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the recent rewards. C.2.2 Adaptive Data Optimization (ADO) Jiang et al. (2024) propose Adaptive Data Optimiza- tion (ADO), where they adjust the dynamic weights wtat a fixed interval tupdate by fitting a domain scal- ing law for each domain. The domain scaling law predicts the i-th domain’s training loss after train- i... | https://arxiv.org/abs/2505.21598v1 |
arXiv:2505.21646v1 [cs.CL] 27 May 2025Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts Lei Zhang ( ), Markus Stricker Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-University Bochum, Universitätsstraße 150, 44780 Bochum, Germany {lei.zhang-w2i, markus.stricker}... | https://arxiv.org/abs/2505.21646v1 |
models to substi- tute expensive DFT simulations require high-quality datasets that are available at scale and variety to match the parameter space of technologically interesting materials. The scientific literature offers an alternative resource for material discovery. Research articles and patents contain hidden know... | https://arxiv.org/abs/2505.21646v1 |
All code and data needed to reproduce our workflow are available. The code can be found in [29], references to all datasets are provided when they are introduced further below. 2.1 Corpus Collection and Preprocessing Fig.1showsaschematicoverviewofourmethodology.First,wecollectarelevant corpus of documents using the Pap... | https://arxiv.org/abs/2505.21646v1 |
resolution for a given material system containing several elements, we can use the two-dimensional similarity scores to calculate a centroid of the based on N different compositions in the material system as follows: centroid =1 NNX i=1 Sdielectric (i) Sconductivity (i) . (1) Convergence Criterion: The previous step ... | https://arxiv.org/abs/2505.21646v1 |
iterations to reach the threshold, showing a more extended effort to refine the embedding space. – NiPdPtRu: Converged after 14 iterations, balancing between moderate re- finement complexity and stabilization. Title Suppressed Due to Excessive Length 7 These results for different material systems exhibit a large variab... | https://arxiv.org/abs/2505.21646v1 |
model predictions of -1.41mA/cm2. Despite this, the selected-corpus model captures essential in- formation from the full corpus, demonstrating its ability to approximate high- performing materials with a significantly smaller number of documents. Title Suppressed Due to Excessive Length 9 NiPdPtRu Material System012345... | https://arxiv.org/abs/2505.21646v1 |
retains 4 entries compared to 27 in the full model. The minimum current density remains −0.37mA/cm2, while the maximum is lower at −0.34mA/cm2compared to −0.06mA/cm2 in the full model. HER Systems (AgAuPdPtRh, AgAuPdPtRu at −300mV): –InAgAuPdPtRh , the selection-based model retains 29 entries compared to 16 in the full... | https://arxiv.org/abs/2505.21646v1 |
producing noise [20]. Our iterative selection loop, which operates in the embedding space and discards informa- tion that does not shift the centroid meaningfully, offers a possibility to filter noise or at least avoid duplicated information: our framework avoids documents which contribute little new information. As AI... | https://arxiv.org/abs/2505.21646v1 |
Batchelor, T.A., Pedersen, J.K., Winther, S.H., Castelli, I.E., Jacob- sen, K.W., Rossmeisl, J.: High-entropy alloys as a discovery platform for electrocatalysis. Joule 3(3), 834–845 (2019). https://doi.org/https: //doi.org/10.1016/j.joule.2018.12.015 ,https://www.sciencedirect.com/ science/article/pii/S254243511830621... | https://arxiv.org/abs/2505.21646v1 |
cited by: 5119 18. Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C.: Materi- als design and discovery with high-throughput density functional the- ory: The open quantum materials database (oqmd). JOM 65(11), 1501 – 1509 (2013). https://doi.org/10.1007/s11837-013-0755-4 ,https://www. scopus.com/inward/reco... | https://arxiv.org/abs/2505.21646v1 |
arXiv:2505.21693v1 [cs.CL] 27 May 2025 MAKIEVAL: AMultilingual Automatic Wi KIdata-based Framework for Cultural Awareness Evaluation for LLMs Raoyuan Zhao, Beiduo Chen, Barbara Plank, Michael A. Hedderich MaiNLP, Center for Information and Language Processing, LMU Munich Munich Center for Machine Learning (MCML) {rzhao... | https://arxiv.org/abs/2505.21693v1 |
having dinner, the same LLM produces outputs that differ in food entity specificity (i.e., whether the food items are culturally specific to China), diversity, and cross-lingual consistency. Capturing these differences across languages in evaluation, especially under the flexible nature of text generation models, remai... | https://arxiv.org/abs/2505.21693v1 |
systems (Ma et al., 2024; Agarwal et al., 2024; Shi et al., 2024), political ideology (Bang et al., 2024), gen- der norms (Wan et al., 2023), or expressions of stereotypes or hate (Cheng et al., 2023; Deshpande et al., 2023). Others focus on concrete, content- level evaluations, assessing how LLMs engage with specific ... | https://arxiv.org/abs/2505.21693v1 |
generation, cul- tural entity extraction, wikidata-based entity match and metric-based analysis. 3.1 Text Generation We prompt LLMs to generate culturally grounded texts in multiple languages. Unlike prior evalua- tions that rely on constraint output formats such as cloze tasks or entity listing, our setup encour- ages... | https://arxiv.org/abs/2505.21693v1 |
Book Title Q212340 Wer die Nachtigall stört United States Book Title Q26505 Der alte Mann und das Meer United States Book Title Q74287 Der Hobbit United Kingdom Book Title Q12132683 科幻小 说 NA Book Genre Q464928 追 忆 似水年 华 France Book Title Q170583 傲慢与偏 见 United Kingdom Book Title To Kill a Mockingbird 追 忆 似水年 华 傲慢与偏 见Der... | https://arxiv.org/abs/2505.21693v1 |
the entity’s QID, surface la- bels in multiple languages, granularity tags, and country/region-level information. We provide an evaluation of the performance in Appendix C. 4 Evaluation Metrics We propose four metrics to evaluate different as- pects of cultural awareness in multilingual lan- guage models: Granularity ,... | https://arxiv.org/abs/2505.21693v1 |
(US) andDeepSeek-V3 (China) (Liu et al., 2024a), which are widely used but not openly available or easily deployable on local infrastructure. In addition, we evaluate Llama-3.3-70B-Instruct (Grattafiori et al., 2024) and aya-expanse-8b (Canada) (Üstün et al., 2024). The inclusion of both Llama3 variants en- ables a con... | https://arxiv.org/abs/2505.21693v1 |
States, United Kingdom, Canada, Australia, Nigeria de Germany es Mexico, Spain, Argentina fa Iran hi India it Italy ja Japan ko South Korea th Thailand tr Turkey zh China zh-tw Taiwan Table 2: Prompt languages and country/region men- tioned countries/regions in our experiments. Coun- try/region names are color-coded by... | https://arxiv.org/abs/2505.21693v1 |
0.0023 0 0 0.0076 0 0.0021 0 0.0035 0 0 0 0 0 0.0035 0.007 0.056 0.014 0.0035 0.015 0 0 0 0.0035 0 0 0 0.0019 0 0 0 0 0.0019 0 0 0 0.027 0.029 0.0086 0 0 0 0.0076 0 0 0 0 0 0 0 0 0.0041 0 0 0 0 0.05 0 0.0024 0 0 0.0024 0 0 0 0.003 0 0 0 0 0 0 0 0 0 0.012 0.019 0 0 0 0.003 0 0.0061 0 0.0032 0 0 0 0 0 0 0 0 0 0.038 0 0 0... | https://arxiv.org/abs/2505.21693v1 |
from inherent linguistic differ- ences in lexical specificity when describing cultural concepts, where Chinese, e.g., might employ more fine-grained expressions. Or alternatively, these differences might reflect limitations in the models’ multilingual capabilities. 6.2 Diversity The diversity metric varies widely depen... | https://arxiv.org/abs/2505.21693v1 |
for all cul- tural contexts, not only that of the native language. A high consensus might thus be an indicator that the model has a representation of the cultures that is independent of the specific prompt language. As seen in Table 3, model size seems to affect consensus, with the group of three larger mod- els having... | https://arxiv.org/abs/2505.21693v1 |
role in evoking these cultural differences, even when no explicit cultural context is given. The sensitivity to cultural signals in English, the connection between coun- tries and their local languages, and the cultural con- sensus that link regional areas emphasize the need for multilingual and multicontextual evaluat... | https://arxiv.org/abs/2505.21693v1 |
and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM confer- ence on fairness, accountability, and transparency , pages 610–623. Uri Berger and Edoardo Ponti. 2025. Cross-lingual and cross-cultural variation in image descriptions. InProcee... | https://arxiv.org/abs/2505.21693v1 |
Linguistics. Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo, Al- ice Oh, and Hwaran Lee. 2024. KoBBQ: Korean bias benchmark for question answering. Transac- tions of the Association for Computational Linguis- tics, 12:507–524. Karen Sparck Jones and Julia R Galliers. 1995. Evaluat- ing natural language processing systems... | https://arxiv.org/abs/2505.21693v1 |
Isabelle Au- genstein. 2025. Survey of cultural awareness in lan- guage models: Text and beyond. Computational Linguistics , pages 1–96. Florian Schneider and Sunayana Sitaram. 2024. M5 – a diverse benchmark to assess the performance of large multimodal models across multilingual and multi- cultural vision-language tas... | https://arxiv.org/abs/2505.21693v1 |
mapo tofu contain coffee? probing LLMs for food-related cultural knowledge. InProceedings of the 2025 Conference of the Na- tions of the Americas Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 1: Long Papers) , pages 9840–9867, Albuquerque, New Mexico. Association for Co... | https://arxiv.org/abs/2505.21693v1 |
specific ingredi- ent, and ingredient category. To quantify overall granularity, we assign a score of 1 to entities with specific references (e.g., spaghetti, coriander) and a score of 0 to more general categories (e.g., pasta dishes, herbs). The final granularity score for a given set of entities is computed as the av... | https://arxiv.org/abs/2505.21693v1 |
languages. Recall is generally higher, indicating few missing entities, while precision is lower due to over-extraction. Wikidata-based disambiguation step is capable of filtering out such incorrect entities, thereby mitigat- ing their impact during downstream evaluation. C.2 Performance of Wikidata Search We computed ... | https://arxiv.org/abs/2505.21693v1 |
each model, computed by averaging results across all six topics. In this analysis, culture specificity is defined as the difference in entity representa- tions between culturally contextualized prompts (i.e., those mentioning a specific country or region) and neutral prompts. A higher value indicates that the model ada... | https://arxiv.org/abs/2505.21693v1 |
Italy Japan Mexico Nigeria South Korea Spain T aiwan Thailand Turkey United Arab Emirates United Kingdom United States ayachatgptdeepseek llama3 llama3_70b mistralqwen0.330.490.640.800.95JA (Average of 6 T opics) (Avg)Argentina Australia Canada China Germany India Iran Italy Japan Mexico Nigeria South Korea Spain T aiw... | https://arxiv.org/abs/2505.21693v1 |
(Box) Native: China Thai Turkish Chinese (Simplified) 0 20 40 60 80 100 120 140 DiversityBeverage Book Music Clothing Food TransportationT opicGroup Non-native (Box) Native: T aiwan Chinese (Traditional) Figure 10: Box plots for diversity comparison between native and non-native languages for CHATGPT across 13 language... | https://arxiv.org/abs/2505.21693v1 |
Book Music Clothing Food TransportationT opic Group Non-native (Box) Native: Japan 0 20 40 60 80 100 120 140 DiversityBeverage Book Music Clothing Food TransportationT opicGroup Non-native (Box) Native: South Korea Italian Japanese Korean 0 20 40 60 80 100 120 140 DiversityBeverage Book Music Clothing Food Transportati... | https://arxiv.org/abs/2505.21693v1 |
100 120 140 DiversityBeverage Book Music Clothing Food TransportationT opicGroup Non-native (Box) Native: Argentina Native: Mexico Native: Spain 0 20 40 60 80 100 120 140 DiversityBeverage Book Music Clothing Food TransportationT opicGroup Non-native (Box) Native: Iran 0 20 40 60 80 100 120 140 DiversityBeverage Book M... | https://arxiv.org/abs/2505.21693v1 |
comparison between native and non-native languages for MISTRAL across 13 languages. 0 20 40 60 80 100 120 140 DiversityBeverage Book Music Clothing Food TransportationT opicGroup Non-native (Box) Native: Australia Native: Canada Native: United Kingdom Native: United States 0 20 40 60 80 100 120 140 DiversityBeverage Bo... | https://arxiv.org/abs/2505.21693v1 |
0 0 0 0 0 0.025 0.032 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0028 0 0 0 0 0.06 0 0 0 0 0 0 0 0 0.0033 0 0 0 0 0.0033 0 0 0 0 0.02 0.023 0 0 0 0 0 0.0033 0 0.003 0 0 0 0 0.0026 0 0 0 0 0.031 0.007 0.003 0 0 0.0076 0.003 0 0.003Neutral Culture Specificity 0.000.010.020.030.040.050.060.070.08 Culture Specificity (culture-contexualiz... | https://arxiv.org/abs/2505.21693v1 |
0 0 0.0081 0 0 0 0 0 0 0 0 0.0081 0 0.0027 0 0 0 0.0027 0.0027 0.0027 0 0.016 0 0 0 0 0 0 0 0 0.017 0 0 0 0 0.0031 0 0.036 0.0031 0.0031 0.025 0.042 0 0 0 0.0031 0 0 0 0.0032 0 0 0 0 0 0.0032 0.0032 0.078 0.0032 0.0096 0 0 0 0 0 0 0 0 0.0083 0 0 0 0 0.024 0 0 0 0.049 0.033 0.0067 0 0 0 0 0 0 0 0 0 0 0 0 0.0035 0 0 0 0 ... | https://arxiv.org/abs/2505.21693v1 |
0.057 0 0 0 0.033 0 0 0 0.021 0 0.17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.006 0 0.0089 0.048 0 0 0 0 0 0 0.018 0 0 0 0 0.019 0 0 0 0.012 0 0 0 0.036 0.012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0088 0 0 0 0.005 0 0.005 0 0.005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.016 0.0079 0 0 0 0 0 0 0 0 0 0... | https://arxiv.org/abs/2505.21693v1 |
