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IRCoT (Trivedi et al., 2023), and Adaptive- Note (Wang et al., 2024) over 10 independent runs with different random seeds. Our approach con- sistently achieves statistically significant improve- ments across all evaluated benchmarks (p-value < 0.05 for all comparisons). Specifically, our model outperforms these baselin... | https://arxiv.org/abs/2505.21250v1 |
on EM and F1, and also surpasses SearChain by an average of 8.8 on cEM, despite both baselines using GPT-3.5 while our model uses Llama-8B. E Prompts E.1 Answer Generation Prompt Answer Generation Prompt <|start_header_id|>system<|end_header_id|> You will receive three inputs: 'documents ','a question ', and 'hints '. ... | https://arxiv.org/abs/2505.21250v1 |
question that can be answered using the given document(s). - Use information from one or more documents, but ensure that the answer is concise and directly supported by the content. Format: - Return a JSON object formatted as follows: {{ "question": "Your generated question based on the documents.", }} - Make sure the ... | https://arxiv.org/abs/2505.21250v1 |
arXiv:2505.21297v1 [cs.CL] 27 May 2025rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Yifei Liu†Li Lyna Zhang†⋄Yi Zhu† Bingcheng Dong†§Xudong Zhou†‡Ning Shang†Fan Yang†Mao Yang† †Microsoft Research Asia§Dalian University of Technology‡Shanghai Jiao Tong University Abstract Advancing ... | https://arxiv.org/abs/2505.21297v1 |
code reasoning. While frontier LLMs may offer a scalable way for test case synthesis, two substantial challenges arise: generating semantically valid, constraint-aware inputs varying in scale and difficulty; and assigning correct outputs without ground truth solutions, particularly since synthetic problems inherently l... | https://arxiv.org/abs/2505.21297v1 |
total, we collect 418K verified problems, including 37.7K expert-designed and 380K synthetic problems. SourceOriginal Seed Questions Synthesized Questions Total # Original #Verified # Synthesized #Verified Verified AIZU 4302 2151 100712 17386 19537 AtCoder 2824 2080 88103 29096 31176 CodeChef 5232 3765 164646 46749 505... | https://arxiv.org/abs/2505.21297v1 |
mutual-verification mechanism that reliably labels both solutions and test cases. 3 Methodology 3.1 Collection of Competitive Code Problems Collection of expert-designed competitive coding problems . We curate a seed dataset from pub- licly available resources, including programming competition websites and open datase... | https://arxiv.org/abs/2505.21297v1 |
solution; (2) identify the reasoning and core knowledge being tested from the solution; (3) synthesize a new problem that test similar skills. Full prompt details are in Appendix Fig. 8. In totoal, we synthesize 1,565K new code problems as shown in Table 1. Compared to the original seeds, the synthesized problems still... | https://arxiv.org/abs/2505.21297v1 |
!= 2: return False n = int(lines[0]) if not (2 <= n <= 100000): return False a = list(map(int, lines[1].strip().split())) if len(a) != n: return False if any(x < 1 or x > 10**9 for x in a): return False return True except: return FalseFigure 3: An example of LLM-generated utility functions for test input generation and... | https://arxiv.org/abs/2505.21297v1 |
generation function. Step3: Executing utility functions to produce valid test inputs . Finally, for each instantiated scale-controlling parameters from Step 2, we invoke the GENERATE_TEST_INPUT function to generate a test input string. We then use the VALIDATE_TEST_INPUT function to verify whether each generated input ... | https://arxiv.org/abs/2505.21297v1 |
simple yet effective mutual verification mechanism that identifies both correct test outputs and the solutions that produce them. As illustrated in Fig. 4, for each problem, we first sample 16 long-reasoning candidate solutions using a frontier reasoning model (QWQ-32B [ 29]). We then sample a diverse set of at least 5... | https://arxiv.org/abs/2505.21297v1 |
dataset includes 418K problems with extensive test cases, totaling 580K question-solution pairs. 4 Experiments 4.1 Setup Training setup . Using our 580K dataset, we fine-tune Qwen2.5-Coder instruct models [ 16] at 1.5B, 7B, and 14B scales for 6 epochs using the AdamW optimizer, a batch size of 96, and a max sequence le... | https://arxiv.org/abs/2505.21297v1 |
