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detection. Hum = human caption, syn = synthetic caption, #S = number of shots. Meme 1 Meme 2 Meme 3 Features responsib_micro ,humortypes_pun responsib_macro humortypes_irony Gold Stance Convinced Skeptical Convinced Predicted StanceNeither Convinced Skeptical Gold Frames ALLOCATION , PROSPECT ADEQUACY HOAX, PROPRIETY ,... | https://arxiv.org/abs/2505.16592v2 |
From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment Jing Ye1,2, Lu Xiang1,2*, Yaping Zhang1,2, Chengqing Zong1,2 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China 2School of Artificial Intelligen... | https://arxiv.org/abs/2505.16610v1 |
might help: 1. Allow yourself to grieve... 2. Create new routines ... 3. Focus on your own healing ... ... Remember that the love and the connection you shared doesn’ t simply vanish. I'm sorry to hear that. It sounds like you'r e really missing the deep connection you had with your best friend ...... verbose, formulai... | https://arxiv.org/abs/2505.16610v1 |
user preference, including profile ,situation , and emotions . The pre- and post- refined responses are considered the preference data. Through direct preference optimization, the model generates responses that reflect an under- standing of the user’s implicit preferences during interactions, thereby eliminating the ne... | https://arxiv.org/abs/2505.16610v1 |
employ Low-Rank Adaptation (Hu et al., 2021), fine-tuning only the LoRA adapter parameters. We further incorpo- rate a replay mechanism by incorporating some instruction-following data (Wang et al., 2024c). The model is trained using the SFT loss: LSFT=−logP(y|x,P;θ) (2) where x,yrepresent the input and output of the m... | https://arxiv.org/abs/2505.16610v1 |
A, and the prompts for self-reflection and self-refinement are shown in Figure E.1. 2.2.2 Preference Optimization The synthetic preference data generation process naturally facilitates iterative self-improvement. In each iteration, we employ DPO (Rafailov et al., 2023) for training. LDPO = log σ(β·logP(˜ytn|Cn;θ) P(˜yt... | https://arxiv.org/abs/2505.16610v1 |
objective evaluation measures the similarity between model-generated and manu- ally annotated responses using the ESConv test set. Recognizing the limitations of text overlap metrics for the open-ended ES task, which can penalize informative and creative responses, we prioritize subjective evaluation to better reflect ... | https://arxiv.org/abs/2505.16610v1 |
16.72 13.37 86.28 90.84 94.72 M220.06 13.63 15.50 15.77 86.38 91.43 96.11 Qwen-2-7B-Instruct Vanilla 9.56 6.85 9.13 14.78 83.32 68.74 83.55 SFT-ESConv 19.24 13.54 17.19 13.78 86.33 90.66 94.71 SFT-SynESC 18.55 12.98 16.72 13.82 86.24 90.84 94.86 M019.18 13.56 17.00 13.82 86.27 90.68 94.94 M220.02 13.80 15.91 15.52 86.1... | https://arxiv.org/abs/2505.16610v1 |
of empathy characteristic of M0.This also confirms the effec- tiveness of using pre- and post-refined responses as preference data to learn implicit user preference. 5 Analysis and Discussion This section aims to address the following key ques- tions: Q1: Does the model exhibit self-reflection and self- refinement capa... | https://arxiv.org/abs/2505.16610v1 |
rejected responses 4https://github.com/EleutherAI/lm-evaluation-harness Model B-2 B-3 R-l D-2 M011.29 — 8.04 — 10.43 — 72.83 — {HM,P} M114.93 24.36% 10.51 30.79% 15.77 51.16% 93.71 28.67% M216.99 13.80% 11.94 13.61% 15.89 0.76% 92.75 -1.02% {HL,P} M116.37 44.96% 11.53 43.48% 16.26 55.86% 93.45 28.31% M217.40 6.29% 11.9... | https://arxiv.org/abs/2505.16610v1 |
consisting of prompt-response pairs, where one response is pre- ferred over the other (Rafailov et al., 2023). How- ever, obtaining high-quality human-generated pref- erence data is resource-intensive (Dong et al., 2024; Cui et al., 2023). To mitigate this, some studies utilize synthetic preference data generated throu... | https://arxiv.org/abs/2505.16610v1 |
crucial area for future research. Ethical Considerations Datasets such as ESConv (Liu et al., 2021), ExTES (Zheng et al., 2024b), ServeForEmo (Ye et al., 2024), and Alpaca (Taori et al., 2023), models such as LLaMA (Dubey et al., 2024), Qwen (Yang et al., 2024), and Mistral (Jiang et al., 2023), and toolk- its like LLa... | https://arxiv.org/abs/2505.16610v1 |
Pro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024 , pages 15394–15411. Association for Computational Lin- guistics. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Al... | https://arxiv.org/abs/2505.16610v1 |
guistics. R Likert. 1932. A technique for the measurement of attitudes. Chin-Yew Lin. 2004. ROUGE: A package for auto- matic evaluation of summaries. In Text Summariza- tion Branches Out , pages 74–81, Barcelona, Spain. Association for Computational Linguistics. Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, ... | https://arxiv.org/abs/2505.16610v1 |
preference optimization: Your language model is secretly a reward model. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Sys- tems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 . Stephen A Rains, Corey A Pavlich, Bethany Lutovsky, Eric Tsets... | https://arxiv.org/abs/2505.16610v1 |
Li Zhou, and Qin Jin. 2024. Escot: Towards interpretable emotional support dialogue systems. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , pages 13395–13412. Association for Computational Linguistics.... | https://arxiv.org/abs/2505.16610v1 |
. . . . . . . . . . . 14 C.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 D Human Evaluation Criteria 15 E Prompts 20 E.1 Instruction Prompts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 E.2 Prompts for LLM-as-a-Judge Evaluation . . . . ... | https://arxiv.org/abs/2505.16610v1 |
scale, where higher scores indicate better performance. We evaluate response quality by sampling 100 con- textual queries from the ESConv test set. The judge model’s decoding hyperparameters are set to tem- perature 0.8, top-p 0.95, and top-k 50. Model BLEU-2 BLEU-3 METEOR Distinct-2 LLaMA M0 18.38 — 12.95 — 13.37 — 90... | https://arxiv.org/abs/2505.16610v1 |
agents is evaluated based on several key criteria that reflect the user’s experience and satisfaction with both the quality of responses and the overall interaction. •Coherence: Measures whether the response is logical, clear, and consistent with the ongoing conversation. •Understanding: Assesses the model’s ability to... | https://arxiv.org/abs/2505.16610v1 |
failed to understand me. I wanted to explain that rest is an important part during work. I can understand how frustrating it must be when your girlfriend doesn't seem to understand your perspective. Ef fective communication is key in any relationship. Maybe you can try approaching the conversation again, using "I" stat... | https://arxiv.org/abs/2505.16610v1 |
enable future growth. You are right. It takes courage to share such perso nal struggles and express feelings of frustration. Setbacks and rejections come naturally in any field, especially research where new discoveries often require overcoming obsta cles. You've made progress already by reflecting on how far you have ... | https://arxiv.org/abs/2505.16610v1 |
help-seeker’s feelings or experiences. 3. Reflection of Feelings: Articulating and describing the seeker’s feelings. 4. Information: Providing useful information, such as data, facts, opinions, or resources, or answering questions. 5. Providing Suggestions: Offering suggestions on how to approach the issue, without ove... | https://arxiv.org/abs/2505.16610v1 |
for the user’s emotions and responds appropriately to their feelings. Apply strict penalties for critical errors and use a progressive scoring method, ensuring that basic competence is demonstrated before awarding higher scores. [Rating Criteria] Use 1-5 scale with precise criteria: 5 = Establishes a deep emotional con... | https://arxiv.org/abs/2505.16610v1 |
<Dialogue History> {conversation} </Dialogue History> <Current Response> Supporter: {response} </Current Response> LLM-as-Judge Prompt: Informativeness [Task Description] You are an expert evaluator responsible for assessing the informativeness of emotional support conversations. Does the supporter’s response offer cle... | https://arxiv.org/abs/2505.16610v1 |
violations. 4 (Advanced): Effectively identifies most emotional cues and applies suitable support strategies. Offers deep empathy and strong connection in most responses. Stays on topic and provides thoughtful safety considerations. 3 (Competent): Generally identifies user emotions and employs basic support strategies.... | https://arxiv.org/abs/2505.16610v1 |
Steering Large Language Models for Machine Translation Personalization Daniel Scalena1,2∗Gabriele Sarti1∗ Arianna Bisazza1Elisabetta Fersini2Malvina Nissim1 1CLCG, University of Groningen2University of Milano-Bicocca d.scalena@campus.unimib.it g.sarti@rug.nl Abstract High-quality machine translation systems based on la... | https://arxiv.org/abs/2505.16612v1 |
trained classi- fiers can distinguish writing styles with high accu- racy, while the task is notoriously challenging for human annotators (Youyou et al., 2015; Flekova et al., 2016). We also find a simple prompting setting with in-context personalization examples to improve the style accuracy of LLM translation, sugges... | https://arxiv.org/abs/2505.16612v1 |
data-driven adaptation from unlabeled demonstrations (Michel and Neu- big, 2018; Wang et al., 2021; Zhang et al., 2022). More recently, several studies employed prompt- ing (Garcia and Firat, 2022; Sarti et al., 2023) or preference optimization from post-editing behav- ior (Lee et al., 2023; Berger et al., 2024) to ren... | https://arxiv.org/abs/2505.16612v1 |
provided with some examples; and iii)Whether style distinctions are reflected in the model’s internal representations, to motivate the interest in steering approaches for personalization. We use the PAR3dataset by Thai et al. (2022), which contains multiple non-English novels, as a benchmark to evaluate personalization... | https://arxiv.org/abs/2505.16612v1 |
in intermediate layers are found to capture translation style information with high precision, with layer 13 performing best across all tested languages. tor available across tested novels, we provide the model with 20 in-context examples selected from the original pool of translated paragraphs by that translator, aski... | https://arxiv.org/abs/2505.16612v1 |
inference-time interventions to steer models towards personalized MT outputs. 4 Methods We begin by introducing our the prompting and steering methods that we use as baselines and outline our own proposed SAE-based steering ap- proach for personalized translation. 5Examples are resampled for every test paragraph to pre... | https://arxiv.org/abs/2505.16612v1 |
used for MS prompting. 4.3 Contrastive SAE Steering Given a set of LLM activations zl∈Rdsourced from the output of layer l, where dis the model hid- den size, a sparse autoencoder learns a projection to an overcomplete latent space Rm, with m≫d, from which a reconstructed version z∗ lof the orig- inal activations is th... | https://arxiv.org/abs/2505.16612v1 |
and non-personalized ( {Xi}−) examples is selected using this procedure.8For every selected latent, we compute its expected logit when personalization is present or absent in pro- vided examples, i.e. E+[Xi]andE−[Xi]. Inference-time intervention Finally, activations are steered by setting selected latents to their ex- ... | https://arxiv.org/abs/2505.16612v1 |
0.24 0.16 0.69 0.48 0.31 0.27 0.73 0.58 0.32 0.28 0.73 ActAdd 0.27 0.22 0.12 0.67 0.32 0.24 0.20 0.70 0.55 0.36 0.28 0.70 ReFT 0.31 0.22 0.18 0.70 0.46 0.34 0.27 0.67 0.53 0.38 0.26 0.70 SAE Cont. HT 0.39 0.27 0.19 0.70 0.46 0.33 0.29 0.72 0.59 0.31 0.27 0.72 SAE Cont. PT – 0.27 0.18 0.69 – 0.35 0.29 0.73 – 0.33 0.28 0... | https://arxiv.org/abs/2505.16612v1 |
2B model. This could be due to the larger models’ superior abil- ity to incorporate in-context information naturally, reducing the relative benefit of explicit steering. Which contrastive setup is better? Comparing the two contrastive setups ( HTand PT) for the ZS- Figure 4: Comet andHaccuracy across αsteering intensit... | https://arxiv.org/abs/2505.16612v1 |
task, our approach achieves strong alignment with hu- man translation quality, as reflected in both general human-likeness and translator-specific personaliza- tion metrics. These results highlight the method’s robustness and its potential to support high-fidelity translation workflows in real-world settings. Con- cret... | https://arxiv.org/abs/2505.16612v1 |
step-by-step: De- composing the translation process for improved trans- lation quality of long-form texts. In Proceedings of the Ninth Conference on Machine Translation , pages 1301–1317, Miami, Florida, USA. Association for Computational Linguistics. Sviatoslav Chalnev, Matthew Siu, and Arthur Conmy. 2024. Improving s... | https://arxiv.org/abs/2505.16612v1 |
at once on gemma 2. InProceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP , pages 278–300, Miami, Florida, US. Association for Computational Linguistics. Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, and Jin- song Su. 2021. Towards use... | https://arxiv.org/abs/2505.16612v1 |
