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experiment. Specifically, we replaced the brand name “McDon- ald’s” with a fictitious name “Stack Shack” while keeping all other attributes of the item, including its recommendation probability, unchanged. The purpose was to examine whether altering the brand name would influence user’s choice. As shown in Figure 7(a),... | https://arxiv.org/abs/2505.16429v1 |
Narimasa Watanabe. 2025. Simuser: Simulating user behavior with large lan- guage models for recommender system evaluation. arXiv preprint arXiv:2504.12722 . Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2019. Generative adversarial user model for reinforcement learning based recommen- dation syst... | https://arxiv.org/abs/2505.16429v1 |
arXiv preprint arXiv:2403.09498 . Yuhan Liu, Yuxuan Liu, Xiaoqing Zhang, Xiuying Chen, and Rui Yan. 2025. The truth becomes clearer through debate! multi-agent systems with large lan- guage models unmask fake news. arXiv preprint arXiv:2505.08532 . Yuhan Liu, Zirui Song, Xiaoqing Zhang, Xiuying Chen, and Rui Yan. 2024c... | https://arxiv.org/abs/2505.16429v1 |
Wei He, Yi- wen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, and 1 others. 2025. The rise and potential of large language model based agents: A survey. Science China Information Sci- ences , 68(2):121101. Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xi- aolong Wang, Weidong Liu, and Yang Liu. 2023.... | https://arxiv.org/abs/2505.16429v1 |
validation set as the final trained model. Figure 8: Impact of merchant reply on likes count. A.5 Base Model Setting For closed-source large language models, we ac- cess them via the official API, configuring the tem- perature to 0, top-p to 1, and setting both the fre- quency penalty and presence penalty to 0. For ope... | https://arxiv.org/abs/2505.16429v1 |
of real reviews and simula- tion reviews. Figure 11: Items distribution comparison between real- ity and simulation for GoogleLocal dataset. It can be observed that the choice of base model has a certain impact on the final performance; closed- source models such as GPT-4o generally achieve higher accuracy compared to ... | https://arxiv.org/abs/2505.16429v1 |
109 0.3800 0.2567 0.5617 SimUSER 106 54 140 0.3533 0.1800 0.5867 RecInter 143 28 129 0.4767 0.0933 0.6917 Table 5: LLM-Based simulation credibility comparison of different methods. tion study. From the pairs evaluated by the GPT-4o Judge Agent, we randomly selected a subset of 30 distinct pairs of agent simulation samp... | https://arxiv.org/abs/2505.16429v1 |
and friendly service. And the sales are also very good . So, I will make a purchase try for it", Action: [{ "name": " purchase_product ", "arguments": { "product_id ": 4, #McDonald’s "purchase_num ": 1 } }] Figure 14: A case of agent response. D Prompts D.1 Subjective Profile Prompt The prompt used in Subjective Profil... | https://arxiv.org/abs/2505.16429v1 |
arXiv:2505.16460v1 [cs.CL] 22 May 2025University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection Ikhlasul Akmal Hanif, Eryawan Presma Yulianrifat, Jaycent Gunawan Ongris, Eduardus Tjitrahardja, Muhammad Falensi Azmi, Rahmat Bryan Naufal, Alfan Farizki Wicakso... | https://arxiv.org/abs/2505.16460v1 |
mod- els such as LLaMA, GPT, DeepSeek, and Qwen (Brown et al., 2020; OpenAI et al., 2024; DeepSeek-AI et al., 2025; Yang et al., 2024; Grattafiori et al., 2024), alongside the widespread use of the BERT family of models (Devlin et al., 2019; Zhuang et al., 2021; Conneau et al., 2020), have demonstrated strong performan... | https://arxiv.org/abs/2505.16460v1 |
samples, |Ci|is the number of samples in class i,kis the total number of classes. 3.2 End-to-End Fine-Tuning Fine-tuning Strategy. The first type of model in- volves fine-tuning independently for each emotion category (BR). For the cross-encoder model, we explore two strategies: 1.Multiple Head Approach. A single outpu... | https://arxiv.org/abs/2505.16460v1 |
to ensure an equal distribution of la- bels. Computational Power Used. We use different machines for different experiments. Lightweight experiments, such as running tree-based models, are conducted using Kaggle’s free GPU, while heavier tasks, such as inferencing with BGE, mE5, JINA , are performed on an RTX 4090 rente... | https://arxiv.org/abs/2505.16460v1 |
on the MMTEB (Enevoldsen et al., 2025) multilingual em- bedding benchmark, which attests to their stronger multilingual representations; fine-tuning on low- resource task data cannot match this pre-validated embedding quality. BGE as the Overall Best Result. The statistical test yielded a significant result ( W= 205 , ... | https://arxiv.org/abs/2505.16460v1 |
the languages in the dataset, our analysis primarily relies on quantitative methods. 7 Conclusion Our study demonstrates that classifier-based ap- proaches with prompt-based encoders, particu- larly BGE and multilingual-E5 (mE5), outper- form fully fine-tuned transformer models for mul- tilingual multi-label emotion cl... | https://arxiv.org/abs/2505.16460v1 |
Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping ... | https://arxiv.org/abs/2505.16460v1 |
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schel- ten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mi- tra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Ro- driguez... | https://arxiv.org/abs/2505.16460v1 |
Aayushi Sri- vastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein, Amanda Kallet, Amit San- gani, Amos Teo, Anam Yunus, Andrei Lupu, An- dres Alvarado, Andrew Caples, Andrew Gu, Andrew Ho, Andrew Pou... | https://arxiv.org/abs/2505.16460v1 |
Sharadh Ramaswamy, Shaun Lind- say, Shaun Lindsay, Sheng Feng, Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satterfield, Sudarshan Govindaprasad, Sumit Gupta, ... | https://arxiv.org/abs/2505.16460v1 |
Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufino Cardenas, Rendi Chevi, Chia- maka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat,... | https://arxiv.org/abs/2505.16460v1 |
Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, DanielMossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Ne... | https://arxiv.org/abs/2505.16460v1 |
Ke Chen, and Yun Yang. 2021. Multi- label classification with weighted classifier selec- tion and stacked ensemble. Information Sciences , 557:421–442. An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Hao- ran Wei, Huan Lin, Jialong ... | https://arxiv.org/abs/2505.16460v1 |
46.17 swa 39.07 39.10 36.20 36.22 34.61 30.21 swe 49.31 57.20 47.48 48.98 47.22 47.93 tat 48.72 56.24 56.94 52.84 50.11 61.59 tir 35.04 39.40 38.70 36.62 38.38 38.49 ukr 51.79 52.45 48.69 56.09 52.18 45.43 vmw 14.95 16.47 17.98 19.58 16.74 18.79 yor 31.48 32.68 26.74 32.65 33.76 29.42 average 53.52 55.40 54.19 55.39 52... | https://arxiv.org/abs/2505.16460v1 |
78.42 90.88 69.40 87.23 91.03 orm 37.33 39.22 35.88 28.15 28.82 32.08 pcm 48.62 50.18 42.79 44.83 40.96 46.06 ptbr 52.69 47.42 41.04 48.09 39.98 38.04 ptmz 41.45 37.24 38.96 29.93 28.84 46.19 ron 68.09 69.71 72.17 67.40 55.76 71.86 rus 75.03 72.25 80.72 61.39 74.09 81.90 som 39.38 37.86 30.08 28.65 27.77 31.71 sun 47.9... | https://arxiv.org/abs/2505.16460v1 |
23.91 24.16 swe 40.59 39.03 41.48 48.00 41.47 42.49 tat 14.15 26.46 51.14 52.01 45.92 43.53 tir 25.26 25.95 24.67 21.56 25.67 21.75 ukr 40.79 33.65 41.48 51.65 41.59 33.36 vmw 01.62 06.07 25.41 14.63 18.07 11.87 yor 06.67 09.52 31.70 28.03 21.82 24.96 average 36.38 38.54 46.71 47.10 39.95 42.40 Table 10: Detailed perfo... | https://arxiv.org/abs/2505.16460v1 |
XLMR-SVM-LANG XLMR-XGB-ALL XLMR-XGB-LANG afr 17.13 29.51 14.79 18.72 amh 24.18 38.36 38.51 36.58 arq 38.47 35.88 39.46 35.59 ary 24.68 30.92 26.77 23.71 chn 24.88 40.11 39.64 35.97 deu 38.24 41.24 42.09 40.91 eng 41.83 46.20 47.89 43.88 esp 35.92 45.50 53.54 50.61 hau 26.81 39.17 39.11 39.07 hin 27.04 41.26 62.35 51.74... | https://arxiv.org/abs/2505.16460v1 |
arXiv:2505.16467v1 [cs.CL] 22 May 2025Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization Vera Neplenbroek1, Arianna Bisazza2, Raquel Fernández1 1Institute for Logic, Language and Computation, University of Amsterdam 2Center for Language and Cognition, University of Groningen {v.e.neplenbr... | https://arxiv.org/abs/2505.16467v1 |
unaware of, is that whenever LLMs engage in this implicit per- sonalization , their responses may differ not only in content, but also in quality. For instance, there is evidence indicating that users assumed to be men receive longer and more detailed responses than women (Chen et al., 2024b), neighborhood and col- leg... | https://arxiv.org/abs/2505.16467v1 |
associ- ations. Taken together, our results deepen our under- standing of how LLM’s latent user representations are influenced by stereotypes, revealing undesir-able implicit personalization in current LLMs and suggesting possible paths forward to alleviate ex- isting issues. 2 Related Work Personalization or ‘user mod... | https://arxiv.org/abs/2505.16467v1 |
to exhibiting difficulties with conver- sational memory, Kantharuban et al. (2024) show that LLMs do not admit to engaging in implicit personalization when asked; instead, models tend to provide unfaithful explanations of their own rea- soning (Turpin et al., 2023; Chen et al., 2024a). Hence, besides questioning the mo... | https://arxiv.org/abs/2505.16467v1 |
(i.e., with- out stereotypical associations) for the topics food, drinks , and hobbies .4Examples are shown in Ta- ble 2. All items (404 in total) are provided in the codebase. Conversations As illustrated in Figure 1, conver- sations consist of a user introduction followed by 6 rounds of interaction, where each round ... | https://arxiv.org/abs/2505.16467v1 |
shows an example of a full con- versation.Appendix B for more details about these models and the compute budget used for all experiments. 3.3 Evaluation We evaluate the LLM’s latent representation of the user at 4 points in the conversation: during the ini- tial round in which the user introduces themselves (with or wi... | https://arxiv.org/abs/2505.16467v1 |
into account, e.g., ‘What are some books or movies that represent peo- ple from my background?’ . Direct questions more closely match the fact-retrieval questions used to test conversational memory, but we suspect mod- els might refuse to answer such targeted questions about demographic attributes. Indirect questions i... | https://arxiv.org/abs/2505.16467v1 |
6 User turn020406080100Accuracy Model Gemma Llama OLMo (c) Direct questions 0 1 3 6 User turn020406080100Accuracy Model Gemma Llama OLMo (d) Indirect questions Figure 3: Surprisal results, probe accuracy and accuracy on direct and indirect questions for explicit+neutral conversations. The user’s introduction is indicat... | https://arxiv.org/abs/2505.16467v1 |
those answers do not exceed 2% for any model or group over the course of the conversation. How- ever, a substantially different picture emerges with other evaluation techniques. Surprisal values and probe accuracy reveal that the latent user represen- tations of all models are significantly affected by stereotypes abou... | https://arxiv.org/abs/2505.16467v1 |
Reported results are averages across all gender groups (Female, Male, Non-Binary), with translucent error bands indicating the 95% confi- dence interval. and probing classifier accuracy, Gemma is still sig- nificantly affected by stereotypes for the majority of groups (see Figure 5a for the gender attribute). For non-b... | https://arxiv.org/abs/2505.16467v1 |
and indirect questions to measure its effect.10 Results To mitigate the effect of stereotypes that contrast with the user’s explicitly stated demo- graphic group (RQ3), we use the probe’s weights to steer the model’s user representations towards that group. For all models, we observe that this steering is highly effect... | https://arxiv.org/abs/2505.16467v1 |
limits us to the demo- graphic groups we included and the stereotypical topics we have collected, which are both to a large extent U.S-centered. We acknowledge that these are both non-exhaustive sets, which do not cover all real-life demographic groups that may suffer from implicit personalization due to stereotypes, n... | https://arxiv.org/abs/2505.16467v1 |
and Kathleen Mckeown. 2024a. Do models explain themselves? Counterfactual simulatability of natural language ex- planations. In Proceedings of the 41st International Conference on Machine Learning , volume 235 of Proceedings of Machine Learning Research , pages 7880–7904. PMLR. Yida Chen, Aoyu Wu, Trevor DePodesta, Cat... | https://arxiv.org/abs/2505.16467v1 |
of Large Lan- guage Models. Preprint , arXiv:2504.10227.Anjali Kantharuban, Jeremiah Milbauer, Emma Strubell, and Graham Neubig. 2024. Stereotype or Personal- ization? User Identity Biases Chatbot Recommenda- tions. Preprint , arXiv:2410.05613. Yonghee Kim, Jeesoo Bang, Junhwi Choi, Seonghan Ryu, Sangjun Koo, and Gary ... | https://arxiv.org/abs/2505.16467v1 |
Kaffee, and Isabelle Augenstein. 2025. Presumed Cultural Iden- tity: How Names Shape LLM Responses. Preprint , arXiv:2502.11995. Karl Pearson. 1900. On the criterion that a given system of deviations from the probable in the case of a corre- lated system of variables is such that it can be reason- ably supposed to have... | https://arxiv.org/abs/2505.16467v1 |
demographic group except non-binary gender, as we were unable to find stereotypical associations for our topics. For the other 12demographic groups, we collect a to- tal of 404items, which comes down to ~ 33items per group. Out of these 404items, the majority (214) belong to the hobbies category, followed by food (133)... | https://arxiv.org/abs/2505.16467v1 |
introductions without demographic information and introductions with explicitly mentioned demo- graphic information (see Table 4 for introduction templates). For introductions with explicit demo- graphic information, we insert a description of a person from that demographic group (see Table 5 for descriptions of each d... | https://arxiv.org/abs/2505.16467v1 |
Layer050100AccuracyGemma 0 10 20 30 LayerLlama 0 10 20 30 LayerOLMo Figure 9: Probe accuracy per model layer for socio- economic status . one direct question (‘What is my {demographic attribute}?’) and 5indirect questions that ask for advice, recommendations or creative writing for which the user’s demographic should b... | https://arxiv.org/abs/2505.16467v1 |
group, and therefore lists multiple (e.g. for age: ‘New- borns need x hours of sleep. Children need y hours of sleep. Adults need z hours of sleep.’). We also have a few other interesting observations from this qualitative analysis: Foragewe find that both OLMo and Llama miscalculate the user’s birth year when the user... | https://arxiv.org/abs/2505.16467v1 |
respectively. D.4 Mitigating undesirable implicit personalization We mitigate undesirable implicit personalization as a result of stereotypes by using the weights of our trained probes to steer the model’s latent rep- resentations in the direction of a/no particular de- mographic group. More specifically, we take the w... | https://arxiv.org/abs/2505.16467v1 |
want to ask you some questions. Hello I am {} and I want to ask you some questions. Hi, I want to ask you some questions. Hi I am {} and I want to ask you some questions. Hey, can I ask you some questions? Hey I am {}, can I ask you some questions? Hello, can I ask you some questions? Hello I am {}, can I ask you some ... | https://arxiv.org/abs/2505.16467v1 |
30-year-old, a 35-year-old, a 40-year-old, a 45-year-old, an adult man, an adult woman, an adult, a 50-year-old Age Older Adultan elderly man, an elderly woman, an old man, an old woman, a senior citizen, a 90-year-old, a 70-year-old, a 75-year-old, an 80-year-old, an 85-year-old Gender Female a woman, a girl, an aunt,... | https://arxiv.org/abs/2505.16467v1 |
.4→91.6 93 .5→41.4 76 .8→57.8 SES 100.0→99.8 100 .0→100.0 61 .0→0.0 19 .7→0.0 Table 7: Results for Gemma for explicit+neutral conversations. Reported results are for the group corresponding to the explicit demographic information. Reported results are from round 0, right after the introduction, and round 6 which is the... | https://arxiv.org/abs/2505.16467v1 |
.1(∆+6.9) Gender female 20.0(∆+19.9 ) 99 .6(∆−0.4) 0 .0(∆0.0) 20 .9(∆ + 2 .7) Gender male 10.9(∆+10.6 ) 17 .2(∆+17.2 ) 0.0(∆0.0) 8 .2(∆ + 0 .5) Race asian 24.6(∆+24.6 ) 0.8(∆ + 0 .8) 0 .0(∆0.0) 25 .8(∆+23.7 ) Race black 13.2(∆+13.2 ) 3.6(∆+3.6) 0 .0(∆0.0) 16 .0(∆+8.6) Race hispanic 53.8(∆+53.8 ) 74 .8(∆+74.4 ) 0.0(∆0.0... | https://arxiv.org/abs/2505.16467v1 |
38 .1(∆−1.7) Gender female male 69.0(∆-29.9) 82 .4(∆−17.6) 4 .4(∆−4.0) 41 .4(∆-10.6) Gender male female 55.8(∆-44.0) 51 .2(∆-48.4) 7 .2(∆−6.0) 46 .4(∆-10.1) Gender non-binary female 37.1(∆-47.5) 1 .6(∆−6.8) 21 .6(∆−1.2) 31 .4(∆-25.5) Gender non-binary male 27.4(∆-57.2) 0 .0(∆−8.4) 22 .0(∆−0.8) 26 .7(∆-30.2) Race asian ... | https://arxiv.org/abs/2505.16467v1 |
0 .0(∆0.0) 0 .0(∆0.0) 0 .0(∆0.0) Race black asian 65.9(∆+65.9 ) 63 .6(∆+63.6 ) 0.0(∆0.0) 0 .0(∆0.0) Race black hispanic 74.2(∆+74.2 ) 59 .2(∆+59.2 ) 0.0(∆0.0) 1 .8(∆+1.8) Race black white 3.0(∆ + 2 .6) 0 .4(∆ + 0 .4) 0 .0(∆0.0) 0 .0(∆0.0) Race hispanic asian 61.9(∆+61.8 ) 90 .8(∆+90.8 ) 0.0(∆0.0) 0 .0(∆0.0) Race hispan... | https://arxiv.org/abs/2505.16467v1 |
79 .5(∆-10.7) Race white black 61.8(∆−3.0) 100 .0(∆0.0) 100 .0(∆0.0) 79 .9(∆-10.3) Race white hispanic 45.1(∆-19.6) 100 .0(∆0.0) 100 .0(∆0.0) 67 .4(∆-22.8) SES high low 77.0(∆-21.0) 89 .6(∆−10.0) 100 .0(∆ + 2 .0) 5 .8(∆-13.6) SES low high 94.0(∆-6.0) 100 .0(∆0.0) 88 .4(∆-8.4) 14 .2(∆-8.5) Table 15: Results for Llama fo... | https://arxiv.org/abs/2505.16467v1 |
conversations. Reported results are for the group corre- sponding to the Stereotypes in the conversation. Results are from round 6, which is the end of the conversation. In brackets we report the difference with respect to non-stereotypical conversations with the same explicit demographic information, differences in bo... | https://arxiv.org/abs/2505.16467v1 |
from round 6, which is the end of the conversation. In brackets we report the difference with respect to non-stereotypical conversations with the same explicit demographic information, differences in bold are statistically significant with p <0.01. 25 Attribute Explicit Stereotypes Probe Surprisal Direct question Indir... | https://arxiv.org/abs/2505.16467v1 |
differences in bold are statistically significant with p <0.01. 26 Attribute Group Surprisal Direct question Indirect questions Age adult 0.0(∆0.0) 0 .0(∆0.0) 7 .5(∆−2.4) Age child 96.0(∆0.0) 0 .0(∆0.0) 1 .8(∆+1.5) Age older adult 99.2(∆ + 0 .4) 0 .0(∆0.0) 0 .4(∆ + 0 .3) Age teenager 100.0(∆0.0) 0 .0(∆0.0) 0 .4(∆−0.3) ... | https://arxiv.org/abs/2505.16467v1 |
of the conversation. In brackets we report the difference with respect to stereotypical conversations without steering, differences in bold are statistically significant with p <0.01. N Response 0 The scent of sawdust and varnish hung in the air, a familiar and comforting aroma to Elias. Sunlight streamed through the o... | https://arxiv.org/abs/2505.16467v1 |
"working son tells him he needs to get a real job if he ever wants to be a successful son son tells him he will be a successful son if he ever tells him he needs to get a son tells him he will be a successful son if he ever tells him he need to get a son tells him he will be a tells him he need to get a son tells him h... | https://arxiv.org/abs/2505.16467v1 |
.8) 0 .0(∆0.0) 0 .0(∆0.0) SES low high 100.0(∆ + 7 .6) 3 .6(∆ + 2 .4) 0 .2(∆ + 0 .2) Table 23: Results for Gemma for explicit+stereotype-clash conversations, with steering applied towards the explicitly mentioned group. Reported results are for the group corresponding to the Explicit demographic content. Results are fr... | https://arxiv.org/abs/2505.16467v1 |
1 .6) 82 .4(∆−4.4) 65 .4(∆+7.6) Age older adult child 100.0(∆ + 2 .0) 83 .2(∆−7.2) 66 .8(∆+8.4) Age older adult teenager 100.0(∆ + 8 .4) 83 .6(∆−4.0) 67 .0(∆+8.5) