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multi-head self-attention mechanism [ 15], layer normalization [ 4], a position-wise feedforward net- work, and residual connection [ 29]. The encoder-decoder structure serves as the foundation for many early-stage influential LLMs, such as T5 [ 66], and BART [ 45]. These models have demonstrated strong capabilities on... | https://arxiv.org/abs/2505.16270v1 |
arXiv:2505.16276v1 [cs.AI] 22 May 2025 peer reviewed and to appear in the ESWC 2025 Workshops and Tutorials Joint ProceedingsHow do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance Desiree Heim1,2,*, Lars-Peter Meyer3,4, Markus Schröder1, Johannes Fre... | https://arxiv.org/abs/2505.16276v1 |
the comprehension of KGs [ 11,12], their schemata and query languages [10, 13]. The results on this benchmark not only show single model performances but might also provide valuable indications on KGE-specific scaling laws of LLMs. Such scaling laws typically examine LLM task performances concerning their model sizes, ... | https://arxiv.org/abs/2505.16276v1 |
Following the analysis, we summarize and discuss the gained insights in Section 5. Section 6 concludes this paper and gives an outlook to future work. 2. Related Work In order to compare the vast amount of LLMs, there are several LLM leaderboards, which rank various LLMs based on a selection of benchmarks or workloads.... | https://arxiv.org/abs/2505.16276v1 |
to evaluate their performance. Since also the evaluation is automated, the same experiments can be repeated which increases reproducibility and gives a broader sample size for statistical analysis to take the probabilistic nature of LLM-generated answers into account. The dataset used in this work evaluated over 30 ope... | https://arxiv.org/abs/2505.16276v1 |
have a similar prompt and evaluation but differ e.g. by the serialization format (JSON-LD, N-Triples, Turtle, XML) presented to the LLM. For each open LLM, respectively 50 repetitions per task variation were executed. To assess the performance of the LLMs, tasks compute several measures based on the LLM answers with va... | https://arxiv.org/abs/2505.16276v1 |
format. For the given answer, the task computes listTrimF1 as F1-measure on trimmed list entries without leading or trailing whitespaces. The textHttpF1 measure is an F1-measure on IRI-like answer parts starting, e.g., with “ http:// ”. RdfFriendCount This task presents a small KG with nodes of one type and edges of on... | https://arxiv.org/abs/2505.16276v1 |
a closer look at task performances with respect to model sizes and shed light on two aspects: comparison of performances between different size categories (Section 4.2) and the development of scores with respect to model sizes and families (Section 4.3). 4.1. Overall Task Performance To get an overview of the benchmark... | https://arxiv.org/abs/2505.16276v1 |
central 0.68 0.32 0.08 1.00 textHttpF1 fragment 0.75 0.27 0.30 1.00 xml listTrimF1 central 0.70 0.30 0.12 1.00 textHttpF1 fragment 0.77 0.23 0.42 1.00 RdfFriendCount jsonld f1 central 0.17 0.37 0.00 1.00 nt f1 central 0.17 0.37 0.00 1.00 turtle f1 central 0.06 0.22 0.00 0.47 xml f1 central 0.29 0.45 0.00 1.00 RdfSyntax... | https://arxiv.org/abs/2505.16276v1 |
To examine whether LLM size affects task performance, we first divided the LLMs into four groups with respect to their sizes. We classify models into the size categories tiny [0−3𝐵], small (3𝐵−8𝐵], medium (8𝐵−33𝐵]and large (33𝐵−72𝐵]. Subsequently, to assess whether there are any significant differences in achiev... | https://arxiv.org/abs/2505.16276v1 |
mainly plateau effects occurred. Similarly, the three insignificant differences between scores of the pair tiny-small are plateaus. In contrast to Table 3 The table shows the mean scores of central measures per model size groups and the group-wise similarity. The models are grouped into the size groups tiny (t, [0−3𝐵]... | https://arxiv.org/abs/2505.16276v1 |
addition, the plateau and ceiling effects can be seen. Furthermore, additional insights are visible in the plots. For reference, the plots also show the highest average score achieved by a proprietary model included in the benchmark run as a red horizontal line. Here, we see that, except for the Text2Sparql task, the b... | https://arxiv.org/abs/2505.16276v1 |