Bespoke-Stratos-32B 48.9 95.5 89.8 93.8 77.5 DeepSeek-R1-Distill-Qwen-32B 57.2 95.9 89.9 92.8 78.7 OpenThinker-32B (OpenThoughts-114k) 54.1 94.8 89.2 94.1 78.4 OlympicCoder-32B (OpenR1-codeforces-100k) 57.4 90.0 85.2 86.7 71.3 OCR-Qwen-32B-Instruct (OCR-736k) 61.7 - - - - DeepSeek-R1-Distill-LLaMA-70B 57.5 96.5 90.7 91... | https://arxiv.org/abs/2505.21297v1 |
39.58 12.5 4.17 0 DeepSeek-R1-Distill-Qwen-32B 11.98 39.58 4.17 4.17 0 DeepSeek-R1-Distill-LLaMA-70B 13.54 43.75 8.33 2.08 0 rStar-Coder-14B 17.19 47.92 12.50 8.33 0 Table 4: Ablation on curated-only vs. synthetic-only subsets proves the value of each source. Model LiveCodeBench HumanEval HumanEval+ MBPP MBPP+ DeepSeek... | https://arxiv.org/abs/2505.21297v1 |
while GPT-4o generated inputs are concentrated in the easier range ( 100–102), with no input scales exceeding 103. This demonstrates the superiority of our method in generating more challenging and diverse test cases. 5 Conclusion In this work, we present rStar-Coder to construct a large-scale, high-quality dataset for... | https://arxiv.org/abs/2505.21297v1 |
Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938 , 2021. [11] Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, and Nan Duan. Enhancing large language models in coding through multi-perspective self-consistency. arXiv preprint arXiv:230... | https://arxiv.org/abs/2505.21297v1 |
Guilherme Penedo, Lewis Tunstall, Anton Lozhkov, Hynek Kydlicek, Edward Beeching, Loubna Ben Allal, Quentin Gallouedec, Leandro von Werra, Agustín Piqueres Lajarín, and Nathan Habib. Open r1: Update 3, 2025. [27] Quan Shi, Michael Tang, Karthik Narasimhan, and Shunyu Yao. Can language models solve olympiad programming?... | https://arxiv.org/abs/2505.21297v1 |
total size of rStar-Coder-580K. As shown in Table 6, both scaling solution count and problem diversity improve reasoning perfor- mance. However, scaling only the number of solutions yields diminishing returns and becomes less efficient. For example, our 580K dataset, with broader problem coverage, achieves significantl... | https://arxiv.org/abs/2505.21297v1 |
scale as input and produces a formatted input string, which is subsequently checked for validity by validate_test_input . 14 Figure 6: Standard input-ouput based problem example Standard Input/Output Problem Example You have two positive integers aandb. You can perform two kinds of operations: •a=⌊a/b⌋ •b=b+ 1 Find the... | https://arxiv.org/abs/2505.21297v1 |
8: New code problem synthesis prompt. New Code Problem Synthesis Prompt in rStar-Coder I will provide you with a programming problem along with its solution. Your task is to create a new, transformed programming problem based on the original one. You need to complete the following steps: 1. Analyze and understand the o... | https://arxiv.org/abs/2505.21297v1 |
_ i n p u t ( < param1 > , <param2 > , . . . ) : # Check i f p a r a m e t e r s meet c o n s t r a i n t s i f not ( < c o n d i t i o n 1 >) or not ( < c o n d i t i o n 2 > ) : return None # Generate i n p u t u s i n g CYaRon i n p u t _ d a t a = [ . . . ] return " \ n " . j o i n ( map (s t r, i n p u t _ d a t a... | https://arxiv.org/abs/2505.21297v1 |
arXiv:2505.21315v2 [cs.CL] 28 May 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead Jesujoba O. Alabi1Michael A. Hedderich2David Ifeoluwa Adelani3Dietrich Klakow1 1Saarland University, Saarland Informatics Campus 2LMU Munich and Munich Center for Machine Learning 3Mila - Quebec AI I... | https://arxiv.org/abs/2505.21315v2 |
and distinct differences in the development of language tech- nologies for Africa. This survey has three primary goals: 1.Comprehensive overview : By systematically 1 1454 articles (All data were collected using keyword matching via the Semantic Scholar API.) AI Conferences (AAAI, IJCAI, ..) : 33 ARXIV : 643 HCI Confer... | https://arxiv.org/abs/2505.21315v2 |
within specific national contexts. Beyond country-specific studies, language-specific surveys have been conducted for individual African languages such as southern Sotho (Sibeko and Setaka, 2022) and Yoruba (Jimoh et al., 2025). However, a survey looking at the broader African continent is necessary to uncover both uni... | https://arxiv.org/abs/2505.21315v2 |
