,” in 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS -C), Cambridge, United Kingdom: IEEE, Jul. 2024, pp... | https://arxiv.org/abs/2505.21291v1 |
arXiv:2505.21298v1 [cs.MA] 27 May 2025Large Language Models Miss the Multi-Agent Mark Emanuele La Malfa1∗Gabriele La Malfa2∗Samuele Marro3 Jie M. Zhang2Elizabeth Black2Micheal Luck4Philip Torr3Michael Wooldridge1 1Department of Computer Science, University of Oxford 2Department of Informatics, King’s College London 3De... | https://arxiv.org/abs/2505.21298v1 |
as well as an aspect related to benchmarking those systems. We criticise the notions of agents’ social intelligence and environment as proposed in the MAS LLMs literature (Sections 2 and 3), and discuss what is missing in terms of coordination and communication (Section 4). Further, we observe that the interest in open... | https://arxiv.org/abs/2505.21298v1 |
specific state. Further, agents should receive and consistently maintain a unique representation of the environ- inferring beliefs, desires and intentionsment [ 31,90], particularly when settings are dynamic and partially observable [ 81]. To foster effective cooperation and competition, that should not reduce to the e... | https://arxiv.org/abs/2505.21298v1 |
actions. In line with the MAS literature, we argue mechanisms to negotiate and implement communication methods that integrate the principles of speech acts [ 6,104] and Gricean maxims [ 41] to minimise the cost of communication and maximise its effectiveness. We expand on these points in Section 4. IV . MAS LLMs do not... | https://arxiv.org/abs/2505.21298v1 |
must be socially reactive and proactive, i.e., able to grasp and reason about other agents’ goals, negotiate with them, and even enlist their cooperation when needed. The literature on LLMs includes a substantial body of works aimed at developing competitive lack of social behaviour and ToMand cooperative systems of LL... | https://arxiv.org/abs/2505.21298v1 |
learn to respond appropriately while also interpreting, anticipating, and reacting to the actions of other agents, based on feedback from their interactions. In conclusion, while fine-tuning is proven to be promising to specialise and assign roles to LLMs [ 68, 73,108], we argue it is alone insufficient to provide them... | https://arxiv.org/abs/2505.21298v1 |
the actions the LLMs make), the intrinsic non-determinism of LLMs flaws this setting. In terms of safety, one can design specific procedures, such as safety mechanisms and guardrail measures, that deterministically trigger when particular events happen; on the other hand, non-deterministic LLMs provide no guarantees th... | https://arxiv.org/abs/2505.21298v1 |
cannot be modelled without asynchronicity and require simplification (e.g., by assuming agents act sequentially through an orchestrator). Notably, while most closed-source LLM providers offer asynchronous APIs, agents employing LLMs LLMs are asynchronous... tend to be predominantly used in synchronous or parallel fashi... | https://arxiv.org/abs/2505.21298v1 |
natural language communications; any other communication that routines and protocols can implement should otherwise favour structured languages [76]. To conclude, overlooking the importance of communication by assuming natural language as the standard has the concrete risk of developing MAS LLMs that are expensive (the... | https://arxiv.org/abs/2505.21298v1 |
authors show that simply nudging one agent causes other agents to engage in complex behaviours, the concept of emergence itself is never addressed formally. Another recent work studies how LLMs build agent societies within a Minecraft environment [ 3]. While the work claims that agents can achieve significant milestone... | https://arxiv.org/abs/2505.21298v1 |
be social agents & Section 4 - Central orchestration is enough to build complex MAS LLMs. The main argument against our position in Section 2 and the first paragraph of Section 4 are that (i) agentic tools do not need to pre-train LLMs to enhance their social behaviour and capabilities to interact with other agents and... | https://arxiv.org/abs/2505.21298v1 |
in completion tokens is achieved, but requires optimising a set of parameters that is not transferable across tasks. Section 5 - The point of open-ended MAS LLMs is not benchmarking. The main concurrent argument to our point in Section 5 is that emergent behaviours are all those systems that exhibit, in the long run, c... | https://arxiv.org/abs/2505.21298v1 |
