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lan- guage models are zero-shot reasoners. Advances in neural information processing systems , 35:22199– 22213. Dongyuan Li, Ying Zhang, Zhen Wang, Shiyin Tan, Satoshi Kosugi, and Manabu Okumura. 2024. Active learning for abstractive text summarization via llm- determined curriculum and certainty gain maximiza- tion. I... | https://arxiv.org/abs/2505.17746v1 |
2025. Kimi k1. 5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599 . Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 . Ben... | https://arxiv.org/abs/2505.17746v1 |
arXiv:2505.17747v1 [cs.CL] 23 May 2025Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks Maureen de Seyssel*1Jie Chi*1,2Skyler Seto1Maartje ter Hoeve1 Masha Fedzechkina1Natalie Schluter1 1Apple2Technical University of Denmark {mdeseyssel,jchi2}@apple.com Abstract We introduce a set of tr... | https://arxiv.org/abs/2505.17747v1 |
given a triplet (A,B,X), isXcloser to AorB? By designing minimal pairs that differ only in language or in meaning, we isolate and quantify how well models distinguishes these dimensions. Because they are contrastive, zero-shot, and training-free, these met- rics can be applied across languages, checkpoints, 1In fact, i... | https://arxiv.org/abs/2505.17747v1 |
may not reflect the geometry of the representation space itself. These two evaluation paradigms have remained largely separate. To our knowledge, no existing method allows for simultaneous, controlled evalu- ation of both dimensions without relying on task- specific training. As a result, we lack a unified evaluation f... | https://arxiv.org/abs/2505.17747v1 |
To directly assess the intrinsic structure of multilingual representations without relying on the pitfalls of extrinsic evalua- tion, we adapt the ABX discrimination paradigm, originally developed for evaluating speech embed- dings, to the text domain. In the original ABX framework (Schatz et al., 2013, 2014; Schatz, 2... | https://arxiv.org/abs/2505.17747v1 |
is considered successful if the model leads to the dis- tance between AandXto be smaller than that between BandX. Meaning Discrimination In the MD task, we test whether the model captures differences in meaning while holding language constant. The goal is to evaluate whether semantic content is encoded in the represent... | https://arxiv.org/abs/2505.17747v1 |
35 languages. These global metrics offer a higher-level view of how well a language is discriminated or semantically aligned within the multilingual space. Validation of ABX Metrics To validate our met- rics, we perform two control analyses. First, we dates, MD ABX uses contrastive triplets that isolate semantic differ... | https://arxiv.org/abs/2505.17747v1 |
language pairs which are more separable by form tend to exhibit lower meaning preservation, even in the fully trained model. Layer-level patterns. To better understand how these abilities are distributed within the model, Fig- ure 3 plots discrimination scores across layers for the final checkpoint. LD is strongest in ... | https://arxiv.org/abs/2505.17747v1 |
transfer. 5.1 Experimental Setup Following Blevins et al. (2022), we evaluate both monolingual probing and cross-lingual transfer to test how our ABX discrimination metrics relate to linguistic generalization. We use part-of-speech tagging (POS), named entity recognition (NER), and natural language inference (NLI) as r... | https://arxiv.org/abs/2505.17747v1 |
probably due to gradient clipping, that affects both probing and discrimination metrics (see Figure 2). 7We also ensure that these effects are not driven by training data size. Language-wise probing accuracy shows no signifi- cant correlation with pretraining data quantities (taken from Conneau et al. (2020a)).Setting ... | https://arxiv.org/abs/2505.17747v1 |
monolingual probing, where high LD may reflect a failure to encode shared syntactic patterns. By contrast, MD does not significantly predict downstream accuracy in any task. While one might expect MD to relate to semantically oriented tasks like NLI, success there may depend on higher-level reasoning unaccounted for by... | https://arxiv.org/abs/2505.17747v1 |
resentations, which are well suited to probing and contrastive analysis. While this makes them a natu- ral starting point for validating our ABX discrim- ination framework, it remains an open question whether similar dynamics hold for decoder-only or encoder–decoder models, which are trained using autoregressive or seq... | https://arxiv.org/abs/2505.17747v1 |