paper proposed a model and, if so, whether the model was released. After the annotation process. We excluded papers on African American Vernacular English (AA VE) and those on spoken English that is not African-accent. Figure 2 shows an overview of the data filtering process. Resulting Publication Set In summary, we an... | https://arxiv.org/abs/2505.21315v2 |
African languages such as French, English, and Portuguese. While some papers clearly specified the country-specific ac- cents they addressed (Aryal et al., 2023; Afonja et al., 2021; Muhammad et al., 2023; Hagemeijer et al., 2022a), others were more generic (Shan et al., 2023) in their approach. Furthermore, African la... | https://arxiv.org/abs/2505.21315v2 |
4 Task Datasets Datasets NLU Taxi-1500, SIB-200, MasakhaNEWS, AfriSenti, AfriXNLI MT FLORES-200, AfroLingu-MT, NTREX-128, TICO-19, MAFAND-MT Reasoning AfriMGSM, LINGOLY , MGSM IE MasakhaNER, MasakhaNER 2.0 Speech FLEURS, BibleTTS, African V oices LM mC4, ROOTS, WURA, GlotCC, MADLAD Models MT NLLB-200, Toucan, M2M-100, ... | https://arxiv.org/abs/2505.21315v2 |
21 African languages, creating an imbalance in evaluation. Language models : During the survey pe- riod, there was a notable increase in the develop- ment of large transformer-based language mod- els. Many studies introduced such models for African languages, including monolingual mod- els for individual languages (Nze... | https://arxiv.org/abs/2505.21315v2 |
language models. Recent work explores various types of reasoning—linguistic (Bean et al., 2024), cultural knowledge (Myung et al., 2024a), com- monsense (Ponti et al., 2020), mathematical (Ade- lani et al., 2025), ethical (Agarwal et al., 2024), and moral (Khandelwal et al., 2024). Notably, many of the datasets used fo... | https://arxiv.org/abs/2505.21315v2 |
pretrained models that support cross-lingual transfer (Pfeiffer et al., 2021). However, a major challenge arises when the target African language is not well rep- resented in the pretrained model or its tokenizer. Common strategies to address this include adap- tive pretraining (Pfeiffer et al., 2021; Alabi et al., 202... | https://arxiv.org/abs/2505.21315v2 |
al., 2023a; Magomere et al., 2024) were the most commonly addressed. However, much remains to be done to address bias and ensure equity across diverse linguistic and cultural contexts. Human-Centered NLP Human-centric research in NLP has gained prominence in both the NLP community (Soni et al., 2024) and the field of h... | https://arxiv.org/abs/2505.21315v2 |
al., 2025) and the difficulty of finding volunteers. Aside from popular tasks like ASR, other tasks such as text-to-speech, audio classification, speech translation, and many others lack datasets. To de- velop NLP models that would be used by many African communities, it must be accompained with a speech component. Mor... | https://arxiv.org/abs/2505.21315v2 |
of papers on computer vision (though not on NLP). However, a few relevant NLP papers for African languages may have been omitted as well. Acknowledgments Jesujoba Alabi was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 1102. We are grateful to Aravind Krish... | https://arxiv.org/abs/2505.21315v2 |
Jesujoba Oluwadara Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Emezue, Colin Leong, Michael Beukman, Shamsuddeen H. Muham- mad, Guyo D. Jarso, Oreen Yousuf, and 26 others. 2022a.... | https://arxiv.org/abs/2505.21315v2 |
Birbo. 2024. Age: Amharic, ge’ez and english parallel dataset. Proceedings of the Seventh Workshop on Technolo- gies for Machine Translation of Low-Resource Lan- guages (LoResMT 2024) . Tosin P. Adewumi, Mofetoluwa Adeyemi, Aremu An- uoluwapo, Bukola Peters, Happy Buzaaba, Oy- erinde Samuel, Amina Mardiyyah Rufai, Ben-... | https://arxiv.org/abs/2505.21315v2 |
Ahia, Sachin Kumar, Hila Gonen, Jungo Kasai, David Mortensen, Noah Smith, and Yulia Tsvetkov. 2023. Do all languages cost the same? tokenization in the era of commercial language mod- els. In Proceedings of the 2023 Conference on Em- pirical Methods in Natural Language Processing , pages 9904–9923, Singapore. Associati... | https://arxiv.org/abs/2505.21315v2 |