in the game of diplomacy by combining language models with strategic reasoning. Science , 378(6624):1067–1074, 2022. [8]D. Balduzzi, M. Garnelo, Y . Bachrach, W. M. Czarnecki, J. Perolat, M. Jaderberg, and T. Graepel. Open-ended learning in symmetric zero-sum games, 2019. [9]C. Bandi, H. Bandi, and A. Harrasse. Adversa... | https://arxiv.org/abs/2505.21298v1 |
Coulouris, J. Dollimore, T. Kindberg, and G. Blair. Distributed Systems: Concepts and Design . Addison-Wesley Publishing Company, USA, 5th edition, 2011. [25] I. Dasgupta, C. Kaeser-Chen, K. Marino, A. Ahuja, S. Babayan, F. Hill, and R. Fergus. Collaborating with language models for embodied reasoning. arXiv preprint a... | https://arxiv.org/abs/2505.21298v1 |
progress and challenges. In K. Larson, editor, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI- 24, pages 8048–8057. International Joint Conferences on Artificial Intelligence Organization, 8 2024. Survey Track. [43] S. Holt, M. R. Luyten, and M. van der Schaar. L2mac: L... | https://arxiv.org/abs/2505.21298v1 |
solving. IEEE Transactions on Systems, Man, and Cybernetics , 21(6):1347–1362, 1991. [61] G. Li, H. A. A. K. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem. Camel: Commu- nicative agents for "mind" exploration of large scale language model society. arXiv preprint arXiv:2303.17760 , 2023. [62] H. Li, Y . Chong, S. Step... | https://arxiv.org/abs/2505.21298v1 |
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Russell and P. Norvig. Artificial Intelligence: A Modern Approach . Prentice Hall, 3 edition, 2010. [95] H. Sami, M. ul Islam, S. Charas, A. Gandhi, P.-E. Gaillardon, and V . Tenace. Nexus: A lightweight and scalable multi-agent framework for complex tasks automation, 2025. [96] G. M. Saunders and J. B. Pollack. The ev... | https://arxiv.org/abs/2505.21298v1 |
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arXiv:2505.21301v1 [cs.CL] 27 May 2025How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian Andrea Pedrottiα, Giulia Rambelliβ, Caterina Villaniβ, Marianna Bolognesiβ αIstituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI-CNR) andrea.pedrotti@isti.cnr.it βUniversit... | https://arxiv.org/abs/2505.21301v1 |
categories is influenced by the distributional properties of linguistic input remains a central question in cognitive science, linguistics, and artificial intelligence (van Hoef et al., 2023). This paper investigates the organization and the contents of conceptual categories produced at a subordinate level by humans an... | https://arxiv.org/abs/2505.21301v1 |
and process (Murphy, 2002). A common approach for investigating the struc- ture of categorical knowledge involves analyzing typicality effects, by asking typicality ratings on a Likert scale (i.e., “How typical is a catfor the category mammal ?”) or by instructing participants to freely name members of a given category... | https://arxiv.org/abs/2505.21301v1 |
model the semantic fluency task (§2.1). They designed differ- ent approaches to predict the next item in a given list (“Examples of fruits are the strawberry and the [MASK]”) for five superordinate categories (Fruits, Vegetables, Animal, Supermarket items,Tool, Foods). Among the models, RoBERTa-Large proved to be the b... | https://arxiv.org/abs/2505.21301v1 |
Discussion. In line with Monte- finese et al. (2012), we find that dominance, avail- ability, and first occurrence are all strongly and positively correlated ( rs= 0.95, 0.75, 0.89; for dominance vs. availability, dominance vs. first occurrence, and availability vs. first occurrence); whereas mean rank order of product... | https://arxiv.org/abs/2505.21301v1 |
We analyse our data from two complementary ANIMALS BODY PARTS CLOTHES FOODS FURNISHING FURNITURE HOBBIES HOUSING KITCHEN PLANTS STATIONERY VEHICLES avg llama-3.2-3B 0.63 0.74 0.76 0.84 0.61 0.69 0.67 0.72 0.59 0.50 0.63 0.63 0.67 llama-3.1-8B 0.48 0.64 0.64 0.86 0.61 0.69 0.74 0.72 0.49 0.41 0.49 0.63 0.62 llama-3.1-70... | https://arxiv.org/abs/2505.21301v1 |
differs widely across models, with larger and more recent LLMs generating a higher propor- tion of valid exemplars in comparison to smaller models or vLMs. For instance, LLaMA-v3.1-70B generates 82% valid exemplars, while Mistral-7B generates only 52% valid exemplars. The lowest performance is observed by LLaVa-7B (44%... | https://arxiv.org/abs/2505.21301v1 |