Qwen2.5 models stand out. The two included Mixture-of-Experts (MoE) LLMs, namely Phi-3.5-MoE-instruct (42B parameters thereof 6.6B active) and Qwen2-57B-A14B-Instruct (57B parameters thereof 14B active) show for most tasks scores similar to models of a similar size regarding their total parameters that use all paramete... | https://arxiv.org/abs/2505.16276v1 |
make sense to use smaller models since it saves costs and does not affect the task performance much. Some smaller models also perform comparably well. However, the performance across individual smaller models varies. Furthermore, also Figure 1 confirms that even some small ( 8B) or medium-sized ( 13B) models might be a... | https://arxiv.org/abs/2505.16276v1 |
to also consider smaller models for similarly complex tasks. For the RdfFriendCount andText2Sparql tasks, the benchmark scores were overall low, plateau effects spanned up to the largest models analysed. Here, we can conclude that the capabilities of SOTA open LLMs are not yet sufficient to solve tasks of this complexi... | https://arxiv.org/abs/2505.16276v1 |
(2024) 102324. doi: 10.1016/j.datak.2024.102324 . [5]T. A. Taffa, R. Usbeck, Leveraging LLMs in scholarly knowledge graph question answering (2023). doi:10.48550/ARXIV.2311.09841 . [6]M. Hofer, J. Frey, E. Rahm, Towards self-configuring knowledge graph construction pipelines using llms - a case study with rml, in: Fift... | https://arxiv.org/abs/2505.16276v1 |
Zhang, D. Narayanan, Y. Wu, A. Kumar, B. Newman, B. Yuan, B. Yan, C. Zhang, C. Cosgrove, C. D. Manning, C. Ré, D. Acosta- Navas, D. A. Hudson, E. Zelikman, E. Durmus, F. Ladhak, F. Rong, H. Ren, H. Yao, J. Wang, K. Santhanam, L. Orr, L. Zheng, M. Yuksekgonul, M. Suzgun, N. Kim, N. Guha, N. Chatterji, O. Khattab, P. Hen... | https://arxiv.org/abs/2505.16276v1 |
Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, J. W. Rae, O. Vinyals, L. Sifre, Training compute-optimal large language models (2022). arXiv:2203.15556 . [28] W. H. Kruskal, W. A. Wallis, Use of ranks in one-criterion variance analysis, ... | https://arxiv.org/abs/2505.16276v1 |
arXiv:2505.16277v1 [cs.CL] 22 May 2025Spontaneous Speech Variables for Evaluating LLMs’ Cognitive Plausibility Sheng-Fu Wang Academia Sinica Institute of Linguistics Taipei, Taiwan sftwang@gate.sinica.edu.twLaurent Prévot CNRS & MEAE CEFC Taipei, Taiwan laurent.prevot@cnrs.fr Jou-An Chi Graduate Institute of Linguistic... | https://arxiv.org/abs/2505.16277v1 |
results across different languages, which should offer valuable insights into both the learning models and the underlying learning processes. While gathering a 100M token conversational dataset based on real spoken conversational data might be challenging, a 10M token dataset is acces- sible for languages like English,... | https://arxiv.org/abs/2505.16277v1 |
speech (POS) and morphology, as opposed to relying on simpler surface-level features like position-based or length- based features. More recently, several studies have probed LLMs to better characterize their perfor- mance across various domains, particularly with regard to their linguistic competence versus com- monse... | https://arxiv.org/abs/2505.16277v1 |
ability to predict neurophys- iological data (Pasquiou et al., 2022). Focusing on spontaneous speech, (Rauchbauer et al., 2019; Hmamouche et al., 2024) examined the predictabil- ity of fMRI-derived signals from conversational variables, including lexical information. In terms of specialized language models, (Cabiddu et... | https://arxiv.org/abs/2505.16277v1 |
In spontaneous speech, some chunks of speech are produced in a reduced manner, both in terms of duration and articulatory amplitude. The location of these re- ductions is not random. For example, studies have suggested that speakers tend to smooth the informa- tion density of their speech signal over time, with reducti... | https://arxiv.org/abs/2505.16277v1 |
et al. (2023)). 4 Method 4.1 Pre-training LLMs We pretrained multiple RoBERTa models (an archi- tecture choice motivated in part by a more direct comparison of our topline choice, XLM-RoBERTa, which has a more manageable size for fine-tuning) using three corpora in different languages: English, French, and Taiwan Manda... | https://arxiv.org/abs/2505.16277v1 |
ity of their speech transcript alignment, down to the syllable or even the segment level. 4.2.1 Speech Reduction There are several methods to determine whether a portion of speech is reduced. Following approaches in the literature, we first derived ratios of every word token’s actual duration and its expected du- ratio... | https://arxiv.org/abs/2505.16277v1 |