Alumäe, Kunnar Kukk, Viet Bac Le, Claude Bar- ras, Abdelkhalek Messaoudi, and Waad Ben Kheder. 2023. Exploring the impact of pretrained models and web-scraped data for the 2022 nist language recogni- tion evaluation. In Interspeech . Cynthia Jayne Amol, Everlyn Asiko Chimoto, Rose Delilah Gesicho, Antony M. Gitau, Naom... | https://arxiv.org/abs/2505.21315v2 |
Lambebo Tonja, Tadesse Destaw Belay, Mitiku Yohannes Fuge, Aman Kassahun Wassie, Eyasu Shiferaw Jada, Yonas Chanie, Walelign Tewabe Sewunetie, and Seid Muhie Yimam. 2024. Walia-LLM: Enhancing Amharic-LLaMA by integrating task-specific and generative datasets. In Findings of the Association for Computational Linguistics... | https://arxiv.org/abs/2505.21315v2 |
Ifeoluwa Adelani, Xiaoyu Shen, and Vera Demberg. 2020. Unsupervised pidgin text generation by pivoting english data and self-training. ArXiv , abs/2003.08272. Tyler A. Chang, Catherine Arnett, Zhuowen Tu, and Benjamin Bergen. 2024. Goldfish: Monolin- gual language models for 350 languages. ArXiv , abs/2408.10441. Jiaao... | https://arxiv.org/abs/2505.21315v2 |
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for Computational Linguistics. Moussa Doumbouya, Lisa Einstein, and Chris Piech. 2021. Using radio archives for low-resource speech recognition: Towards an intelligent virtual assistant for illiterate users. In Proceedings of the AAAI Con- ference on Artificial Intelligence , volume 35. Abhimanyu Dubey, Abhinav Jauhri,... | https://arxiv.org/abs/2505.21315v2 |
Workshop on Scaling Up Multilingual Evaluation , pages 21–24, Online. Association for Computational Linguistics. Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Ari- vazhagan, and Wei Wang. 2022. Language-agnostic BERT sentence embedding. In Proceedings of the 60th Annual Meeting of the Association for Compu- tational... | https://arxiv.org/abs/2505.21315v2 |
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and those ul- timately analyzed, not all venues had retrievable papers, and not all contributed to the final analysis (for example, CSCW, UIST). The ACL Summit cat- egory had the fewest papers retrieved, while ArXiv, as expected, had the most. Due to the size, and presence of several irrelevant papers from ArXiv and HC... | https://arxiv.org/abs/2505.21315v2 |
'or'Not AI-related 'only. Title: "{title}" Abstract: "{abstract}" """ C Other tasks researchers work on? Semantics Work on semantics include semantic textual relatedness (Ousidhoum et al., 2024) which was run as a shared task, lexical semantic rela- tions (Gromann et al., 2024), and word analogy tasks (Mersha and Wu, 2... | https://arxiv.org/abs/2505.21315v2 |
grammatical error correction (Adouane et al., 2019; Gezmu et al., 2021a; Keita et al., 2024)—are among the least explored for African languages. Within the text generation category, existing work also includes data-to-text (Gehrmann et al., 2023), question gen- eration (Asai et al., 2024), title or topic genera- tion (... | https://arxiv.org/abs/2505.21315v2 |
et al., 2020). Addition- ally, rule-based approaches are evaluated for gram- matical error correction. These studies showcase the diverse roles rule-based methods continue to play across linguistic resource creation and tasks. EComprehensive list of African language resources available Several language resources have b... | https://arxiv.org/abs/2505.21315v2 |
directly, multiple studies have shown that such models tend to underperform on African languages compared to high-resource languages such as Ger- man, French, and English (Adelani et al., 2024, 2025). To address this disparity, transfer learn- ing has emerged as an effective strategy: pre- trained multilingual models a... | https://arxiv.org/abs/2505.21315v2 |
MRC 53 AfriMMLU (Adelani et al., 2025) QA 16 AfriQA (Ogundepo et al., 2023) QA 10 NaijaRC (Aremu et al., 2024) MRC 3 TyDi QA (Clark et al., 2020) QA 1 (swh) KenSwQuAD (Wanjawa et al., 2023) QA 1 (swh) Amh-QuAD (Taffa et al., 2024) QA 1 (amh) Reasoning AfriMGSM (Adelani et al., 2025) Math 17 LINGOLY (Bean et al., 2024) ... | https://arxiv.org/abs/2505.21315v2 |