Science Society , volume 38. Rochelle Choenni and Ekaterina Shutova. 2022. Inves- tigating language relationships in multilingual sen- tence encoders through the lens of linguistic typology. Computational Linguistics , 48(3):635–672. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek,... | https://arxiv.org/abs/2505.17747v1 |
Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , pages 2733–2743. Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020. Xtreme: a massively multilingual multi-task bench- mark for evaluating cross-lingual generalization. I... | https://arxiv.org/abs/2505.17747v1 |
multilingual llms think in english? arXiv preprint arXiv:2502.15603 . Amitay Sicherman and Yossi Adi. 2023. Analysing dis- crete self supervised speech representation for spo- ken language modeling. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 1–5. IEEE. ... | https://arxiv.org/abs/2505.17747v1 |
families, scripts, and typological characteristics. Code Language ABX POS NER NLI mono. CL mono. CL mono. CL ar Arabic ✓ ✓ ✓ ✓ ✓ ✓ ✓ bg Bulgarian ✓ ✓ ✓ ✓ ✓ ✓ ca Catalan ✓ ✓ ✓ cs Czech ✓ ✓ ✓ ✓ ✓ da Danish ✓ ✓ ✓ de German ✓ ✓ ✓ ✓ ✓ ✓ ✓ el Greek ✓ ✓ ✓ ✓ ✓ ✓ en English ✓ ✓ ✓ ✓ ✓ ✓ ✓ es Spanish ✓ ✓ ✓ ✓ ✓ ✓ ✓ et Estonian ✓ ✓... | https://arxiv.org/abs/2505.17747v1 |
AandXshare the same meaning ( M1) but are expressed in different languages, while A andBshare the same language ( L2) but express different meanings. When d(X, A)< d(X, B), the model successfully discriminates based on se- mantic similarity across languages despite surface form differences. Here is an example for the M... | https://arxiv.org/abs/2505.17747v1 |
in- dividual language level, we present heatmaps of language and meaning discrimination scores across checkpoints (Figure 8). Scores are normalised per language to highlight relative changes over time. For language discrimination (left), we observe a sharp decline during early training steps for most languages, followe... | https://arxiv.org/abs/2505.17747v1 |
15 illustrates the relationship between lan- guage discrimination scores and cross-lingual trans- fer accuracy for all source-target language pairs in our experiments. For both POS tagging and NER tasks, we observe a strong negative correlation: language pairs with higher discrimination scores (indicating more distinct... | https://arxiv.org/abs/2505.17747v1 |
probing task. Lighter regions mean higher accuracy scores. Figure 11: Relationship between ABX-based Language Discrimination scores and downstream probing POS accuracy, averaged across checkpoints. Each point rep- resents a single evaluation language. The x-axis shows how well the model distinguishes that language from... | https://arxiv.org/abs/2505.17747v1 |
arXiv:2505.17762v1 [cs.CL] 23 May 2025Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs Ziyu Ge1∗,Yuhao Wu1∗,Daniel Wai Kit Chin1,Roy Ka-Wei Lee1and Rui Cao2 1Singapore University of Technology and Design 2University of Cambridge {ziyu ge, roy lee}@sutd.edu.sg, {yuhao wu, da... | https://arxiv.org/abs/2505.17762v1 |
source credibility—capabilities that are currently underexplored in fact-checking research. 2https://www.bbc.co.uk/sport/football/54691842 3https://en.mehrnews.com/news/165168/ Pogba-quits-intl-football-after-comments-from-Macron-report 4https://mediabiasfactcheck.com/mehr-news-agency/ Research Objectives. Addressing t... | https://arxiv.org/abs/2505.17762v1 |
credibility estimation is crucial, as not all media sources are reliable; however, this problem remains underex- plored. Early works addressed this issue by estimating media credibility through analysis of fake news records associated with sources [Mukherjee and Weikum, 2015; Popat et al. , 2016; Popat et al. , 2017 ].... | https://arxiv.org/abs/2505.17762v1 |
was then submitted as a query on Google, from which we retrieved the top 10 web pages7. To ensure reproducibility, the retrieved web pages were archived using the Wayback Machine8. 3.3 Conflict Evidence Annotation Next, we annotated the stances of the collected evidence doc- uments using a two-stage process designed to... | https://arxiv.org/abs/2505.17762v1 |