This condition is not fair with the other models, since it is pretrained on much larger data set. It was originally included both as a topline and to assess the feasability of the task. For more efficient fine- tuning, these models underwent vocabulary prun- ing (Yang et al., 2022) on the training texts for our three l... | https://arxiv.org/abs/2505.16277v1 |
fine-tuning results, it suggests that at least for reduction, the fine-tuning task is not as trivial as something that can be predicted from base models’ behavior. Figure 1: F-scores on the benchmarks as a function of model (x), task and language (prominence). Model comparisons are based on Bayesian regression analyses... | https://arxiv.org/abs/2505.16277v1 |
density, en- tropy, and predictability and phonetic realizations. However, the prediction of these phenomena can- not be reduced to information-theoretic explana- tions alone, and our analyses do show some diver- gences between fine-tuning experiments and direct correlations between perplexity and benchmark la- bels. A... | https://arxiv.org/abs/2505.16277v1 |
texts perform differently. In the future, it will be crucial to explore more nuanced variations in training data, such as balanc- ing conversational speech, child-directed speech, and simple texts as well as developping comple- mentary evaluation metrics. From a broader perspective, we hope to show that benchmarks like... | https://arxiv.org/abs/2505.16277v1 |
changed the world . Random House. Kawai Chui and Huei-ling Lai. 2008. The nccu corpus of spoken chinese: Mandarin, hakka, and southern min. Taiwan Journal of Linguistics , 6(2). Uriel Cohen Priva. 2012. Sign and signal: Deriving linguistic generalizations from information utility . Ph.D. thesis, Stanford University. Ur... | https://arxiv.org/abs/2505.16277v1 |
59th Annual Meeting of the Asso- ciation for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing , pages 5203–5217. Association for Com- putational Linguistics. Stephen C Levinson. 2020. On the human "interaction engine". In Roots of human sociality , pages 39–69. Routle... | https://arxiv.org/abs/2505.16277v1 |
pairs. arXiv preprint arXiv:2406.19232 . Aarne Talman, Antti Suni, Hande Celikkanat, Sofok- lis Kakouros, Jörg Tiedemann, and Martti Vainio. 2019. Predicting prosodic prominence from text with pre-trained contextualized word representations. In Proceedings of the 22nd Nordic Conference on Com- putational Linguistics , ... | https://arxiv.org/abs/2505.16277v1 |
Proceedings of the 60th An- nual Meeting of the Association for Computational Linguistics: System Demonstrations , pages 35–43, Dublin, Ireland. Association for Computational Lin- guistics. Yian Zhang, Alex Warstadt, Xiaocheng Li, and Samuel R. Bowman. 2021. When do you need bil- lions of words of pretraining data? In ... | https://arxiv.org/abs/2505.16277v1 |
appear at least 50 times. Table 3: The most over-predicted and under-predicted words for each task. For each pretraining scheme (wiki, con- versational, mixed, ROBERT A-base and ROBERT A-large), we selected the best-performing model, and calculated every word’s rate difference. Only the top-5 and bottom-5 words in rate... | https://arxiv.org/abs/2505.16277v1 |
arXiv:2505.16281v1 [cs.CL] 22 May 2025HIMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation Shijie Zhang1*and Renhao Li1,2*†and Songsheng Wang1*and Philipp Koehn3and Min Yang2 and Derek F. Wong1 1NLP2CT Lab, Department of Computer and Information Science, University of Macau 2Shenzhen Insti... | https://arxiv.org/abs/2505.16281v1 |
rics (MQM),1includes detailed information regard- ing error spans, categories, and severity for each translated sentence, which facilitates more precise finetuning and thorough analysis of MTE mod- 1themqm.org. 1 els. The xCOMET model (Guerreiro et al., 2024) is finetuned on such data for interpretable scores, while it... | https://arxiv.org/abs/2505.16281v1 |
and severity eval- uation, achieving an average 89% improvement in F1-score and 95% enhancement in Recall over the best-performing baseline. 2 Related Work 2.1 Machine Translation Evaluation The objective of MTE is to assess the quality of machine-translated text. Traditional statistics- based metrics (Papineni et al.,... | https://arxiv.org/abs/2505.16281v1 |