DeepSeek with a neutral prompt (left) and a prompt explicitly mentioning the country (right, showing delta to neutral). ChinaT aiwanJapan South KoreaThailandIndia UAE Iran Turkey GermanyItalySpainUK CanadaAustraliaUS Mexico ArgentinaNigeria Prompt Country/Regionzh zh-tw ja ko th hi ar fa tr de it es enPrompt Language0.... | https://arxiv.org/abs/2505.21693v1 |
0.013 0.083 0.0027 0.19 0.07 0.028 0.073 0.037 0.026 0.089 0.092 0.074 0.056 0.059 0.0046 0.015 0.065 0.055 0.012 0.017 0.065 0.036 0.18 0.092 0.03 0.1 0.02 0.026 0.044 0.094 0.052 0.1 0.019 0 0 0.027 0.084 0.027 0.01 0.19 0.072 0.23 0.21 0.043 0.15 0.069 0.06 0.16 0.12 0.083 0.19 0.08 0.042 0.065 0.055 0.11 0.055 0.1 ... | https://arxiv.org/abs/2505.21693v1 |
0.0043 0 0.011 0.015 0.0017 0.023 0.022 0.013 0.052 0.0019 0 0 0.025 0.014 0.0088 0.0013 0.017 0.0021 0.091 0.018 0.0067 0.013 0.011 0.022 0.029 0.013 0.019 0.029 0.0015 0 0.0021 0.024 0.014 0.011 0 0.06 0.0073 0.15 0.058 0.011 0.021 0.0042 0.023 0.12 0.057 0.067 0.068 0.0056 0.029 0.0024 0.045 0.043 0.021 0.0041 0.061... | https://arxiv.org/abs/2505.21693v1 |
0.036 0.0018 5.8e-05 0 0 -0.0059 0.0019 0 0.0026 0.013 0.0027 0.021 0.0048 0.007 0.0012 0.012 0 0 0.0015 0.0093 0.011 0.0032 0 0.0011 -0.00033 0.0037 0 0 0.012 0.014 0.054 -0.0042 0 0.049 0 0.0049 0 -0.0045 0.00032 0 0 0 0 -0.0016 0.0042 0 0 0.029 0.0045 0.031 0.0088 0.0012 0.0069 0.016 0 0.01 0.00096 0.0035 0.028 -2.5... | https://arxiv.org/abs/2505.21693v1 |
UAE Iran Turkey GermanyItalySpainUK CanadaAustraliaUS Mexico ArgentinaNigeria Prompt Country/Regionzh zh-tw ja ko th hi ar fa tr de it es enPrompt Language0.078 0.006 0.052 0.004 0 0.00052 0 0 0.0039 0.01 0.0044 0.013 0.0021 0 0.0064 0.024 0.013 0 0.0019 0.012 0.016 0.065 0 0 0.0079 0 0 0 0.019 0.024 0.024 0 0 0 0.0076... | https://arxiv.org/abs/2505.21693v1 |
0.017 0 0 0 0.013 0 0 0 0 0.0019 0 0 0 0.0019 0.052 0.0019 0 0 0 0.0019 0 0 0 0.007 0 0 0 0 0 0 0.007 0 0 0.014 0.058 0 0 0 0 0 0 0 0.012 0 0.0058 0 0 0 0 0 0 0 0.034 0 0 0 0 0.033 0 0 0Neutral Culture Specificity 0.000.020.040.060.080.100.12 Culture Specificity (culture-contexualized - neutral) ChinaT aiwanJapan South... | https://arxiv.org/abs/2505.21693v1 |
arXiv:2505.21701v1 [cs.CL] 27 May 2025Do We Know What LLMs Don’t Know? A Study of Consistency in Knowledge Probing Raoyuan Zhao, Abdullatif Köksal, Ali Modarressi, Michael A. Hedderich and Hinrich Schütze LMU Munich and Munich Center for Machine Learning (MCML) {rzhao,akoksal,amodaresi,hedderich}@cis.lmu.de Abstract Th... | https://arxiv.org/abs/2505.21701v1 |
can be unreliable and sensitive to method choice and prompt perturbation. tection tools for LLMs’ knowledge gaps (Wang et al., 2023c), based on various signals be- yond prompting (Feng et al., 2023) and self- consistency (Mündler et al., 2024; Feng et al., 2024b), such as token probabilities (Guo et al., 2017; Jiang et... | https://arxiv.org/abs/2505.21701v1 |
on identifying inter- nal signals that reflect a model’s certainty about the given answer, including token probabilities, response consistency, self-reported confidence scores (Kadavath et al., 2022). To make use of these signals, researchers have developed several strategies: calibration-based methods align model conf... | https://arxiv.org/abs/2505.21701v1 |
in prompts (Stureborg et al., 2024; Pezeshkpour and Hruschka, 2024; Errica et al., 2025; Salinas and Morstatter, 2024). This sensitivity undermines the reliability of language models in both evaluation and real-world deployment. Sclar et al. (2024) systematically explore this issue, demonstrating that LLM performance c... | https://arxiv.org/abs/2505.21701v1 |
.69 .96 .68 .97 .67 .62 .66 .58 .77 .96 ASKCALSpace .76 .77 .76 .87 .94 .52 .79 .42 .81 .93 .64 .69 .60 .79 .87 Options .61 .61 .61 .76 .73 .31 .72 .20 .74 .76 .62 .70 .55 .78 .13 Typo .76 .75 .76 .86 .93 .51 .79 .42 .81 .91 .62 .68 .58 .78 .85 One-shot .41 .41 .47 .63 .80 .33 .54 .27 .64 .69 .45 .48 .43 .63 .86 EMBEDD... | https://arxiv.org/abs/2505.21701v1 |
four metrics: Acceptance/Rejection Consistency Intersec- tion over Union (IoU acc/IoU rej)is defined as the ratio of the intersection (the number of common accepted/rejected questions) to the union (total dis- tinct accepted/rejected questions): IoU acc=|A1∩A2| |A1∪A2|,IoU rej=|R1∩R2| |R1∪R2| Higher values indicate gre... | https://arxiv.org/abs/2505.21701v1 |