context (e.g., c. da corridoio ‘hall- way dresser’, c. da esterno ‘outdoor dresser’) or intended contents (e.g., c. per giocattoli ‘for toys’, per oggetti di cancelleria ‘for stationery items’). While such expressions might be interpretable and even plausible, they are not attested in usage and do not correspond to est... | https://arxiv.org/abs/2505.21301v1 |
especially evident in vLMs. For example, idefics2-8B not only relies on com- positional operations but also lists other types of trees (e.g., acacia, eucalyptus, maple as exemplars ofabete ‘fir’), failing to generate subordinate exem- plars and generating basic-level exemplars instead. 5 Are LLMs Sensitive to Human Cat... | https://arxiv.org/abs/2505.21301v1 |
superordinate categories is smaller (12 vs 187 con- cept terms). A possible explanation is that models have seen the occurrence <exemplar, basic-level concept> more frequently than the pair <exemplar, superordinate-level concept>. In addition, most of Model Low Medium High llama-3.2-3B 0.65 0.62 0.42 llama-3.1-8B 0.58 ... | https://arxiv.org/abs/2505.21301v1 |
increases, LLMs are less likely to detect the typical item. This sug- gests that when humans provide fewer exemplars, the first one is cognitively dominant compared to the other ones, a distinction reflected in the model’s perplexity scores. However, in richer categories, the cognitive distinctive attributes among exem... | https://arxiv.org/abs/2505.21301v1 |
models, with alignment to human responses below 25% (§4). Additional subtasks in §5 illustrate that models struggle to build a hierarchical conceptual organi- zation like humans, limiting their ability to reasonalong the taxonomic axis (§5.1). While they per- form well in basic-level category induction, they underperfo... | https://arxiv.org/abs/2505.21301v1 |
gue that GPT last models could achieve better performances for the presented tasks, we pre- fer open models that can be accessed in their internal representations. Ethical Considerations •We administrated the exemplars generation task described in §3 to a total of 365 partici- pants (48.5% women; 49.9% man; 1.6% non- b... | https://arxiv.org/abs/2505.21301v1 |
Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, L... | https://arxiv.org/abs/2505.21301v1 |
of chatgpt on human data collection: A case study in- volving typicality norming data. Behavior Research Methods , 56(5):4974–4981. Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. 2021. Perceiver: General perception with iterative attention. InProceedings of the 38th Interna... | https://arxiv.org/abs/2505.21301v1 |
feature-based analysis and new norms for italian. Behavior Research Methods , 45:440 – 461. Gregory Murphy. 2002. The big book of concepts . MIT press. Cristina Izura Natividad Hernández-Muñoz and An- drew W. Ellis. 2006. Cognitive aspects of lexical availability. European Journal of Cognitive Psychol- ogy, 18(5):730–7... | https://arxiv.org/abs/2505.21301v1 |
human participants. Exemplar Dominance ED(E) =P(E|C) =N(E∩C) N(C)(1) where N(E∩C)is equal to the number of par- ticipants who produced the exemplar Ewhen in response to the concept C, andN(C)is the number of participants elicited by C. Mean Rank Order MRO(E) =PN(C)ri(E|C) N(C)(2) First Occurrence Value FOV(E, C) =Nfirs... | https://arxiv.org/abs/2505.21301v1 |
was trained on over more than 100 languages, and compresses nat- ural language text and source code more efficiently than the SentencePiece tokenizer used in previous Mistral models. B.3 Multimodal Language Models LLaV A (Liu et al., 2023) is a multimodal model that integrates visual understanding with language capabil... | https://arxiv.org/abs/2505.21301v1 |
kens that may be sampled, while frequency and repetition penalty are set to 0. B.7 Top-5 Matches Table 6 shows the percentage of matches among the top-5 human- produced and LLMs-generated exemplars, reporting individual accuracy for each of the 12 superordinate categories. B.8 Generated Exemplars and Hallucinations In ... | https://arxiv.org/abs/2505.21301v1 |
of a valid, attested exemplar, leading to the overgeneralization of that structure across subse- quent, unattested or spurious exemplars. This imi- tation is often form-driven rather than grounded in semantic plausibility or real-world usage. This phe- nomenon typically arises when a salient exemplar 13https://en.wikip... | https://arxiv.org/abs/2505.21301v1 |