the weighted values of errors. within Nature Language Generation (NLG). Chat- Eval (Chan et al., 2023) employs LLMs in a frame- work similar to human group discussions. MAT- Eval (Li et al., 2024) introduces architectures that rely on multi-round discussion and summarization for evaluation. Similarly, a courtroom-inspi... | https://arxiv.org/abs/2505.16281v1 |
error is accurately identified during the initial phase, the correction should either rectify the issue or mitigate its severity, thereby validating the initial judgment. Otherwise, this may indicate a false positive in the previous evaluation, suggest- ing that the purported error might not exist. This verification me... | https://arxiv.org/abs/2505.16281v1 |
0.0948 0.0144 COMETKIWI 0.365 0.487 0.1821 0.0386 0.212 0.277 0.1351 0.0221 xCOMET-XL 0.394 0.515 0.1210 0.0317 0.283 0.355 0.0567 0.01154o-miniGEMBA-MQM 0.381 0.464 0.0755 0.0131 0.270 0.316 0.0420 0.0052 M-MAD 0.350 0.435 0.0379 0.0033 0.282 0.327 0.0164 0.0007 HiMATE (ours) 0.404 0.502 0.0421 0.0051 0.293 0.338 0.02... | https://arxiv.org/abs/2505.16281v1 |
0.468 0.1614 0.0614 0.278 0.329 0.0698 0.0205 -SE+SR 0.370 0.477 0.1178 0.0362 0.283 0.332 0.0497 0.0106 -SE+SR+CD 0.404 0.502 0.0421 0.0051 0.293 0.338 0.0250 0.0026qwen2.5SA 0.341 0.413 0.0356 0.0031 0.183 0.200 0.0197 0.0017 HiMATE-SE 0.397 0.489 0.0572 0.0172 0.240 0.267 0.0341 0.0100 -SE+SR 0.406 0.496 0.0479 0.01... | https://arxiv.org/abs/2505.16281v1 |
0.382 0.460 0.093 0.344 0.427 0.100 M-MAD 0.333 0.386 0.124 0.316 0.392 0.108 0.311 0.408 0.116 HiMATE 0.368 0.436 0.218 0.425 0.529 0.260 0.377 0.491 0.294 10% 30% 50% 70% 90%0.00.20.40.60.81.0Precision HiMATE(ours) M-MAD GEMBA-MQM 10% 30% 50% 70% 90% Matching Threshold0.00.20.40.60.81.0Recall HiMATE(ours) M-MAD GEMBA... | https://arxiv.org/abs/2505.16281v1 |
Reference I hope you can find out about it. Annotation Major-Mistranslation-’know about it’ GEMBA-MQMMajor-Mistranslation-‘I hope you know about it’, Minor-Awkward-‘I hope you know about it’ M-MAD Stage1 Minor-Omission-‘know about it’ M-MAD Stage2&3 Minor-Omission-‘know about it’ HiMATE-SEMinor-Inappropriate for contex... | https://arxiv.org/abs/2505.16281v1 |
process presented in this study primarily fo- cuses on selected models and language pairs, rather than an exhaustive evaluation across diverse mod- els and languages. The current study does not eval- uate the applicability of the proposed framework to advanced reasoning models, which have shown promising potential in c... | https://arxiv.org/abs/2505.16281v1 |
Machine Translation , pages 1–46, Miami, Florida, USA. As- sociation for Computational Linguistics. Tom Kocmi and Christian Federmann. 2023a. GEMBA- MQM: Detecting translation quality error spans with GPT-4. In Proceedings of the Eighth Conference on Machine Translation , pages 768–775, Singapore. Association for Compu... | https://arxiv.org/abs/2505.16281v1 |
guage models are not fair evaluators. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 9440–9450, Bangkok, Thailand. Association for Computational Linguistics. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, D... | https://arxiv.org/abs/2505.16281v1 |
methods is gpt-4o-mini. A higher Kendall’s correlation coefficient ( τ) and Spearman’s correlation coefficient ( s), as well as a lower Mean Absolute Er- ror (MAE) and Mean Squared Error (MSE), indicate better alignment with human evaluations. Reference- based methods are indicated with a gray background . The best res... | https://arxiv.org/abs/2505.16281v1 |
Ta- bles 11 and 12. The second example, also derived from MQM22 ZH-EN, is illustrated from Table 13 to Table 15. The third example, originating from MQM22 EN-DE, is presented in Tables 16 and 17. 12 Table 8: Core error subtypes and descriptions after refinement. Here, ’Core’ refers to high-level error categories at tie... | https://arxiv.org/abs/2505.16281v1 |
source Chinese sen- tence and its English translation. You need to correct the errors in the translation.You are an evaluator conducting a manual translation evaluation based on the MQM framework. Here you need to compare the following original translation and the corrected translation based on the error definition. Ta... | https://arxiv.org/abs/2505.16281v1 |
meaning of the source text "希望你们了解一下 " which more appropriately translates to "I hope you all understand" or "I hope you all are aware" The use of "know about it" implies a lesser degree of understanding and does not capture the intent of encouraging awareness or understanding, thus constituting a major mistranslation.... | https://arxiv.org/abs/2505.16281v1 |