datasets including African languages published between 2019 and 2024.Name Task Number Translation Models Toucan (Elmadany et al., 2024) MT 517 NLLB-200 (NLLB Team et al., 2024) MT 53 MADLAD-MT (Kudugunta et al., 2023) MT 19 M2M-100 (Fan et al., 2020) MT 17 SMaLL-100 (Mohammadshahi et al., 2022a) MT 17 Masked Language M... | https://arxiv.org/abs/2505.21315v2 |
arXiv:2505.21324v1 [cs.CL] 27 May 2025Leveraging Large Language Models and Traditional Machine Learning Ensembles for ADHD Detection from Narrative Transcripts Yuxin Zhu∗ Yuting Guo∗ yuxin.zhu@emory.edu yuting.guo@emory.edu Emory University Atlanta, Georgia, USANoah Marchuck noah.marchuck@emory.edu Emory University Atl... | https://arxiv.org/abs/2505.21324v1 |
clinical profiles, and comorbidities [ 20]. No single risk fac- tor or biomarker conclusively accounts for ADHD’s onset; instead, multiple genetic, environmental, and neurodevelopmental factors interplay, leading to varied symptom manifestations in different individuals. Such heterogeneity poses a fundamental challenge... | https://arxiv.org/abs/2505.21324v1 |
like RoBERTa [ 16] have reached∼76% accuracy in classifying ADHD-related posts from on- line forums [ 15], and SVM classifiers using linguistic and memory- related features attained F 1≈0.77in distinguishing ADHD in personal essays [ 6]. These advances demonstrate that textual mark- ers of ADHD, such as patterns of lan... | https://arxiv.org/abs/2505.21324v1 |
relying on in-context learning [ 21]. While generative LLMs like LLaMA3 have excellent capabilities in understanding nuances of complex, unstructured texts, in-context learning strategies have limitations on how many labeled examples can be provided to the LLM to guide its decision making. This is because the context w... | https://arxiv.org/abs/2505.21324v1 |
by the clinician after administering the KSADS and considering other data and interactions provided as part of par- ticipation into the HBN study. Each of the 441 youths (ages 5–21) in our dataset watched an emotionally evocative short animated film (“The Present”) during functional MRI scanning; immediately upon exiti... | https://arxiv.org/abs/2505.21324v1 |
of ADHD detection, which is detailed in the following section. The comprehensive experiment pipeline is shown in Figure 1. 2.2 Classification Models We split the dataset into a training, development, and test set with a ratio of 60/20/20. All classification models were trained using the same training and development se... | https://arxiv.org/abs/2505.21324v1 |
contribution to our overall framework. 2.2.2 Transformer-Based Model: RoBERTa. RoBERTa is a widely used transformer-based model pre-trained on large English cor- pora. The model was selected for its proven effectiveness in tasks involving contextual language understanding, and its superior per- formance reported in cla... | https://arxiv.org/abs/2505.21324v1 |
corpus [11]. Recognizing potential feature limitations, we introduced sup- plementary engineered features calculated directly from narrative transcripts. We conducted experiments under two configurations: (1)TF–IDF only: using the 1,000 highest-weighted n-gram fea- tures derived from TF–IDF. (2)TF–IDF + engineered feat... | https://arxiv.org/abs/2505.21324v1 |
or other workplace, or in other situations that require remaining in place).c. Often runs about or climbs in situations where it is inappropriate. (Note: In adolescents or adults, may be limited to feeling restless.)d. Often unable to play or engage in leisure activities quietly.e.Is often “on the go,” acting as if “dr... | https://arxiv.org/abs/2505.21324v1 |
quirks, yet require consensus to declare a positive classification, thus filtering out idiosyncratic errors from any single model. For each participant transcript 𝑖, let ˆ𝑦LLM 𝑖,ˆ𝑦RoBERTa 𝑖,ˆ𝑦SVM 𝑖∈ {0,1} denote the binary predictions (1 = ADHD, 0 = non-ADHD) pro- duced by the LLaMA3 prompt, the RoBERTa classifi... | https://arxiv.org/abs/2505.21324v1 |
relatively narrow interval (0.60–0.80), highlighting its stability across runs.Figure 4 presents the confusion matrices for the four classifica- tion models. LLaMA3 demonstrates a high recall for ADHD cases, correctly identifying 11 out of 44 ADHD instances, but misclassifies a large number of true ADHD cases as non-AD... | https://arxiv.org/abs/2505.21324v1 |