the human exper- iment’s results. ANIMALS BODY PARTS CLOTHES FOODS FURNISHING FURNITURE Cane (dog) Capelli (hair) Scarpa (shoe) Pasta (pasta) Vaso (vase) Sedia (chair) 1 pastore tedesco riccio stivale spaghetti di fiori poltrona 2 segugio ricci sandalo fettuccine di rame a dondolo 3 rottweiler afro ciabatta penne di cr... | https://arxiv.org/abs/2505.21301v1 |
produced by the human study group but still considered valid, with more than 15 occurrences in the ItTenTen corpus. Exemplars with lower frequency are denoted by alight-yellow background . A light-red background indicates unattested exemplars, which are regarded as hallucinations. ANIMALS BODY PARTS CLOTHES FOODS FURNI... | https://arxiv.org/abs/2505.21301v1 |
11 a schema logico coperto da legno a fioritura primaverile per plastica a cingoli motrici 12 a schema numerico fiorito da salsa d’appartamento acrilica agricolo cingolato 13 a parole sovrapposte scoperto da metallo d’altura a base di silano a cingoli motrici 4x2 14 a parole nascoste giardino da risotto cespuglioso a b... | https://arxiv.org/abs/2505.21301v1 |
PLANTS STATIONERY VEHICLES avg llama-3.2-3B 0.38 0.60 na na 0.60 0.50 0.67 0.75 na 0.77 1.00 0.60 0.65 llama-3.1-8B 0.38 0.80 na na 0.60 0.25 0.33 0.75 na 0.54 1.00 0.60 0.58 llama-3.1-70B 0.38 1.00 na na 0.80 0.75 0.67 0.75 na 0.85 1.00 0.40 0.73 mistral-7B 0.62 0.60 na na 0.80 0.50 0.33 0.00 na 0.54 0.50 0.60 0.50 ne... | https://arxiv.org/abs/2505.21301v1 |
1.00 1.00 0.71 0.67 0.75 1.00 0.20 0.00 0.69 mixtral-8x7B 0.75 0.33 0.75 0.86 1.00 1.00 0.71 0.50 0.62 0.50 0.80 0.50 0.69 llava-7B 0.50 0.33 0.50 0.86 0.80 1.00 0.57 0.67 0.50 0.50 0.60 0.50 0.61 idefics2-8B 0.50 0.67 0.50 0.71 0.80 0.50 0.57 0.33 0.25 0.50 0.60 0.00 0.49 category avg 0.50 0.54 0.69 0.80 0.85 0.88 0.6... | https://arxiv.org/abs/2505.21301v1 |
arXiv:2505.21317v1 [cs.LG] 27 May 2025A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features Ihab Bendidi1 2 3Yassir El Mesbahi1 2Alisandra K. Denton1 2Karush Suri1 2Kian Kenyon-Dean2 Auguste Genovesio3 *Emmanuel Noutahi1 2 * 1Valence... | https://arxiv.org/abs/2505.21317v1 |
different modalities are considered ”paired” if they belong 1 A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomic Representations to the same biological state or metadata (Xi et al., 2024). In our case, this means transcriptomics and microscopy imaging samples that are not from ... | https://arxiv.org/abs/2505.21317v1 |
foundation models with train- able adapters. It achieves state-of-the-art performance in cross-modal distillation under data-scarce conditions for our biological modalities. •We introduce PEA,Perturbation Embedding Augmentation, a novel biologically inspired data augmentation technique for representations of transcript... | https://arxiv.org/abs/2505.21317v1 |
but while unimodal datasets (Replogle et al., 2022; Chandrasekaran et al., 2023; Fay et al., 2023) are growing, weakly paired multimodal datasets remain scarce. Recent methods (Xi et al., 2024; Watkinson et al., 2024; Sanchez-Fernandez et al., 2023; Xie et al., 2023) address this kind of limitation for different modali... | https://arxiv.org/abs/2505.21317v1 |
the teacher embedding space, this avoids dependence on massive amounts of paired data for encoder training and ensures one-way knowledge transfer from the teacher to the student, mitigating mutual drift and feedback from the student to the teacher. Batch Correction for Data Augmentation. In biolog- ical datasets, batch... | https://arxiv.org/abs/2505.21317v1 |
ET(xT) Sample batch correction function A∼ A Drop a random subset of steps in A→A′ Sample a random subset of control samples X(c) S Apply batch correction: za S=A′(zS, X(c) S) Compute transformed embeddings: hS=fS(za S) Apply TVN correction to teacher embeddings: zb S= B(zT)and compute CLIP loss : L=−BX i=1logexp( sim(... | https://arxiv.org/abs/2505.21317v1 |
pro- files. The average of these metrics provides a comprehensive measure of interpretability preservation. Success is defined as improving retrieval scores while main- taining interpretability metrics comparable to unimodal transcriptomic representations. This dual focus ensures that the student representations do not... | https://arxiv.org/abs/2505.21317v1 |
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