its intra-method consistency is still poor: IoU cons is only 0.6. Impact of One-Shot Variant The impact of one- shot prompting is even greater than that of the three zero-shot variants, with IoU consranging from 0.04 to 0.97.The impact is particularly evident for MORE- INFO. In the MMLU dataset, the Mistral and Llama 8... | https://arxiv.org/abs/2505.21701v1 |
on the dataset and model. Methods Using Similar Signals Exhibit Higher Consistency EMBEDDING is less consis- tent with other methods (in Mistral+Hellaswag, DecCons with MOREINFO is 0.07). This may be because EMBEDDING utilizes deeper-level model outputs (signals) than other methods, specifically leveraging the model’s ... | https://arxiv.org/abs/2505.21701v1 |
1.00 0.78 0.77 0.30 0.53 0.80 0.78 1.00 0.77 0.31 0.50 0.81 0.77 0.77 1.00 0.31 0.51 0.28 0.30 0.31 0.31 1.00 0.49 0.49 0.53 0.50 0.51 0.49 1.00 LLaMA3-3B, MMLU - Decision Consistency Heatmap (Original) (d) T okProb AskCal Embedding NOTA MoreInfo SelfRefT okProb AskCal Embedding NOTA MoreInfo SelfRef1.00 0.69 0.78 0.82... | https://arxiv.org/abs/2505.21701v1 |
the IoU consin shuffling option variants is just 46% (see Table 1), indicating that many of the specific ques- tions being rejected differ. The inconsistency becomes more striking under the one-shot setting. Although one-shot prompting is often considered to stabilize LLM outputs (Chat- terjee et al., 2024), calibratio... | https://arxiv.org/abs/2505.21701v1 |
the intra-method consistency of the ASKCALvariants. As shown in Table 3, the IoU consscores increased across all variants, demon- strating that the threshold correction significantly mitigated the instability caused by poor threshold calibration. 6 Conclusion In this study, we explore the consistency of four types of k... | https://arxiv.org/abs/2505.21701v1 |
Bhatia, and Tanmoy Chakraborty. 2024.POSIX: A prompt sensitivity index for large language models. In Findings of the Association for Compu- tational Linguistics: EMNLP 2024 , pages 14550– 14565, Miami, Florida, USA. Association for Com- putational Linguistics. Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayi... | https://arxiv.org/abs/2505.21701v1 |
Lample, Lucile Saulnier, et al. 2023. Mistral 7b.arXiv preprint arXiv:2310.06825 . Haoming Jiang et al. 2021. How can we know when lan- guage models know? on the calibration of language models for question answering. Transactions of the Association for Computational Linguistics . Saurav Kadavath, Tom Conerly, Amanda As... | https://arxiv.org/abs/2505.21701v1 |
ity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. InThe Twelfth International Conference on Learning Representations . Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber. 2023. Getting MoRE out of mixture of language model reasoning experts.... | https://arxiv.org/abs/2505.21701v1 |
s, Meijie Li, Jörg Schlötterer, and Christin Seifert. 2023. Give me the facts! a survey on factual knowledge probing in pre-trained language models. In Findings of the Association for Computational Linguistics: EMNLP 2023 , pages 15588–15605, Singapore. Association for Computa- tional Linguistics. Rowan Zellers, Ari Ho... | https://arxiv.org/abs/2505.21701v1 |
IOU consvalues for cross-method consistency, and the rejection rates for each method with different variants can be seen in Table 11,12,13,14. Figures 6 and 8 present heatmaps of cross- method consistency using IoU consas the metric, comparing original and variant-introduced con- ditions. Figures 4 and Figure 5 display... | https://arxiv.org/abs/2505.21701v1 |
one answer from the above choices. Guess: {response} ASKCALThe question is: What is the chemical symbol for water? Choices: A: H2O, B: CO2, C: NaCl, D: O2 Choose one answer from the above choices. Guess: A Provide the probability that your guess is correct. Give ONLY the probability, no other words or explanation.For e... | https://arxiv.org/abs/2505.21701v1 |
0.56 0.49 0.50 0.45 0.54 0.56 1.00 Mistral, Hellaswag - Decision Consistency Heatmap (Typo) 1.0 0.8 0.6 0.4 0.2 T okProb AskCal Embedding NOTA MoreInfo SelfRefT okProb AskCal Embedding NOTA MoreInfo SelfRef1.00 0.53 0.56 0.45 0.43 0.48 0.53 1.00 0.61 0.26 0.27 0.52 0.56 0.61 1.00 0.28 0.26 0.44 0.45 0.26 0.28 1.00 0.89... | https://arxiv.org/abs/2505.21701v1 |
(Original) 1.0 0.8 0.6 0.4 0.2 T okProb AskCal Embedding NOTA MoreInfo SelfRefT okProb AskCal Embedding NOTA MoreInfo SelfRef1.00 0.61 0.42 0.72 0.60 0.52 0.61 1.00 0.58 0.53 0.45 0.53 0.42 0.58 1.00 0.25 0.28 0.51 0.72 0.53 0.25 1.00 0.74 0.51 0.60 0.45 0.28 0.74 1.00 0.49 0.52 0.53 0.51 0.51 0.49 1.00 Mistral, MMLU -... | https://arxiv.org/abs/2505.21701v1 |