labeled as true/supported correspond to questions with Yesas answers, and those la- beled as false/refuted correspond to questions with Noas an- swers. The statistics of CONFACT are provided in Table 1 and an illustration of a data sample from CONFACT is pro- vided in Appendix B. An analysis of document credibility rev... | https://arxiv.org/abs/2505.17762v1 |
and used as input for answer generation. We evaluate multiple prompting strategies for leveraging these augmented contexts: •Direct Answer (DirA. ): The Kselected paragraphs are provided to the LLM along with the claim verificationquestion, and the model directly generates an answer. •Majority Vote (MajV . ): The model... | https://arxiv.org/abs/2505.17762v1 |
of filter- ing in the document level, credibility scores influence ranking (Figure 2(c)). The final ranking score for a paragraph Pmis computed as: sm=srel,m+β∗scred,m, (2) where srel,mis the relevance score and βbalances relevance and credibility. We considered both a soft (CW soft) and a hard (CW hard) setting for le... | https://arxiv.org/abs/2505.17762v1 |
that prompting LLMs to explicitly reason about retrieved content helps mitigate the influence of un- reliable sources. However, despite these improvements, the overall accuracy and F1 scores remain suboptimal, highlight- ing the need for more effective mechanisms to incorporate source credibility into the fact-checking... | https://arxiv.org/abs/2505.17762v1 |
ens 76.76 67.75 66.78 63.25 67.27 63.81 74.91 63.39 64.11 58.01 66.55 60.07 Table 2: Performance of retrieval-augmented LLMs on the ModC andHumC splits of our CONFACT dataset. Baseline (Bsl.) denotes models without incorporating source backgrounds. GT-MB (GT) represents models that only consider incorporating source ba... | https://arxiv.org/abs/2505.17762v1 |
AI-driven fact- checking by demonstrating both the potential and limita- tions of leveraging source credibility to enhance retrieval- augmented generation for misinformation detection. 5.2 Ablation Studies To further understand the impact of media source back- grounds on fact-checking with conflicting evidence, we con-... | https://arxiv.org/abs/2505.17762v1 |
hybrid sources (including AI-generated backgrounds) tend to introduce noise and mislead evaluators. This suggests that un- reliable or AI-generated context can impair judgment rather than enhance it. These results have critical implications for real-world fact- checking organizations. Fact-checkers must adopt rigorous ... | https://arxiv.org/abs/2505.17762v1 |
Alexandrov, James R. Glass, and Preslav Nakov. Pre- dicting factuality of reporting and bias of news media sources. In Proceedings of the 2018 Conference on Em- pirical Methods in Natural Language Processing , pages 3528–3539, 2018. [Baly et al. , 2020 ]Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, ... | https://arxiv.org/abs/2505.17762v1 |
model serving with paged attention. In Proc. of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023. [Leeet al. , 2024 ]Yoonsang Lee, Xi Ye, and Eunsol Choi. Ambigdocs: Reasoning across documents on different en- tities under the same name. CoRR , abs/2404.12447, 2024. [Lewis et al. , 2020 ]Patrick S. ... | https://arxiv.org/abs/2505.17762v1 |
and beyond. Found. Trends Inf. Retr. , 3(4):333–389, 2009. [Schlichtkrull et al. , 2023 ]Michael Schlichtkrull, Zhijiang Guo, and Andreas Vlachos. Averitec: A dataset for real- world claim verification with evidence from the web. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Infor... | https://arxiv.org/abs/2505.17762v1 |
the URL content to determine its support, rejection, or neutrality toward the claim. Consider the claim date. Re- spond only with: - Support - Reject - Not enough evidence. No additional text. Claim: {claim} Date of Claim: {claim_date} URL: {evidence_url} C. Text Prompt without Justification Review the text to determin... | https://arxiv.org/abs/2505.17762v1 |
Limitations We identify three main limitations of our study. Bias in Source Credibility Annotations. Our credibility an- notations rely on the Media Bias/Fact Check (MBFC) dataset, which may carry inherent biases. As a result, any systematic bias present in MBFC is inherited by our framework and may influence model beh... | https://arxiv.org/abs/2505.17762v1 |
assess- ments. Mitigating such biases requires integrating structured fact-checking methodologies that encourage LLMs to assess the credibility of competing claims rather than defaulting to frequency-based heuristics. Overall, these error categories reveal fundamental chal- lenges in fact-checking with conflicting evid... | https://arxiv.org/abs/2505.17762v1 |