demonstrates promising re- sults for ADHD classification using narrative transcripts, several limitations should be acknowledged. First, the dataset used in this study is relatively small and may not capture the full variability present in broader clinical or community populations. This limits the generalizability of o... | https://arxiv.org/abs/2505.21324v1 |
are few-shot learners. Advances in Neural Information Processing Systems 2020-Decem, NeurIPS (2020). arXiv:2005.14165 [6]Florian Raphaël Cafiero, Juan Barrios Rudloff, and Simon Gabay. 2024. Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories. In Proceedings of th... | https://arxiv.org/abs/2505.21324v1 |
Rebecca M Gardner, Tina Hernandez-Boussard, and Yair Bannett. 2024. Measuring quality-of-care in treatment of young children with attention-deficit/hyperactivity disorder using pre-trained language models.Journal of the American Medical Informatics Association 31, 4 (01 2024), 949– 957. doi:10.1093/jamia/ocae001 arXiv:... | https://arxiv.org/abs/2505.21324v1 |
arXiv:2505.21342v2 [cs.CL] 28 May 2025PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claims Valentin Knappich1,2, Annemarie Friedrich2, Anna Hätty1and Simon Razniewski3 1Bosch Center for AI 2University of Augsburg 3ScaDS.AI & TU Dresden Abstract Patent claims define the scope of protection ... | https://arxiv.org/abs/2505.21342v2 |
instance, it 6th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech) 2025 *Corresponding author. †These authors contributed equally. /envel⌢pe-⌢penvalentin.knappich@de.bosch.com (V. Knappich); annemarie.friedrich@uni-a.de (A. Friedrich); anna.haetty@de.bosch.com (A. Hätty); simon.razniewski@tu-dres... | https://arxiv.org/abs/2505.21342v2 |
for the prediction of patent edits following rejections based on novelty and non-obviousness. Patent Clarity and Definiteness. Hido et al. [19]predict patentability using linguistic features including syntactic complexity and word age. Kong et al. [20] model patent readability using § 112(a) (lack of disclosure) with l... | https://arxiv.org/abs/2505.21342v2 |
the listed items are exhaustive or merely examples, leading to indefiniteness. Functional Claiming 2173.05(g)Claim recites ’means for’ or ’step for’ without disclosing adequate corresponding structure, material, or acts in the specification, as required under 35 U.S.C. 112(f) or pre-AIA equivalent. Contradicting Limita... | https://arxiv.org/abs/2505.21342v2 |
not detect all rejections. To also balance the number of applications in each class, we first compute the average number of in- definite claims per application included in the dataset. We iterate through the definite applications in random order and sample the same number of claims until there are as many definite as i... | https://arxiv.org/abs/2505.21342v2 |
assigned “undefined term” . In both cases, the term “antecedent basis” was not mentioned explicitly, unlike in most reasoning texts of this category. 3.7. Dataset Statistics Table 2 shows dataset statistics over the splits. In total, the dataset contains 14.5k claims from 3.7k patent applications, half of which are ind... | https://arxiv.org/abs/2505.21342v2 |
by inferring the date. independent. The full list is visualized in Figure 3. For all features sets, we train separate classifiers for the binary classification and the multi-label classification. 4.2. LLM Agent LLMs have shown impressive performance on many tasks related to patents [ 4], yet they have not been evaluate... | https://arxiv.org/abs/2505.21342v2 |
same format as the structured represen- tations extracted from the office actions, with the additional confidence scores. This allows quantitative evaluation of the binary classification, multi-label classification, and the correctness of textual reasons. 5. Evaluation Metrics In this section, we propose a set of metri... | https://arxiv.org/abs/2505.21342v2 |
point to the same essential issue in the claim on a scale from 1 (worst) to 5 (best) (we normalize the final scores to the range [0,100]). The few-shot prompt is attached in Appendix C. To allow the model to analyze the ground truth and the predicted reason before settling on a grade, we first prompt it to find similar... | https://arxiv.org/abs/2505.21342v2 |