0.84 0.53 0.69 0.76 0.88 1.00 0.87 0.54 0.69 0.78 0.84 0.87 1.00 0.55 0.51 0.56 0.53 0.54 0.55 1.00 LLaMA3, MMLU - Decision Consistency Heatmap (One-shot) 1.0 0.8 0.6 0.4 0.2Figure 5: Heatmap of cross-method consistency evaluation results for MMLU. The values represent the average consistency across three different ran... | https://arxiv.org/abs/2505.21701v1 |
okProb AskCal Embedding NOTA MoreInfo SelfRef1.00 0.13 0.15 0.16 0.21 0.14 0.13 1.00 0.29 0.23 0.21 0.21 0.15 0.29 1.00 0.25 0.25 0.22 0.16 0.23 0.25 1.00 0.34 0.24 0.21 0.21 0.25 0.34 1.00 0.26 0.14 0.21 0.22 0.24 0.26 1.00 LLaMA3, Hellaswag - Harmonic IOU Heatmap (Shuffled_option) 0.20.30.40.50.60.70.80.91.0 T okProb... | https://arxiv.org/abs/2505.21701v1 |
0.20 0.20 0.15 0.17 1.00 0.17 0.19 0.22 0.31 0.15 0.17 1.00 LLaMA3, MMLU - Harmonic IOU Heatmap (Blank_space) 0.20.40.60.81.0 T okProb AskCal Embedding NOTA MoreInfo SelfRefT okProb AskCal Embedding NOTA MoreInfo SelfRef1.00 0.24 0.23 0.08 0.24 0.20 0.24 1.00 0.24 0.17 0.23 0.23 0.23 0.24 1.00 0.13 0.20 0.33 0.08 0.17 ... | https://arxiv.org/abs/2505.21701v1 |
1.00 0.32 0.48 0.84 0.75 0.85 0.32 1.00 0.48 0.49 0.49 0.49 0.48 0.48 1.00Shuffled_option - Decision Consistency Heatmap (Setup 0) 0.40.50.60.70.80.91.0 calibration embedding nota moreinfo token reflect Methodscalibration embedding nota moreinfo token reflectMethods1.00 0.74 0.88 0.92 0.91 0.56 0.74 1.00 0.69 0.72 0.75... | https://arxiv.org/abs/2505.21701v1 |
1.00 .48 1.00 .24 Typo .77 .83 .73 .93 .79 .70 .61 .81 .86 .89 .65 .99 .49 .99 .88 One-shot .08 .72 .04 .72 .44 .04 .26 .02 .28 .80 .43 .99 .28 .99 .76 SELFREFSpace .49 .41 .59 .68 .89 .56 .54 .58 .71 .95 .47 .48 .46 .55 .63 Options .44 .37 .56 .65 .32 .48 .46 .49 .64 .80 .46 .49 .44 .63 .20 Typo .49 .42 .59 .68 .86 .5... | https://arxiv.org/abs/2505.21701v1 |
0.528 ± 0.000 Typo 0.668 ± 0.000 0.668 ± 0.000 0.668 ± 0.000 0.943 ± 0.000 0.907 ± 0.000 One-shot 0.494 ± 0.000 0.508 ± 0.000 0.482 ± 0.001 0.843 ± 0.000 0.773 ± 0.002 LLaMa-3.1-8B TOKPROBSpace 0.643 ± 0.001 0.937 ± 0.000 0.491 ± 0.001 0.952 ± 0.000 0.936 ± 0.000 Options 0.593 ± 0.001 0.930 ± 0.000 0.435 ± 0.001 0.827 ... | https://arxiv.org/abs/2505.21701v1 |
0.000 0.979 ± 0.000 0.970 ± 0.000 One-shot 0.904 ± 0.000 0.896 ± 0.000 0.913 ± 0.000 0.676 ± 0.005 0.488 ± 0.015 ASKCALSpace 0.243 ± 0.069 0.168 ± 0.038 0.865 ± 0.009 0.889 ± 0.006 0.889 ± 0.006 Options 0.049 ± 0.000 0.026 ± 0.000 0.793 ± 0.000 0.389 ± 0.025 0.389 ± 0.025 Typo 0.134 ± 0.011 0.076 ± 0.004 0.870 ± 0.008 ... | https://arxiv.org/abs/2505.21701v1 |
0.743 ± 0.001 0.939 ± 0.000 0.920 ± 0.000 One-shot 0.220 ± 0.005 0.144 ± 0.004 0.604 ± 0.008 0.559 ± 0.021 0.347 ± 0.028 NOTASpace 0.433 ± 0.001 0.658 ± 0.000 0.323 ± 0.001 0.962 ± 0.000 0.944 ± 0.000 Options 0.413 ± 0.000 0.638 ± 0.000 0.306 ± 0.000 0.601 ± 0.001 0.416 ± 0.000 Typo 0.428 ± 0.000 0.655 ± 0.000 0.318 ± ... | https://arxiv.org/abs/2505.21701v1 |
0.800 ± 0.000 0.055 ± 0.000 0.625 ± 0.008 0.313 ± 0.033 MoreInfoSpace 0.851 ± 0.000 0.858 ± 0.000 0.843 ± 0.000 0.862 ± 0.000 0.749 ± 0.000 Options 0.692 ± 0.027 0.704 ± 0.025 0.681 ± 0.029 0.519 ± 0.001 0.512 ± 0.019 Typo 0.853 ± 0.000 0.860 ± 0.000 0.845 ± 0.000 0.857 ± 0.000 0.749 ± 0.000 One-shot 0.151 ± 0.022 0.51... | https://arxiv.org/abs/2505.21701v1 |
0.000 0.022 ± 0.000 0.278 ± 0.001 0.608 ± 0.048 0.422 ± 0.070 Table 9: Intra-method consistency evaluation using six knowledge probing methods in LLaMa-3.2-1B and 3 B with Hellaswag. Results represent the mean and standard deviation across six comparisons derived from three different variants generated with three diffe... | https://arxiv.org/abs/2505.21701v1 |
± 0.001 0.840 ± 0.000 AskCalSpace 0.497 ± 0.001 0.563 ± 0.007 0.449 ± 0.000 0.680 ± 0.002 0.765 ± 0.001 Options 0.459 ± 0.001 0.623 ± 0.000 0.366 ± 0.002 0.691 ± 0.000 0.655 ± 0.000 Typo 0.520 ± 0.003 0.681 ± 0.000 0.424 ± 0.004 0.742 ± 0.000 0.752 ± 0.000 One-shot 0.439 ± 0.000 0.487 ± 0.001 0.400 ± 0.000 0.618 ± 0.00... | https://arxiv.org/abs/2505.21701v1 |
EmbeddingOriginal 0.308 -0.005 0.620 0.624 0.986 0.987 0.764 Blank Space 0.333 -0.004 0.614 0.617 0.987 0.988 0.760 Blank Space 1 0.462 -0.001 0.632 0.634 0.989 0.987 0.773 Blank Space 2 0.333 -0.012 0.623 0.634 0.962 0.964 0.764 Shuffled Option 0.366 -0.124 0.528 0.668 0.549 0.536 0.603 Shuffled Option 1 0.388 -0.061 ... | https://arxiv.org/abs/2505.21701v1 |