credibility prediction framework achieved 70.04% in a zero- shot manner. G.2 Source Background Incorporation F. System Prompt You are given a question and several pieces of evidence. Your task is to analyze the evidence and provide a concise answer to the question. For each piece of evidence, the background of its sour... | https://arxiv.org/abs/2505.17762v1 |
besides the answer; in SBA exp the model is explicitly instructed to ignore augmented texts from incredible sources. Detailed prompts are shown in Box G and H. For the SBA enssetting, a two stage prompting mechanism is exploited. As illustrated in Fig 4, we first employ a LLM to categorize each piece of evidence as eit... | https://arxiv.org/abs/2505.17762v1 |
is needed. 6.Conclude: • Evaluate the reliability of the media background and determine whether the sentence supports the question. • Ensure that your conclusion is based solely on the information provided. 7.Answer: • Optionally, provide a justification based on the above steps, explaining your reasoning. Keep your ju... | https://arxiv.org/abs/2505.17762v1 |
journals and a range of books that cover most disciplines. I.2 Credibility Score Prediction We utilize the MBFC dataset to train a model for predicting media credibility scores. The dataset consists of media de- scriptions paired with credibility scores. The BigBird-RoBERTa model serves as the backbone of our architect... | https://arxiv.org/abs/2505.17762v1 |
arXiv:2505.17767v1 [cs.CL] 23 May 2025The Real Barrier to LLM Agent Usability is Agentic ROI Weiwen Liu1, Jiarui Qin2, Xu Huang3, Xingshan Zeng2, Yunjia Xi1, Jianghao Lin1,Chuhan Wu∗,Yasheng Wang2∗,Lifeng Shang2,Ruiming Tang2, Defu Lian3,Yong Yu1,Weinan Zhang1∗ 1Shanghai Jiao Tong University, 2Huawei Noah’s Ark Lab, 3U... | https://arxiv.org/abs/2505.17767v1 |
based on the approximate monthly active users (MAU) of conventional applica- tions in each domain. Agentic ROI is a conceptual representation of relative trends. The listed agent products are illustrative and may not be exhaustive. In particular, the current generation of LLM agents focuses on specialized, professional... | https://arxiv.org/abs/2505.17767v1 |
negative, despite meeting the minimum threshold for information quality. In some cases, the agent’s response time can exceed the time required for users to complete the task manually. As a result, the Agentic ROI in these domains remains low, limiting 2 /uni00000030/uni00000052/uni00000047/uni00000048/uni0000004f/uni00... | https://arxiv.org/abs/2505.17767v1 |
to their larger predecessors but at significantly lower cost ( e.g.o3-mini vs. o1, o4-mini vs. o3). In the context of LLM agents, what we see is a similar optimization process in which each generation makes tradeoffs along different axes of the ROI surface: information gain, agent time, and total cost— first scaling up... | https://arxiv.org/abs/2505.17767v1 |
user preferences, and external information sources. This allows agents to execute multi-step workflows, track long-term goals, and manage ambiguity more robustly. 3.2 Post-training Scaling Post-training scaling refers to the enhancement of a base language model’s capabilities through techniques such as supervised fine-... | https://arxiv.org/abs/2505.17767v1 |
preferences, improving its performance in real time based on personalized feedback. •Scaling towards Agentic ROI under budget constraints. By formalizing the concept of Agentic ROI, we envision that next-generation LLM agents will be capable of directly optimizing their Agentic ROI while operating under real-world budg... | https://arxiv.org/abs/2505.17767v1 |
✓ ✗ Text, Image ✓ - WorkArena [18] Web Browsing ✓ ✗ Text, Image ✗ 10.0 InfoDeepSeek [99] Web Search ✓ ✗ Text ✓ 5.0 •Multi-step dependent tasks. Many real-world goals cannot be achieved in a single step or response. The world model should support multi-turn interactions and long-horizon tasks, allowing agents to revise ... | https://arxiv.org/abs/2505.17767v1 |
empirical understanding of deceptive behaviors is required. Such insights are crucial for designing robust reward functions that align more faithfully with human values and intent. Moreover, developing scalable transparency mechanisms, behavioral auditing methods, and interpretability-aware training could form the basi... | https://arxiv.org/abs/2505.17767v1 |