in Natural Language Process- ing (EMNLP) , pages 2685–2702, Online. Association for Computational Linguistics. Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. 2024. Steer- ing llama 2 via contrastive activation addition. In Proceedings of the 62nd Annual Meeting of the As- soci... | https://arxiv.org/abs/2505.16612v1 |
Christian Hardmeier, and Andy Way. 2018. Getting gender right in neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 3003–3008, Brussels, Belgium. Associa- tion for Computational Linguistics. Rob V oigt and Dan Jurafsky. 2012. Towards a literary... | https://arxiv.org/abs/2505.16612v1 |
The Thirty-eighth Annual Conference on Neural Information Process- ing Systems . Wu Youyou, Michal Kosinski, and David Stillwell. 2015. Computer-based personality judgments are more ac- curate than those made by humans. Proceedings of the National Academy of Sciences , 112(4):1036– 1040. Zeyu Yun, Yubei Chen, Bruno Ols... | https://arxiv.org/abs/2505.16612v1 |
a batch size of 32, selecting the best model checkpoint based on validation accuracy. Training data only includes generations from models and the translator without any source text. It is also perfectly balanced, as each paragraph provides one instance for all three labels: H1, H2, and MT. The total size of the trainin... | https://arxiv.org/abs/2505.16612v1 |
3- way (MT, H1, H2) classifiers on balanced held-out sets for every language. Random baseline: 0.33. detailing the stylistic differences between a base translation and a target human translation. The prompt template used for this task is shown in List- ing 1, using the same 20 examples as in the MS, SAE Cont. HT, and S... | https://arxiv.org/abs/2505.16612v1 |
There was also a poem that said: "For twenty years, I have been trying to distinguish right from wrong, and the plum blossoms bloom in the palace. In the spring, the tigers and the rhinoceroses meet, and they dream of returning home."MT 0.49 Gemma 2 9B Looking back, he saw a painting of a bow with a quiver hanging on i... | https://arxiv.org/abs/2505.16612v1 |
imperial court resided. During the three springs, the tiger and rhinoceros met, dreaming of their return."MT 0.62 α= 5 He looked back and there hung a bow adorned with a plum blossom. A verse read: “Twenty years I ’ve struggled for truth, where fragrant plums bloom near the imperial court. Spring arrives, tiger and rhi... | https://arxiv.org/abs/2505.16612v1 |
he began yawning from hunger. Each yawn opened his mouth wide enough to look like an oven.H2 0.81 ActAdd Meanwhile, the stable was closed and Pinocchio remained alone. Because many hours passed without eating, he started yawning from very big hunger. And while yawning, he opened his mouth wide like an oven.MT 0.75 ReFT... | https://arxiv.org/abs/2505.16612v1 |
author's voice, keeping a balance between academic rigor and narrative engagement. − Ensure smooth transitions between sentences and paragraphs to support coherent argumentation. − Translate idiomatic expressions in a way that retains their intended effect, even if the wording differs. When comparing H1 and H2 for Bewa... | https://arxiv.org/abs/2505.16612v1 |
goodness [...]H1 0.23 Source (DE) Nein was Sie lieber haben, Herr Leutnant! Nur keine Zeremonien, es ist doch ganz einerlei. classifier Comet H2 "No, no-whatever you would rather have, Lieutenant Hofmiller! Please don’t stand on ceremony, it makes no difference to us."- - ZS No, whatever you prefer, Lieutenant! Just no... | https://arxiv.org/abs/2505.16612v1 |
arXiv:2505.16624v1 [cs.CV] 22 May 20251 Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports Francesco Dalla Serra, Patrick Schrempf, Chaoyang Wang, Zaiqiao Meng, Fani Deligianni, and Alison Q. O’Neil Abstract — We present a novel approach to Chest X- ray (CXR) Visual Question Answering (VQA... | https://arxiv.org/abs/2505.16624v1 |
has attracted a lot of interest. In this paper, we focus on VQA applied to Chest X-Ray (CXR) radiology. CXR images are 2D projections through various overlapping structures within the thoracic cavity, such as the ribs, heart, and lungs. This makes it challenging to isolate and identify abnormalities in specific structu... | https://arxiv.org/abs/2505.16624v1 |
want to perform. Our approach adopts anatomy-finding anatomical tokens [6] and a longitudinal projection module [5]. Moreover, we study the effect of grounding the answer generation module using a radiology report predicted from the same image. Our approach is inspired by Chain-of-Thought (CoT) reasoning, as demon- str... | https://arxiv.org/abs/2505.16624v1 |
question answering , henceforth referred to asdiff-VQA , is the task where questions refer to the differences between two or more images. This task has received limited attention in both the general [28, 44] and medical [13, 4] domains. In the medical domain, this is primarily due to the DALLA SERRA et al. : GROUNDING ... | https://arxiv.org/abs/2505.16624v1 |
T), (1) which is achieved in an autoregressive manner. The text inputs and outputs vary based on the specific task (RG or AG). The VLM architecture of the RG and AG models are shown in Figure 2. 1) Longitudinal Projection Module: The LPM is responsible for projecting the current and prior CXR scans into a joint represe... | https://arxiv.org/abs/2505.16624v1 |
or the answer. The diagram shows how visual inputs ( i.e., anatomical tokens Atok) are aligned, concatenated and projected into a joint representation via the Longitudinal Projection Module . This representation is then combined with tokenised text inputs, which the language model processes to generate the target text.... | https://arxiv.org/abs/2505.16624v1 |
their attributes. (3) The question-answer pairs were generated based on the entities DALLA SERRA et al. : GROUNDING CHEST X-RAY VISUAL QUESTION ANSWERING WITH GENERATED RADIOLOGY REPORTS 5 Split QA pairs CXR pairs Training 560,563 88,098 Validation 70,070 10,864 Test 70,070 10,963 TABLE I NUMBER OF QA PAIRS AND CXR PAI... | https://arxiv.org/abs/2505.16624v1 |
BLEU [27], METEOR [1], ROUGE [21], and CIDEr [38]. We calculate exact-match accuracy for other types of ques- tions, differentiating between open-ended and closed (yes/no) questions. D. Baselines We evaluate our method against existing approaches de- signed for comparing multiple input images. For the diff-VQA task, we... | https://arxiv.org/abs/2505.16624v1 |
GENERATION STEP (RG-AG) AND WITHOUT IT (AG), AND PREVIOUS METHODS ON ALL BUT THE difference QUESTIONS OF MIMIC- DIFF-VQA DATASET [13]. W E COMPUTE THE ACCURACY (EXACT MATCH )ON THE OPEN -ENDED AND THE CLOSED -ENDED QUESTIONS (YES/NO). W E SHOW THE BEST RESULTS IN BOLD . ALL THE RESULTS OF THE COMPARISON METHODS ARE TAK... | https://arxiv.org/abs/2505.16624v1 |
the importance of this input in enhancing model performance. Figure 5 highlights several borderline and failure cases of our proposed RG-AG model. These examples illustrate: (rows 1–2) the model’s robustness to inconsistencies within the predicted reports ( i.e., different descriptions between the “finding” and “impres... | https://arxiv.org/abs/2505.16624v1 |
are a total of only 18 unique question templates into which relevant keywords are inserted from data-mined lists of 29disease keywords and approximately 100 keywords describing spatial distribution. This means that more nuanced variation is lacking; for instance for the ‘difference’ question type, rather than asking “ ... | https://arxiv.org/abs/2505.16624v1 |
We leave these two directions as open questions for future work in this space. REFERENCES [1] Satanjeev Banerjee and Alon Lavie. “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments”. In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine ... | https://arxiv.org/abs/2505.16624v1 |
visual question answering”. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2023, pp. 4156–4165. [14] Yefan Huang, Xiaoli Wang, Feiyan Liu, and Guofeng Huang. “OVQA: A clinically generated visual question answering dataset”. In: Proceedings of the 45th Interna- tional ACM SIGI... | https://arxiv.org/abs/2505.16624v1 |
Dario Amodei, Ilya Sutskever, et al. “Language models are unsupervised multitask learners”. In: OpenAI blog 1.8 (2019), p. 9. [30] F. Ren and Y . Zhou. “CGMVQA: A New Classification and Generative Model for Medical Visual Question An- swering”. In: IEEE Access 8 (2020), pp. 50626–50636. [31] Shaoqing Ren, Kaiming He, R... | https://arxiv.org/abs/2505.16624v1 |
Dataset for Clinical Reasoning”. In: NeurIPS: Datasets and Benchmarks Track (Round 2) . 2021. [44] Linli Yao, Weiying Wang, and Qin Jin. “Image dif- ference captioning with pre-training and contrastive learning”. In: Proceedings of the AAAI Conference on Artificial Intelligence . V ol. 36. 3. 2022, pp. 3108–3116. [45] ... | https://arxiv.org/abs/2505.16624v1 |
MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries Jonghwi Kim1, Deokhyung Kang1,, Seonjeong Hwang1, Yunsu Kim3,Jungseul Ok1,2,Gary Geunbae Lee1,2, 1Graduate School of Artificial Intelligence, POSTECH, Republic of Korea , 2Department of Computer Science and Engineering, POSTECH, Republic ... | https://arxiv.org/abs/2505.16631v1 |
performance analysis of multilingual IR models on MiLQ , establishing initial baselines for MQIR . •We show intentionally mixed-language queries are effective for English document retrieval across diverse methods, providing token-level analysis of their rationale. 2 MiLQ: Mixed-Language Query test set Data Construction... | https://arxiv.org/abs/2505.16631v1 |
scale, higher=more mixing; Ap- pendix A.3). Average CMI increased from 6.2 to 39.5 (Title) and 5.0 to 31.6 (Description), show- ing substantially more mixing than originals. Next, GPT-Eval (GPT-4o) using Kuwanto et al.’s frame- work (high human alignment, Kendall’s Tau > 0.5) assessed MiLQ (1-3 scale; rubrics in Append... | https://arxiv.org/abs/2505.16631v1 |
annotators in all languages. These discussions revealed that Somali speakers frequently code-switch, primarily using English to express modern concepts due to Somali’s limited contemporary vocabulary—findings aligned with prior literature (Andrzejewski, 1979, 1978; Kap- chits, 2019). Further interview insights, includi... | https://arxiv.org/abs/2505.16631v1 |
challenge MiLQ addresses: de- veloping a single robust IR model for bilingual users freely querying in native, English or mixed language. To better harness this potential of code- switched training data explored in prior studies (Litschko et al., 2023; Liu et al., 2025), future work could explore advanced methods, like... | https://arxiv.org/abs/2505.16631v1 |
native tokens (e.g., German "Intellekt," "Eigen") to map English concepts. While MaxSim pairings are also iden- tified (×), this mapping yields weaker similarity for such crucial English concepts. Thus, inten- tionally mixing English terms improves MaxSim matching through higher similarity scores for En- glish terms—a ... | https://arxiv.org/abs/2505.16631v1 |
Clark, Kenton Lee, Eunsol Choi, and Hannaneh Hajishirzi. 2021. XOR QA: Cross-lingual open-retrieval question answering. InProceedings of the 2021 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies , pages 547–564, Online. Association for Computa- tio... | https://arxiv.org/abs/2505.16631v1 |
2009. Code-switching . Cambridge university press. Parth Gupta, Kalika Bali, Rafael E Banchs, Monojit Choudhury, and Paolo Rosso. 2014. Query expansion for mixed-script information retrieval. In Proceed- ings of the 37th international ACM SIGIR conference on Research & development in information retrieval , pages 677–6... | https://arxiv.org/abs/2505.16631v1 |
Ji-Rong Wen, Hao Jiang, Xinyu Zhang, and Zhao Cao. 2024. An analysis on matching mechanisms and token prun- ing for late-interaction models. ACM Transactions on Information Systems , 42(5):1–28. Carol Myers-Scotton. 1997. Duelling languages: Gram- matical structure in codeswitching . Oxford Univer- sity Press. Suraj Na... | https://arxiv.org/abs/2505.16631v1 |
RU, we created 94, 90, and 88 queries, respectively, with a total cost of $20 per language. Figure 4: Guideline for German-English mixed-language search query annotators. 2https://www.upwork.com A.2 Examples of Title and Description queries in MiLQ This appendix illustrates Title and Description mixed-language queries ... | https://arxiv.org/abs/2505.16631v1 |
text at the word level using NLTK3. For Chinese text, we apply Jieba4, a specialized tokenizer optimized for Chinese word segmentation. After tokenization, we utilize GPT-4o to classify each token’s language using the prompt template shown in Figure 7. 3https://www.nltk.org/ 4https://github.com/fxsjy/jieba Identify the... | https://arxiv.org/abs/2505.16631v1 |
compensated at $20 per annotator; FI, FR, and DE annotators at $30; and FA, ZH, and RU annotators at $15 each. Accuracy and Fluency Rubrics Accuracy and Fluency rubrics mirrored those used in GPT-Evaluation (see Appendix A.4). Accuracy measures how well a MiLQ preserves the original query’s meaning and appropriately in... | https://arxiv.org/abs/2505.16631v1 |
readily accessible terms. Furthermore, mixed-language can serve to simplify grammatical or orthographic complexities inherent in some native languages, or address deficiencies in language modernization where native terminology for contemporary concepts is lacking. It is important to note that the specific motivations a... | https://arxiv.org/abs/2505.16631v1 |
established methods (Bendersky and Kurland, 2008; Dai and Callan, 2019), documents longer than this threshold were segmented into 180-token passages. During evaluation, the score for each document was determined using the maximum passage score (MaxP) strategy. Model Backbones and Computational Resources We fine-tuned d... | https://arxiv.org/abs/2505.16631v1 |
arXiv:2505.16637v2 [cs.CL] 23 May 2025 SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation Wenjie Yang, Mao Zheng, Mingyang Song, Zheng Li Tencent Hunyuan leonzxyang@tencent.com Abstract Large language models (LLMs) have recently demonstrated remarkable capabilities in ma- chine translation (... | https://arxiv.org/abs/2505.16637v2 |
from costly human annotations or pre- trained reward models distilled from expensive la- beled data, posing ongoing scalability issues. To address this challenge, we propose a Sim- ple Self-Rewarding ( SSR) RL framework for MT, eliminating the need for any external supervision. SSR leverages a self-judging mechanism in... | https://arxiv.org/abs/2505.16637v2 |
resources has become increasingly expensive and challenging, creating significant bot- tlenecks for the sustainable development of MT models. 2.2 MT via Reinforcement Learning Early MT research employed reinforcement learn- ing (RL) to tackle exposure bias, an issue inherent in supervised fine-tuning (Bengio et al., 20... | https://arxiv.org/abs/2505.16637v2 |
employing Monte Carlo Tree Search to derive preferences based on cross-lingual semantic consistency fromthe model’s own outputs. They then used this data for preference learning. Yet, even with the same base model (Qwen-2.5-7B), their method did not outperform MT-specific LLMs like TowerInstruct as our approach. Compar... | https://arxiv.org/abs/2505.16637v2 |
and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: Score the following translation from {src_lang} to {tgt_lang} on a continuous scale from 0 to 100, where a score of zero means “no meaning preserved” and score of one ... | https://arxiv.org/abs/2505.16637v2 |
maximizing the following objective:JGRPO(θ) =Eq∼P(Q),{oi}G i=1∼πθold(O|p) " 1 GGX i=1min πθ(oi|p) πθold(oi|p)Ai, clipπθ(oi|p) πθold(oi|p),1−ε,1 +ε Ai −βD KL(πθ∥πref)!# , where εandβare hyperparameters, πrefis the reference model, and DKL(πθ∥πref)is the KL di- vergence between πθandπref. 4 Experiments 4.1 Experimental... | https://arxiv.org/abs/2505.16637v2 |
88.60 87.85 96.33 86.92 77.10 90.68 75.88 83.33 87.58 92.83 84.57 Ours Qwen2.5-7B 62.62 75.69 69.04 77.33 73.62 85.54 73.97 68.25 81.63 64.28 69.48 82.00 86.07 75.29 SSR-Zero-7B 79.29 92.04 79.04 89.19 87.97 96.70 87.37 79.69 91.18 79.34 85.34 89.25 93.52 86.39 SSR-X-Zero-7B 80.62 91.92 80.56 89.42 88.84 96.62 88.00 81... | https://arxiv.org/abs/2505.16637v2 |
→EN translation (87.37) compared to GPT-4o (87.91), Gemini-1.5- Pro (87.76), and Claude-3.5-Sonnet (88.89). How- ever, in the EN →ZH direction, SSR-Zero-7B sur- passes GPT-4o, achieving 86.39 compared to GPT- 4o’s 84.71. Compared with the backbone model (Qwen2.5- 7B), SSR-Zero-7B significantly improves transla- tion pe... | https://arxiv.org/abs/2505.16637v2 |
75.98 82.21 87.28 92.19 83.96 - Qwen2.5-7B-Instruct 79.10 91.58 79.28 88.56 87.98 96.19 87.12 77.03 89.73 76.60 82.16 87.87 92.07 84.24 w/ External LLM-as-a-judge RM (with Reference): - Qwen2.5-7B 79.30 91.11 79.33 88.57 88.27 96.54 87.19 77.90 90.00 77.69 83.43 88.38 92.63 85.01 - Qwen2.5-7B-Instruct 79.10 91.58 79.28... | https://arxiv.org/abs/2505.16637v2 |
lowers the results for EN →ZH translation (from 86.39 to 85.25, -1.3%). In general, intro- ducing reference translations to different reward methods does not consistently improve the model’s performance, except when using external LLMs as judges. In particular, the addition of external ref- erences does not provide sig... | https://arxiv.org/abs/2505.16637v2 |
Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Jon Ander Campos, Yi Chern Tan, and 1 others. 2024. Aya 23: Open weight releases to further multilingual progress. arXiv preprint arXiv:2405.15032 . Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Jo... | https://arxiv.org/abs/2505.16637v2 |
evaluators of trans- lation quality. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation , pages 193–203, Tampere, Finland. Euro- pean Association for Machine Translation. Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, and Yu Wu. 2025. Inference... | https://arxiv.org/abs/2505.16637v2 |
reinforcement learning. Jiaan Wang, Fandong Meng, Yunlong Liang, and Jie Zhou. 2024. Drt: Deep reasoning translation via long chain-of-thought. arXiv preprint arXiv:2412.17498 . Jiaan Wang, Fandong Meng, and Jie Zhou. 2025. Deep reasoning translation via reinforcement learn- ing. arXiv preprint arXiv:2504.10187 . BigSc... | https://arxiv.org/abs/2505.16637v2 |
arXiv:2505.16648v1 [cs.CL] 22 May 2025Collaboration among Multiple Large Language Models for Medical Question Answering 1stKexin Shang College of Computing & Informatics Drexel University Philadelphia, USA ks4252@drexel.edu2ndChia-Hsuan Chang College of Computing & Informatics Drexel University Philadelphia, USA shane.... | https://arxiv.org/abs/2505.16648v1 |
II. R ELATED WORK A. Medical QA with LLMs Multiple studies have evaluated and reported on the use of LLMs in medical QA using different LLMs and datasets. These include medical textbook questions on ChatGPT [5], medical licensing exam questions on ChatGPT, GPT-3, and GPT-4 [6]–[9], and real world patient questions on C... | https://arxiv.org/abs/2505.16648v1 |
of the collaboration mechanisms involve debate or exchanging opinions on a question, they may not use the chain-of-thought [19], [20] ap- proach to obtain the LLM reasoning for the generated answer, which has been demonstrated to improve model performance. Additionally, there is a lack of studies specifically looking a... | https://arxiv.org/abs/2505.16648v1 |
q∈Q, we use a base LLM ϕto generate n= 10 responses (i.e., line 1 - line 3). During this process, ZS-CoT [20] is utilized to first elicit a step-wise reasoning with prompt template Treasoning and then a letter choice with prompt template Tanswer . Next, the most consistent letter choice out ofngenerations will be selec... | https://arxiv.org/abs/2505.16648v1 |
teammates in various medical fields. Therefore, three open-source LLMs from unique backgrounds are selected to assemble a multi-LLM system: med42-70B- v1[1], ClinicalCamel-70B [2], and Mixtral 8x7B [22]. For brevity, we will refer to their short name, Med42, ClinCamel, and Mixtral in the rest of this paper. The first m... | https://arxiv.org/abs/2505.16648v1 |
we first classify Qdisby the level of teammate support: •S+: disagreed questions where at least one teammate hold the same answer as the base model (with support) •S−: disagreed questions where none of teammate propose the same answer as the base model (without support) ZS-CoT-SC Prompt Treasoning <|System| >: You are ... | https://arxiv.org/abs/2505.16648v1 |
we denote Count (ˆyq,ϕ) =|{yi|1≤i≤n, yi= ˆyq,ϕ}| as the frequency of the majority vote ˆyq,ϕof a question qby the LLM ϕ, where nis the parameter of self-consistency con- trolling the number of repeats. For example, Count (ˆyq,ϕ) = 6 TABLE II: Consensus Convergence Consensus Rate ( Pcon) USMLE ( |Q|) ZS-CoT-SC Collabora... | https://arxiv.org/abs/2505.16648v1 |
( S−) 45.33 12.99 42.24 W/ support ( S+) 68.10 52.11 69.90 Confidence 0.57 0.33 0.56 the variation of consistency between two situations. Results from both ZS-CoT-SC and collaboration are presented. First, all LLM non-exclusively tend to maintain high self- consistency level when their majority vote hit the ground trut... | https://arxiv.org/abs/2505.16648v1 |
of 6.56% with only 0.23 confidence while Mixtral and Med42 gained lower accuracy improvement of 5.91% and 5.24% respectively, which corresponds to their higher confidence of0.49 and 0.51. We utilize the Spearman’s rank correlation [30] to compute a rank-based correlation coefficient rsbetween accuracy improvement and c... | https://arxiv.org/abs/2505.16648v1 |
the generation of false content is relatively random, it is easily detected through self-consistency sampling as low consistency. Holding this assumption, we reckon it is practical to con- sider LLMs’ consistency a potential indicator of its correct- ness, which could be utilized in unsupervised learning. VI. C ONCLUSI... | https://arxiv.org/abs/2505.16648v1 |
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