TokenProbOriginal 0.453 -0.043 0.579 0.686 0.596 0.541 0.638 Blank Space 0.458 -0.037 0.589 0.694 0.615 0.555 0.652 Blank Space 1 0.444 -0.049 0.584 0.693 0.616 0.563 0.652 Blank Space 2 0.465 -0.030 0.610 0.721 0.638 0.566 0.677 Shuffled Option 0.440 -0.051 0.606 0.729 0.638 0.575 0.680 Shuffled Option 1 0.437 -0.057 ... | https://arxiv.org/abs/2505.21701v1 |
0.361 0.793 0.199 0.188 0.318 One-shot 2 0.246 -0.406 0.341 0.720 0.193 0.200 0.304 One-shot 3 0.305 -0.316 0.379 0.695 0.190 0.190 0.298 One-shot 4 0.354 -0.242 0.413 0.700 0.182 0.170 0.288 MoreInfoOriginal 0.475 -0.035 0.523 0.637 0.337 0.295 0.441 Blank Space 0.471 -0.042 0.528 0.670 0.339 0.288 0.450 Blank Space 1... | https://arxiv.org/abs/2505.21701v1 |
0.502 0.638 Typo 0.618 0.112 0.638 0.656 0.656 0.526 0.656 Typo 1 0.629 0.126 0.650 0.670 0.655 0.512 0.662 Typo 2 0.606 0.105 0.633 0.659 0.633 0.507 0.645 One-shot 1 0.588 0.056 0.589 0.590 0.752 0.680 0.661 One-shot 2 0.657 0.067 0.574 0.551 0.856 0.787 0.671 One-shot 3 0.543 0.071 0.545 0.556 0.200 0.171 0.295 One-... | https://arxiv.org/abs/2505.21701v1 |
0.475 0.480 Typo 0.482 -0.018 0.495 0.508 0.499 0.504 0.503 Typo 1 0.482 -0.018 0.500 0.518 0.512 0.512 0.515 Typo 2 0.461 -0.040 0.487 0.514 0.480 0.492 0.497 One-shot 1 0.544 0.050 0.524 0.498 0.458 0.436 0.477 One-shot 2 0.537 0.033 0.502 0.474 0.554 0.549 0.511 One-shot 3 0.556 0.060 0.513 0.463 0.472 0.462 0.468 O... | https://arxiv.org/abs/2505.21701v1 |
0.674 0.606 0.126 0.071 0.209 Shuffled Option 0.656 0.291 0.644 0.477 0.088 0.065 0.148 Shuffled Option 1 0.642 0.263 0.640 0.613 0.122 0.075 0.204 Shuffled Option 2 0.675 0.329 0.665 0.508 0.092 0.061 0.156 Typo 0.655 0.286 0.646 0.539 0.114 0.076 0.188 Typo 1 0.651 0.277 0.648 0.614 0.137 0.083 0.225 Typo 2 0.647 0.2... | https://arxiv.org/abs/2505.21701v1 |
Abstract Attention: This paper includes instances of hateful content for research purposes. This study evaluates the effectiveness of ChatGPT, an advanced AI model for natural language process - ing, in identifying targeting and inappropriate language in online comments. With the increasing challenge of moderating vast... | https://arxiv.org/abs/2505.21710v1 |
comments compared to crowd -sourced and expert annotations. This involves analyzing its scope of detection, accuracy, and consistency in identifying problematic content. (2) To improve model accuracy: By iteratively refining the model’s prompts and configurations, we seek to enhance ChatGPT’s alignment with human judgm... | https://arxiv.org/abs/2505.21710v1 |
for human moderators to effectively manage on their own. The immense scale of content being uploaded daily necessitates automated solutions to handle the massive influx efficiently. Automated systems, despite their imperfections, offer a scal - able approach to content moderation by rapidly processing and flagging pote... | https://arxiv.org/abs/2505.21710v1 |
phase on a vast corpus of text data, which enabled the model to learn language patterns and structures, followed by a supervised fine -tuning phase that refined its abilities on specific tasks like text completion and question -answering using labeled data sets. This extensive training process, which began with a proto... | https://arxiv.org/abs/2505.21710v1 |
it may not adequately address model bias or performance issues when the AI encounters out -of-distribution data. The study also points out that global explana - tions, while intended to assist users, can inadvertently lead to biased rule creation due to their priming effect —participants might adopt high -frequency but... | https://arxiv.org/abs/2505.21710v1 |
tasks over time. This process mirrors human learning, where past experiences inform future decisions, and contrasts with static models that do not update after initial training. In summary, the literature underscores the complex and evolving nature of content moder - ation in the digital age. From the limitations of hu... | https://arxiv.org/abs/2505.21710v1 |
comments were annotated for inappropriateness and targeting language, considering prior context. Annotators reviewed the comments and associated the context, including the title and previous comments within that conversation. For targeting language, titles were annotated as well as comments. For inappropriateness, the ... | https://arxiv.org/abs/2505.21710v1 |
of perspectives that reflect the variability of real -world con - tent moderation environments. The percentage agreement and Cohen’s Kappa scores between the crowd and expert annotations on the gold data were 92% for inappropriate language and 58% for targeting language. These scores indicate that crowd annotations are... | https://arxiv.org/abs/2505.21710v1 |