is thus not to minimize size only, but to minimize size conditional on performance. Shrinking agent size reduces latency, power consumption, and deployment friction, thereby directly lowering agent time. This requires smarter initialization, transfer, and specialization strategies to retain performance at lower computa... | https://arxiv.org/abs/2505.17767v1 |
the financial cost incurred in using an agent, introducing a practical budget constraint: even perfect answers are of limited value if unaffordable, especially at scale or under continuous load. Crucially, the expense is influenced by the model inference time, memory usage, task complexity, reasoning depth, and tool in... | https://arxiv.org/abs/2505.17767v1 |
(rather than based on an existing LLM and predefined workflow) to perform search tasks autonomously. Its success has inspired a wave of research focused on improving search agents via RL [ 85,36,10,35,121,118]. Beyond search, many recent works apply similar end-to-end agent optimization in other domains. For example, A... | https://arxiv.org/abs/2505.17767v1 |
functions in the wild, 2016. https://openai.com/index/ faulty-reward-functions/ . [3]Open AI. Deep research system card, 2025. https://cdn.openai.com/ deep-research-system-card.pdf , Accessed on 2025-5-17. [4]Open AI. Swarm (experimental, educational), 2025. https://github.com/openai/swarm , Accessed on 2025-5-17. [5]R... | https://arxiv.org/abs/2505.17767v1 |
Machine Learning Research , pages 11642–11662. PMLR, 21–27 Jul 2024. [19] Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi, et al. Agent ai: Surveying the horizons of multimodal interaction. arXiv preprint arXiv:2401.03568 , 2024... | https://arxiv.org/abs/2505.17767v1 |
and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516 , 2025. [37] Jin K Kim, Michael Chua, Mandy Rickard, and Armando Lorenzo. Chatgpt and large language model (llm) chatbots: The current state of acceptability and a proposal for guidelines on utilization in academic medicine. Journa... | https://arxiv.org/abs/2505.17767v1 |
2024. [54] Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. Agentbench: Evaluating LLMs as agents. In T... | https://arxiv.org/abs/2505.17767v1 |
on computer vision and pattern recognition , pages 8494–8502, 2018. [70] Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning aligned language models compromises safety, even when users do not intend to! arXiv preprint arXiv:2310.03693 , 2023. [71] Zehan Qi, Xiao Li... | https://arxiv.org/abs/2505.17767v1 |
Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning. arXiv preprint arXiv:2503.05592 , 2025. [86] Claudio Spiess, Mandana Vaziri, Louis Mandel, and Martin Hirzel. Autopdl: Automatic prompt optimization for llm agents. arXiv preprint arXiv:2504.04365 , 2025. [87] Daniel Toyam... | https://arxiv.org/abs/2505.17767v1 |
Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, and Yong Yu. Towards open-world recommendation with knowledge augmentation from large language models. In Proceedings of the 18th ACM Conference on Recommender Systems , pages 12–22, 2024. [101] Zhiheng Xi, Wenxiang ... | https://arxiv.org/abs/2505.17767v1 |
of the 2025 CHI Conference on Human Factors in Computing Systems , pages 1–20, 2025. [114] Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, et al. Aflow: Automating agentic workflow generation, 2024. URL https://arxiv. org/abs/2410.10762... | https://arxiv.org/abs/2505.17767v1 |
EXECUTE: A Multilingual Benchmark for LLM Token Understanding Lukas Edman1,3Helmut Schmid2Alexander Fraser1,3,4 1School of Computation, Information and Technology, TU Munich 2Center for Information and Language Processing, LMU Munich 3Munich Center for Machine Learning 4Munich Data Science Institute lukas.edman@tum.de,... | https://arxiv.org/abs/2505.17784v1 |
on the char- acter level, but either first train the model (Itzhak and Levy, 2022; Kaushal and Mahowald, 2022), or focus on other topics than orthography (Huang et al., 2023; Efrat et al., 2023). Research on error correction, including spelling correction, has been done for many languages. Maxutov et al. (2024) found s... | https://arxiv.org/abs/2505.17784v1 |
also be of higher quality. For non-English languages, we translate all the stories using Google Translate. At this point, for Chinese and Japanese, it is necessary to apply word segmentation. For Chinese, we use jieba3, and for Japanese we use nagisa .4 We then generate a character set and vocabulary from the translate... | https://arxiv.org/abs/2505.17784v1 |
and Japanese, we ask the model to split characters into Kangxi radicals, and vice versa.5 Similarly, we decompose Hangul characters to Jamo and vice versa. These tasks are analogous to thespelling andinverse_spelling tasks. We 5One can further split Kangxi radicals down to strokes, but this showed very poor performance... | https://arxiv.org/abs/2505.17784v1 |
understanding of Amharic might weaken this bias. Gemma 2 Llama 3.1 Llama 3.3 Qwen 2.5 9B 27B 8B 70B 70B 7B 32B Amh 80.5 85.3 75.7 95.9 96.4 41.9 74.4 Ara 51.6 62.3 52.1 68.1 67.8 47.2 68.6 Zho 70.2 74.4 71.3 81.1 79.7 70.4 83.6 Eng 64.8 71.6 61.9 75.7 75.2 62.1 77.3 Hin 47.9 47.1 43.8 54.0 56.4 43.5 86.2 Jpn 60.1 65.2 ... | https://arxiv.org/abs/2505.17784v1 |
7.6 22.8 2.5 8.2 Contains Rad 55.4 65.5 81.1 79.3 69.4 73.5 72.9 68.0 78.8 62.2 74.5 JpnChar to Rad 0.0 0.7 0.7 0.0 0.0 0.0 2.2 2.2 9.2 0.4 3.7 Rad to Char 2.6 8.5 2.2 0.4 1.9 13.7 13.3 5.2 20.3 1.5 7.4 Contains Rad 57.9 61.6 86.4 72.7 69.7 73.1 76.0 73.4 76.0 65.7 83.4 KorHangul to Jamo 7.5 48.1 54.6 65.3 24.5 48.8 45... | https://arxiv.org/abs/2505.17784v1 |
we cannot know for sure without test- ing them all. Several other scripts are not covered which may have differing performances. We also do not test very large language models above 70B parameters due to compute constraints. The CUTE benchmark added scores for the 405B parameter Llama 3.1 and found it made improve- men... | https://arxiv.org/abs/2505.17784v1 |
Heuermann, Leti- cia Lago, Lilly McNealus, Livio Baldini Soares,Logan Kilpatrick, Lucas Dixon, Luciano Martins, Machel Reid, Manvinder Singh, Mark Iverson, Mar- tin Görner, Mat Velloso, Mateo Wirth, Matt Davi- dow, Matt Miller, Matthew Rahtz, Matthew Watson, Meg Risdal, Mehran Kazemi, Michael Moynihan, Ming Zhang, Mins... | https://arxiv.org/abs/2505.17784v1 |
81– 91, Bangkok, Thailand and Online. Association for Computational Linguistics. Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunt- ing Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, and Srini- vasan Iyer. 2024. Byte latent transform... | https://arxiv.org/abs/2505.17784v1 |
results for each language pair. We also correlate the results from English to other Latin-scripted languages, German, Spanish, and Xhosa, in Table 7. Here we see the average correlation is at least 95% between English, Ger- man, and Spanish, and at least 85% to Xhosa. This suggests that the results for other Latin-scri... | https://arxiv.org/abs/2505.17784v1 |
32B 8B 24B AmharicSpell 25.6 72.4 99.5 91.2 98.6 98.4 96.3 0.9 14.3 97.0 99.8 Inv Spell 77.6 71.5 99.1 91.4 98.8 99.8 99.8 8.2 52.4 99.1 100.0 Cont Char 58.5 85.8 73.0 81.9 90.4 91.8 91.8 63.2 93.5 94.7 95.8 Cont Word 55.9 69.5 97.0 98.3 78.5 99.5 99.6 71.7 99.9 95.9 98.8 Ins Char 35.2 58.2 57.1 65.5 26.1 92.3 97.8 10.... | https://arxiv.org/abs/2505.17784v1 |
97.3 100.0 99.9 98.1 100.0 93.3 99.9 Ins Char 11.8 6.0 9.2 7.8 4.4 10.9 13.5 7.1 15.9 7.4 4.4 Ins Word 39.9 60.6 86.7 96.8 48.2 94.9 96.6 70.2 97.6 51.9 72.9 Del Char 35.0 56.3 58.5 80.4 56.1 68.3 67.5 56.8 70.5 33.6 72.4 Del Word 60.3 77.5 53.7 77.7 76.2 97.1 96.5 78.5 95.7 74.3 69.8 Sub Char 27.7 42.2 35.4 60.5 39.3 ... | https://arxiv.org/abs/2505.17784v1 |
39.6 69.7 20.2 41.7 Ins Word 36.4 72.0 93.3 98.6 50.8 91.8 94.1 66.8 92.5 45.0 87.7 Del Char 44.7 64.6 69.1 75.6 62.5 62.2 63.2 44.6 64.1 43.2 56.6 Del Word 56.5 81.8 77.5 88.6 91.9 94.3 86.3 76.7 91.0 75.4 90.7 Sub Char 39.1 62.0 71.4 77.4 56.0 65.5 63.9 50.4 67.6 37.9 53.0 Sub Word 48.9 78.7 90.1 96.7 73.0 91.2 92.1 ... | https://arxiv.org/abs/2505.17784v1 |
Compression Hacking: A Supplementary Perspective on Informatics Metric of Language Models from Geometric Distortion Jianxiang Zang1, Meiling Ning2, Yongda Wei3, Shihan Dou1, Jiazheng Zhang1, Nijia Mo4, Binhong Li5, Tao Gui1∗, Qi Zhang1, Xuanjing Huang1∗ 1Fudan University,2Beijing University of Posts and Telecommunicati... | https://arxiv.org/abs/2505.17793v1 |
LMs with high information compression tend to exhibit representation spaces that degener- ate into highly anisotropic, distorted states. Highly anisotropic representations indicate varying sensi- tivity to semantic changes across different dimen- sions, which can hinder language models’ ability to comprehend instructio... | https://arxiv.org/abs/2505.17793v1 |
|V|Z⊤Z+αID (1) Here, ΣZ∈RD×Ddenotes the covariance ma- trix, and a regularization term αIDis added to ensure it is full rank. The matrix ΣZis positive definite and can be decomposed using eigenvalue decomposition as ΣZ=QΛQ⊤. The eigenvalues from Λare{λd}D d=1, arranged in descending or- der by default, and {qd}D d=1are... | https://arxiv.org/abs/2505.17793v1 |
This sug- gests a potential synergistic relationship between compression and anisotropy. If we can quantify this relationship and confirm its statistical significance, it could provide valuable guidance for refining com- pression metrics. Current tools for qualitatively and quantita- tively analyzing the anisotropy of ... | https://arxiv.org/abs/2505.17793v1 |
covariance matrix and reveals the characteristics of ill-conditioning from an intrinsic structural perspective, making it the first anisotropy metric entirely based on internal structure. A(Z)def= cond (Σ Z) =maxD d=1λd minD d=1λd(7)2.3 Systematic Analysis Mechanistic Analysis As shown in Figure 2, by performing eigenv... | https://arxiv.org/abs/2505.17793v1 |
entropy (Roy and Vetterli, 2007) precisely models this characteristic, and it is formally equiv- alent to a compression metric weighted by eigen- values (Compression (SE)), as formulated in Eq. 8.CSE(Z)def=−tr(Σ Zlog Σ Z) =−DX d=1λdlogλd (8) Semantic Coefficient of Variation Just as compression-anisotropy synchronicity... | https://arxiv.org/abs/2505.17793v1 |
correlations between the four metrics, and both model size (size) and comprehensive capabilities (Ground truth). The bold-highlighted components represent our refined metrics. task-agnostic pipeline operating purely from a rep- resentational perspective. Our evaluation paradigm associates the sampled data batch Bwith a... | https://arxiv.org/abs/2505.17793v1 |
0.962 0.955 0.923 0.967 0.846 0.923 0.965 Table 2: The Spearman correlation coefficient between the metrics based on the representation properties and the ground truth benchmark, where gray-highlighted components represent refined metrics we proposed. metrics include Compression (DE) and Semantic V olume (Li et al., 20... | https://arxiv.org/abs/2505.17793v1 |
1.4 1.6 Partition Function Z(c)050100150200250FrequencyBefore Remove Directions After Remove Directions 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Partition Function Z(c)050100150200250FrequencyBefore Whitening After Whitening 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Partition Function Z(c)050100150200250FrequencyBefore LW Shrinkage After LW Shri... | https://arxiv.org/abs/2505.17793v1 |
scenarios worth exploring. For instance, practical techniques such as pruning, quantization, and distillation could potentially benefit from these indicators that reveal internal redundancies. Our proposed metrics help better identify compressible components in models without causing significant information loss. We an... | https://arxiv.org/abs/2505.17793v1 |
arXiv:1907.12009 . Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tieyan Liu. 2019b. Representation degeneration problem in training natural language generation mod- els. In International Conference on Learning Repre- sentations . Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of... | https://arxiv.org/abs/2505.17793v1 |
Colombo, Malik Boudiaf, Günther Koliander, and Pablo Piantanida. 2022. A differential entropy estimator for training neural net- works. In International Conference on Machine Learning , pages 17691–17715. PMLR. Olivier Roy and Martin Vetterli. 2007. The effective rank: A measure of effective dimensionality. In 2007 15t... | https://arxiv.org/abs/2505.17793v1 |
Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023. Judg- ing llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems , 36:46595–46623. Zhanghao Zhouyin and Ding Liu. 2023. Understanding neural networks withlogarithm determinant entropy estimato... | https://arxiv.org/abs/2505.17793v1 |
tive and quantitative analysis (Ethayarajh, 2019; Cai et al., 2019; Rudman et al., 2022). However, these tools are mainly based on similarity calcula- tions of embedded representations. What is needed instead is an anisotropy metric that can establish a connection with entropy based compression metric. 0 500 1000 1500 ... | https://arxiv.org/abs/2505.17793v1 |