features and the variant with all features perform comparably, while the variant using only TF-IDF features performs substan- tially worse. This indicates that the handcrafted features are indeed helpful to differentiate definite and indefinite claims, Model % indefBinary Multi-Label P R F1 AUC AccMacro F1Micro F1 Rand... | https://arxiv.org/abs/2505.21342v2 |
75% to be correct. 0.00 0.25 0.50 0.75 1.00 Reason Confidence Score0255075% Reasons /JudgeQwen 2.5 32B ( 𝜌= 0.432) 0.00 0.25 0.50 0.75 1.00Qwen 2.5 72B ( 𝜌= 0.514) Figure 5: Distributions of reason-level confidence scores and the relation of confidence and correctness according to LLM-as-Judge. The reported judge sco... | https://arxiv.org/abs/2505.21342v2 |
arelative term that lacks a clear, objective standard for determining the exact input used to determine the first domain.100% 2 The claims recite the limitation ’ranking the one or more groups’ There is insufficient antecedent basis for this limitation in the claim, it appears likely that this claim should instead depe... | https://arxiv.org/abs/2505.21342v2 |
and 𝜌= 0.514for 72B). That is, the reason-level confidence appears to be better calibrated than the claim-level confidence. Future work should further investigate the relation between these two sub-tasks and develop methods to bridge this disconnect.7. Discussion In this section, we provide qualitative insight into th... | https://arxiv.org/abs/2505.21342v2 |
W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, I. Stoica, Judging llm-as-a-judge with mt-bench and chatbot arena, in: Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, 2023. [9]J. Risch, N. Alder, C. Hew... | https://arxiv.org/abs/2505.21342v2 |
11207. arXiv:2310.11207 . [24] A. Madsen, S. Chandar, S. Reddy, Are self-explanations from Large Language Models faithful?, in: L.-W. Ku, A. Martins, V. Srikumar (Eds.), Findings of the Associa- tion for Computational Linguistics: ACL 2024, Associ- ation for Computational Linguistics, 2024, pp. 295–337. doi:10.18653/v1... | https://arxiv.org/abs/2505.21342v2 |
enough context is given to make the rejection undestandable on its own. Leave this field empty if the reasonText does not refer to another reason. 26 27 3.3 "claims": The mentioned claims. You can either list individual claims as integers or inclusive claim ranges (e.g., 1-3 equals 1,2,3) as strings (ranges are preferr... | https://arxiv.org/abs/2505.21342v2 |
given claim with respect to indefiniteness. indefiniteness_categories andclaim are replaced with their respective values. C. LLM-as-Judge Prompt 1<instruction> 2You will evaluate the performance of an AI system used to identify indefiniteness issues in patent claims. You will be given 2 short text snippets that mention... | https://arxiv.org/abs/2505.21342v2 |
arXiv:2505.21378v1 [cs.CL] 27 May 2025Analyzing values about gendered language reform in LLMs’ revisions Jules Watson1Xi Wang1Raymond Liu2 Suzanne Stevenson1Barend Beekhuizen3 1University of Toronto, Department of Computer Science 2University of British Columbia, Department of Computer Science 3University of Toronto, D... | https://arxiv.org/abs/2505.21378v1 |
women and men referents, reflecting uneven application of reform lan- guage, depending on referent gender.yes LLMs will emphasize inclusive language more for genders that language reforms were designed to include (women and nonbinary people), and will emphasize sounding profes- sional more for women.yes H3: explicitnes... | https://arxiv.org/abs/2505.21378v1 |
with work elucidating lan- guage ideologies encoded in LLMs (Hofmann et al., 2024; Jackson et al., 2024; Watson et al., 2025). Role nouns have been the target of language reforms for over 50years (Cameron, 2012). These reforms have sought to modify people’s use of role nouns in ways that both reflect and influence chan... | https://arxiv.org/abs/2505.21378v1 |
Templates for prompt preambles. Role nouns: We consider 50sets of role nouns adapted from Watson et al. (2025), which drew on various sources (Vanmassenhove et al., 2021; Papineau et al., 2022; Bartl and Leavy, 2024; Lucy et al., 2024). Each role noun set consists of three variants (i.e., 50×3 = 150 unique terms): a ge... | https://arxiv.org/abs/2505.21378v1 |
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