’contextual cues’. ChatGPT was then asked to determine if the comment exhibited inappropriateness and/or targeting. The evaluation was conducted under the following scenarios: LATEX Supplement 9 (1) Scenario 1:** ChatGPT received targeting labels from the context, including previous com - ments and the targeting label ... | https://arxiv.org/abs/2505.21710v1 |
data set with crowd annotations, ensuring that the refinements were both accurate and broadly applicable. The crowd annotations, applied to a larger data set compared to the expert annotations, allowed for extensive validation of ChatGPT’s performance. As mentioned earlier, the crowd annotations are sufficiently simila... | https://arxiv.org/abs/2505.21710v1 |
markers of targeting behavior in conversation threads. The token types were annotated by the authors of this paper. Table 4 illustrates the tokens with the highest frequency, all variants of their POS tags in the data, and token types. The analysis reveals a significant presence of offensive tokens (22) and slurs (16),... | https://arxiv.org/abs/2505.21710v1 |
Attributed Targeting Becomes Specific to an Individual represents a progression from attribute -based targeting as the starting point to more personalized attacks. Initially, targeting may be based on an attribute shared with a group (e.g., "go back to your f**king estro weed subs my dude. your rotten brain and sh*t co... | https://arxiv.org/abs/2505.21710v1 |
ChatGPT Inappropriateness before targeting 2.6% 49% Inappropriateness at the same time as targeting 36% 23% Targeting before inappropriateness 43.6% 13% Table 9. : Analysis of the Detection Time of Inappropriateness in Relation to Targeting in the Conversation Threads Table 9 presents the timing of inappropriateness de... | https://arxiv.org/abs/2505.21710v1 |
PROPAGANDA" Go back to /r/politics anon_srpgg: hey s**k my d**k you f**king q***r anon_GQt1h: F**k off fa**ot anon_hMcof: Please never change anon_GQt1h: >being this much of a retard Imagine actually believing everything that is anti-EU is Russian propaganda. Please never change being a retard. anon_srpgg: shut up b***... | https://arxiv.org/abs/2505.21710v1 |
in Cohen’s Kappa is primarily due to better alignment in cases where the content was "not targeting". These improve - ments highlight the efficacy of simplifying the prompt and switching to a more advanced model, contributing to a clearer and more accurate identification of targeting language and bridging the gap betwe... | https://arxiv.org/abs/2505.21710v1 |
the cases where ChatGPT wrongly classified "not targeting" comments as "targeting" decreased from 27 in Version 3 to 16 in Version 4. These figures indicate that while Version 4 showed a slight improvement in accurately identifying "not targeting" comments, it struggled more with identifying "targeting" comments, leadi... | https://arxiv.org/abs/2505.21710v1 |
the temperature, reinstated higher classification accuracy and consistency. Version 6 further fine -tuned the model, achieving a slight increase in Cohen’s Kappa score and refining the balance between false posi - tives and true positives. Overall, these results underscore the importance of careful prompt design and mo... | https://arxiv.org/abs/2505.21710v1 |
more appropriate interactions as discussions progress. The discrepen - cies in the patterns between the gold set and the broader data set arise from differences in sample size and variability as well as the changes in prompt design. The gold set, with its smaller, curated sample, may reflect more controlled patterns of... | https://arxiv.org/abs/2505.21710v1 |
contexts. Lastly, ethical considerations and the potential for biased behavior in AI models remain concerns that need to be addressed to ensure fair and responsible use of automated content moderation tools. LATEX Supplement 25 Figure 10 : ChatGPT vs. Crowd: Distribution of Targeting and Inappropriate Language (Low Com... | https://arxiv.org/abs/2505.21710v1 |
the handling of responses for clarity and consistency by using the "gpt-4" chat-based model, ensuring that the output is clear and less ambiguous. The implementation in Version 1 further streamlines the process by using the ChatCompletion endpoint. (2) Version 2:** This version refines the approach further by introduci... | https://arxiv.org/abs/2505.21710v1 |
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