excessive sensitivity in specific directions, promoting isotropic fea- ture distributions. These training-free paradigms provide refer- ences for decoupling anisotropy from compres- sion. However, these methods maintain the lin- ear geometric structure of the data, with eigenval- ues still exhibiting consistent partiti... | https://arxiv.org/abs/2505.17793v1 |
**** LLaMA3.2-1B-Instruct 0.78 *** LLaMA3.2-3B 0.89 **** LLaMA3.2-3B-Instruct 0.77 **** OPT-0.125B 0.88 **** OPT-1.3B 0.76 **** OPT-2.7B 0.66 ** OPT-6.7B 0.91 **** OPT-13B 0.80 *** OPT-30B 0.83 **** Qwen2.5-0.5B-Instruct 0.81 **** Qwen2.5-1.5B-Instruct 0.86 *** Qwen2.5-3B-Instruct 0.80 **** Qwen2.5-7B-Instruct 0.79 ***... | https://arxiv.org/abs/2505.17793v1 |
1957.1Compression (PCS) 0100 200 300 400 500 600 700 800706807069070700707107072070730707407075070760OPT-13B Cumulative ValueMax: 70756.5 Min: 70679.3 Final: 70704.0 0100 200 300 400 500 600 700 8000.50.60.70.80.9 Max: 1.0 Min: 0.5 Final: 0.8 0100 200 300 400 500 600 700 80015.816.016.216.416.616.817.0 Max: 17.1 Min: 1... | https://arxiv.org/abs/2505.17793v1 |
arXiv:2505.17795v1 [cs.CL] 23 May 2025DialogXpert : Driving Intelligent and Emotion-Aware Conversations through Online Value-Based Reinforcement Learning with LLM Priors Tazeek Bin Abdur Rakib1, Ambuj Mehrish2 Lay-Ki Soon1,Wern Han Lim1,Soujanya Poria2 1School of Information Technology, Monash University Malaysia 2Sing... | https://arxiv.org/abs/2505.17795v1 |
or few-shot gen- eralization capabilities of the frozen policy model. Consequently, the agent may choose locally opti- mal but globally suboptimal actions and struggle with out-of-distribution states. Dual-Process Dialogue Planner (DPDP) (He et al., 2024) improves over PPDPP with Kahne- man’s dual-process theory (Kahne... | https://arxiv.org/abs/2505.17795v1 |
ment learning approaches like PPDPP (Deng et al., 2024) and DPDP (He et al., 2024) improved explo- ration efficiency. Recent latent-policy techniques such as LDPP (He et al., 2025a) and UDP (He et al., 2025b) learn continuous action representations via V AE and diffusion-based user models. In contrast, DialogXpert trea... | https://arxiv.org/abs/2505.17795v1 |
com- bines the generative flexibility of LLMs with a constrained action space A={a1, . . . , a n}. At each dialogue turn t, the model input is: I= (ct, st, Et),, where ctis the case information, stin- cludes the conversation history, and Etrepresents the accumulated emotion. The input Iand action setAare serialized int... | https://arxiv.org/abs/2505.17795v1 |
scalar reward rt from the Critic LLM, which assesses the transi- tion(st, at, st+1)in terms of task effectiveness and emotional alignment. The tuple (st, at, rt, st+1) is appended to the replay buffer D ← D ∪ {(st, at, rt, st+1)}. Periodically, we sample minibatches from Dand perform temporal-difference updates. For ea... | https://arxiv.org/abs/2505.17795v1 |
(AT), which measures conversational efficiency by counting the mean number of turns to reach the goal (Kwan et al., 2023), and Success Rate (SR), which reflects the proportion of successful outcomes within a fixed turn limit (Gao et al., 2021). For the CraigslistBar- gain (CB) dataset, we also report the Sale-to-List R... | https://arxiv.org/abs/2505.17795v1 |
where it de- livers the highest success rates ( 0.972on ExTES) and competitive turn efficiency. These results con- firm that DialogXpert offers a practical alternative to computationally intensive planning approaches, without sacrificing quality. Impact of Emotions: Integrating emotions into policy planning improves di... | https://arxiv.org/abs/2505.17795v1 |
the action space to relevant candidates, reducing com- putation and boosting decision quality. Disabling it causes drop in performance. We can observe in Table 1 that on ESConv, success falls from 0.9876 to0.9401 and average turns rise from 2.31to3.53; on CIMA, success drops from 0.9951 to0.9317 . Without the prior, th... | https://arxiv.org/abs/2505.17795v1 |
and achieves over 93% success, with a negotiation SL of 0.3968 . Increasing to k= 3 improves success to above 95% across all tasks and further reduces turns. The optimal setting is k= 4, yielding the lowest average turns of 2.39 (negotiation/emotional support) and 2.04 (tutoring) highest success rates ( 97.1%–99.5%), a... | https://arxiv.org/abs/2505.17795v1 |
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