about it’ tier-2 agent: "Error has been corrected." HiMATE-CD-Round1Major-Mistranslation-‘know about it’ tier-1 agent: "Error Exist: Yes Error Severity: Major. The evaluator’s assessment is correct. The translation "I hope you know about it" does not accurately capture the meaning of the source sentence " 希望你们了解一下 " wh... | https://arxiv.org/abs/2505.16281v1 |
the overall translation is understandable, this specific phrase could lead to ambiguity regarding the stages mentioned, making it a minor grammatical error." Minor-Inconsistency-‘the late stage’ tier-2 agent: "The translation uses ¨the late stage ¨which is not a commonly used term in English for " 后期" in this context. ... | https://arxiv.org/abs/2505.16281v1 |
in the translation does not correspond to the source text. However, the severity should be considered neutral rather than minor, as this error does not belong to an addition error. " Minor-Inconsistency-‘the late stage’ tier-1 agent: "Error Exist: No. Er- ror Severity: Neutral. The evaluator correctly identified the ex... | https://arxiv.org/abs/2505.16281v1 |
noch stärker gemacht wurde’ tier-2 agent: "The phrase "die diese Woche noch stärker gemacht wurde" implies that the rivalry was made stronger, whereas the original phrase suggests a progression in intensity due to recent events. A more accurate translation could clarify that the rivalry was intensified rather than simp... | https://arxiv.org/abs/2505.16281v1 |
arXiv:2505.16293v1 [cs.CL] 22 May 2025Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA Rishabh Maheshwary§Masoud Hashemi§Khyati Mahajan§ Shiva Krishna Reddy Malay§Sai Rajeswar†Sathwik Tejaswi Madhusudhan§ Spandana Gella†Vikas Yadav§ §ServiceNow †ServiceNow Research {rishabh.maheshwary, vikas.yadav} @s... | https://arxiv.org/abs/2505.16293v1 |
reasoning traces created from multiple documents pose challenges for users to interpret precise reasoning. Redun- dant information can further affect readability. To address these issues, we propose a simple yet effective and scalable method called NotesWriting . At each retrieval step, NotesWriting produces con- cise ... | https://arxiv.org/abs/2505.16293v1 |
. ,st−1]. This is governed by a query formulation function Q(·): qt=Q(x,s<t) (1) For the first step, s<1=∅, and often q1=x. 2.Retrieval: The retriever fetches the top- krele- vant documents: Dqt=ret(qt). 3.Generation: The LM generates the next rea- soning step stusing the original input, previous steps, and the newly r... | https://arxiv.org/abs/2505.16293v1 |
CoT + query based on Step 1 Step 4 Written notes added to context Step 5 Final explanation and answer Where does the eastern sector of the Colorado orogeny extend? Query for retrieval Notes LLM Concise Notes Top Wiki pages What is the elevation range of the High Plains? Query for retrieval Colorado Orogeny Top Wiki pag... | https://arxiv.org/abs/2505.16293v1 |
local evi- dence summaries across iterations to prevent con- text overload. It involves multiple LLM calls per iteration focusing on planning for the next step with sub-questions, summarizing retrieved docu- ments, generating the next sub-questions, and judg- ing for whether there is sufficient information to answer th... | https://arxiv.org/abs/2505.16293v1 |
of iterations T, after which a final answer is synthe- sized based on the full history sand the collected notes{Ot}T t=1. This approach (illustrated in Fig- ure 1) aims to combine the structured reasoning of ReAct with the context management benefits of NotesWriting , leading to a more robust and efficient iterative RA... | https://arxiv.org/abs/2505.16293v1 |
context length of 128K. 4.3 NotesWriting Implementation Details ForNotesWriting , we utilize the Wikipedia API to fetch the top 5 relevant pages based on the query qt. Each retrieved Wikipedia page is converted to Markdown format using markdownify2before being processed by LM notes. Initial experiments revealed that fe... | https://arxiv.org/abs/2505.16293v1 |
all questions). Similarly, notes tokens represent the total number of input & output tokens across all steps by the notes writing LLM (averaged on all questions). Token counts are rounded to the nearest thousand. BM25 from an older Wikipedia dump. However, in initial experiments we observed that the older dump is outda... | https://arxiv.org/abs/2505.16293v1 |
where the main LLM input tokens reduces by at least 4x and 1.5x for GPT-4o-mini and LLaMA-3.1- 70B, with output tokens being almost comparable. Reduced average steps. ReNAct reduces the av- erage number of steps across all the benchmarks MultiHop-RAG (Table 2). For Frames andFanout- QA, the reduction is 2.23 & 1.86 for... | https://arxiv.org/abs/2505.16293v1 |
NotesWriting in coming up with correct stepwise plan and search query for retrieval. Figure 3 also shows that the in-correct questions (dashed red & blue line) have a higher number of steps that shows that it fails Figure 2: Quality evaluation of ReAct and ReNAct reasoning chain. Figure 3: Steps (smoothed) by ReNAct, R... | https://arxiv.org/abs/2505.16293v1 |
better with NotesWriting . We expect our work to significantly enhance the QA user experience, as focused information im- proves performance and reduced context lengths lower computational costs. We hope our NotesWrit- ingmethod can contribute towards better task han- dling at large. We will make our code publicly avai... | https://arxiv.org/abs/2505.16293v1 |
preprint arXiv:2305.06983 . Zhouyu Jiang, Mengshu Sun, Lei Liang, and Zhiqiang Zhang. 2025. Retrieve, summarize, plan: Advanc- ing multi-hop question answering with an iterative approach. Preprint , arXiv:2407.13101. Ziyan Jiang, Xueguang Ma, and Wenhu Chen. 2024. Longrag: Enhancing retrieval-augmented gener- ation wit... | https://arxiv.org/abs/2505.16293v1 |
Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Interleav- ing retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509 . Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou... | https://arxiv.org/abs/2505.16293v1 |
answering benchmark for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages 18–37,Bangkok, Thailand. Association for Computational Linguistics. A Appendix A.1 Infogent Implementation Details We use the official code provided ... | https://arxiv.org/abs/2505.16293v1 |
862 Hotpot-QA 46.2 53.8 26K 193 433K 650 MultiHop-RAG 36.0 65.6 189K 412 40K 324LLaMA-3.1-70BBaselineFanout-QA 21.0 8.4 197K 511 - - Frames 19.4 21.1 192K 732 - - Hotpot-QA 31.5 38.2 167K 557 - - MultiHop-RAG 35.4 64.8 429K 313 - - NotesWritingFanout-QA 36.0 22.9 154K 552 1.4M 7.6K Frames 26.9 33.3 116K 735 1.2M 3.9K H... | https://arxiv.org/abs/2505.16293v1 |
laude , ranked first in her class. Thought: Amy Coney Barrett attended Notre Dame Law School. Next, I need to find out which law school Ketanji Brown Jackson attended. Action: search[Ketanji Brown Jackson; Which law school did Ketanji Brown Jackson attend?] Observation: (Result 1) Ketanji Brown Jackson attended Harvard... | https://arxiv.org/abs/2505.16293v1 |
Watson (American politician); What is the confirmed birth city of Kirk Watson?] Observation: (Result 1) Kirk Watson - Oklahoma City Thought: Based on the confirmed information, Kirk Watson’s birth city is Oklahoma City. Now, I need to find the population of Oklahoma City in the 2000 United States census. Action: search... | https://arxiv.org/abs/2505.16293v1 |
decision. Action: search[Diego Costa; Which club did Diego Costa play for in the 2018 FIFA World Cup?] Observation: (Result 1) Diego Costa - Atlético Madrid Thought: Diego Costa played for Atlético Madrid when he was awarded the first FIFA World Cup Goal based on a V AR Decision. Action: finish[Atlético Madrid] Observa... | https://arxiv.org/abs/2505.16293v1 |
search[James A. Garfield’s mother; What was the maiden name of James A. Garfield’s mother?] Observation: No relevant information, try a different search term. Thought: It seems I’m having difficulty finding the maiden name of James A. Garfield’s mother. I will search for biographies of James A. Garfield to find more de... | https://arxiv.org/abs/2505.16293v1 |
and director best known for playing Jervis Tetch / Mad Hatter in the Fox crime series Gotham and as Owen in The Walking... Wikipedia Title: Gérard Depardieu Gérard Xavier Marcel Depardieu is a multinational French actor, known to be one of the most prolific in film history. An icon of French cinema, considered a world ... | https://arxiv.org/abs/2505.16293v1 |
did not provide the specific actress who portrayed Vasilisa Dragomir. I will select the “Vampire Academy” film page to find the cast details. Action: select[Vampire Academy (film)] Observation: 2014 fantasy comedy horror film by Mark Waters | Vampire Academy | | | Theatrical release poster | | | Directed by | Mark Wate... | https://arxiv.org/abs/2505.16293v1 |
Out.” I will select the “Spin Out (film)” page to find the cast. Action: select[Spin Out (film)] Observation: 2016 Australian film | Spin Out | | | — | — | | Theatrical film poster | | | Directed by | Tim Ferguson, Marc Gracie | | Written by | Edwina Exton, Tim Ferguson | | Produced by | Marc Gracie, David Redman | | S... | https://arxiv.org/abs/2505.16293v1 |
and *The Darkness* in 2016. I need to check if either of these films also features Xavier Samuel. Action: search[Mr. Church; Did Xavier Samuel star in Mr. Church?] Observation: (Result 1) Mr. Church - Xavier Samuel is listed as one of the stars in the film *Mr. Church*. Thought: I have confirmed that both Xavier Samuel... | https://arxiv.org/abs/2505.16293v1 |
the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced. Thought: I need to determine which General Motors vehicle is named after the largest ward in Monaco and find out the first model year of that vehicle. After that, I will find out how many people had walke... | https://arxiv.org/abs/2505.16293v1 |
Raw Hero?] Observation: (Result 1) Raw Hero - *Raw Hero* was serialized in Kodansha’s *Evening*. Thought: The manga series Raw Hero was serialized in Kodansha’s *Evening* magazine. Now, I need to find out who the founder of Kodansha is. Action: search[Kodansha; Who is the founder of Kodansha?] Observation: (Result 1) K... | https://arxiv.org/abs/2505.16293v1 |
midst of his "Lost Weekend". "Walls and Bridges" was an American "Billboard" number-one album and featured two hit singles, "Whatever Gets You thru the Night" and "#9 Dream". The first of these was Lennon’s first number-one hit in the United States as a solo artist, and his only chart-topping single in either the US or... | https://arxiv.org/abs/2505.16293v1 |
of Vandenbrink Design, inspired by the 1962 Ferrari 250 GTO as a tribute. 1969 World Sportscar Championship - The 1969 World Sportscar Championship was the 17th season of FIA World Sportscar racing, featuring Group 6 Prototype Sports Cars, Group 4 Sports Cars, and Group 3 Grand Touring Cars. Ferrari 250 GTO - The Ferra... | https://arxiv.org/abs/2505.16293v1 |
If the content is already extracted - start your answer with NO# • If you can extract relevant information - start your answer with YES# Example answers: •YES#Western philosophy originated in Ancient Greece in the 6th century BCE with the pre-Socratics. •NO#No relevant context. Context: {Context} Previous Context: {Pre... | https://arxiv.org/abs/2505.16293v1 |
Extremely redundant: Most of the chain rehashes prior reasoning with no new value. Criterion 3: Clarity and Coherence of the Reasoning Chain Definition: Examine how clearly and logically the reasoning chain progresses from the question to the final answer. This includes whether steps are easy to follow, logically conne... | https://arxiv.org/abs/2505.16293v1 |
ToDi: Token-wise Distillation via Fine-Grained Divergence Control Seongryong Jung1,2, Suwan Yoon1, DongGeon Kim1, Hwanhee Lee1* 1Department of Artificial Intelligence, Chung-Ang University,2Dmtlabs {jungsr1116, swyoon0312, golddonggun, hwanheelee}@cau.ac.kr Abstract Large language models (LLMs) offer im- pressive perfo... | https://arxiv.org/abs/2505.16297v1 |
Forward KL (FKL) and Reverse KL (RKL) to minimize the discrepancy between teacher and student distribu- tions (Hinton et al., 2015; Gu et al., 2024). How- ever, as depicted in the example for FKL and RKL in Figure 1, these approaches apply a single di- 1arXiv:2505.16297v1 [cs.CL] 22 May 2025 vergence uniformly across t... | https://arxiv.org/abs/2505.16297v1 |
mode (Minka et al., 2005; Koller and Friedman, 2009; Chan et al., 2022; Wang et al., 2023a). To counter these extremes, Wen et al. (2023) adopted the symmetric Jensen–Shannon Di- vergence (JSD), and Agarwal et al. (2024) gen- eralized it to interpolate between FKL and RKL. Skewed KL variants (SKL, SRKL) further mix the... | https://arxiv.org/abs/2505.16297v1 |
the student parameters and y<t= [y1, . . . , y t−1]Case Forward KL Reverse KL p>qθ↑Strong push-up ≈Weak push-up p<qθ≈Weak pull-down ↓Strong pull-down Table 1: Complementary training signals of FKL vs. RKL. are the tokens generated before step t. During KD, the loss is typically instantiated as either the FKL or the RKL... | https://arxiv.org/abs/2505.16297v1 |
teacher–student probabilities at each token, we con- struct a toy example by defining teacher distribu- tionp(x)and student distribution q(x). Figure 2 illustrates the comparison of gradient magnitudes according to the relative relationship between the teacher distribution p(x)and the student distribu- tionq(x)in a toy... | https://arxiv.org/abs/2505.16297v1 |
expressed in the form αt,i(r) =σ(βlogr)with an appropriate scaling factor β. The function Wshould assign a larger value (thus increasing the contribution of FKL) when p(vi|y<t,x)> qθ(vi|y<t,x), so as to boost the student’s probability. Conversely, when qθ(vi| y<t,x)> p(vi|y<t,x),a smaller function value (favoring RKL) ... | https://arxiv.org/abs/2505.16297v1 |
result is shown in bold , and the second best is underlined . Generalized ToDi. Any function satisfying the four weight conditions introduced above can take many forms. To explore this design space and unify various weighting strategies, we introduce a scal- ing hyperparameter β∈R. By incorporating β into the sigmoid i... | https://arxiv.org/abs/2505.16297v1 |
Table 3 presents the performance of the teacher and student mod- els under different teacher–student configurations, compared across various knowledge distillation methods. Our proposed ToDi achieves the high- est average score on all five instruction-following tasks for both teacher–student pairs, outperforming all ba... | https://arxiv.org/abs/2505.16297v1 |
of FKL and RKL globally across the entire vocabu- lary—incurs a time complexity of O(VlogV)due to the required sorting operations. In contrast, ToDi performs computations adaptively on a per-token basis without any sorting during loss computation. As a result, it preserves linear time complexity O(V)with respect to voc... | https://arxiv.org/abs/2505.16297v1 |
that the teacher and stu- dent share an identical vocabulary, which limits its direct applicability when the two models employ different vocabularies. Moreover, ToDi requires ac- cess to the full token probability distribution of the teacher model, restricting its use to open-source LLMs that expose per-token logits. E... | https://arxiv.org/abs/2505.16297v1 |
Process- ing Systems , volume 35, pages 33716–33727. Curran Associates, Inc. Harold Jeffreys. 1946. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences , 186(1007):453–461. Jongwoo Ko, Sungnyun Kim, Tianyi Chen, and... | https://arxiv.org/abs/2505.16297v1 |
Ashok, Arut Selvan Dhanasekaran, An- jana Arunkumar, David Stap, Eshaan Pathak, Gian- nis Karamanolakis, Haizhi Lai, Ishan Purohit, Is- hani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, and 16 others. 2022. Super- NaturalInstructions: Generalization via declarative instructions on 1600+ NLP task... | https://arxiv.org/abs/2505.16297v1 |
we prove the following: •Ifp(vi|y<t,x)> q θ(vi|y<t,x),then logp(vi|y<t,x) qθ(vi|y<t,x)>0⇒αt,i>0.5,which increases the contribution of FKL. •Ifqθ(vi|y<t,x)> p(vi|y<t,x),then logp(vi|y<t,x) qθ(vi|y<t,x)<0⇒αt,i<0.5,which increases the contribution of RKL. •Letr=p(vi|y<t,x)/qθ(vi|y<t,x),so thatαt,i=σ(logr).Then dαt,i dr=σ(... | https://arxiv.org/abs/2505.16297v1 |
response lengths exceeding 11 tokens; SelfInst has 242 examples; and VicunaEval con- sists of 80 examples. For response generation, we used random seeds {10, 20, 30, 40, 50} and report the average ROUGE-L score across these seeds. E Experiments on Additional Models We further evaluated ToDi’s performance across diverse... | https://arxiv.org/abs/2505.16297v1 |
18.24 Table 7: Comparison of ROUGE-L scores of GPT-2 student models under different values of the scaling parameter β. •Low-sensitivity regime ( β < 1): Asβde- creases, the sigmoid’s slope becomes shal- lower, causing the weight αt,ito converge toward 0.5. This nearly fixed combination of FKL and RKL reduces responsive... | https://arxiv.org/abs/2505.16297v1 |
INFERENCE DYNAMICS : Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling Haochen Shi1*, Tianshi Zheng1∗, Weiqi Wang1∗, Baixuan Xu1, Chunyang Li1, Chunkit Chan1, Tao Fan1,2, Yangqiu Song1, Qiang Yang3 1The Hong Kong University of Science and Technology2WeBank 3The Hong Kong Polytechnic Un... | https://arxiv.org/abs/2505.16303v1 |
Rou- terDC (Chen et al., 2024), C2MAB-V (Dai et al., 2024), and P2L (Frick et al., 2025) offer more sophisticated mechanisms for capturing model strengths, their primary limitation lies in the sig- nificant retraining or recalibration required to ef- fectively support newly introduced LLMs, hinder- ing their agility in... | https://arxiv.org/abs/2505.16303v1 |
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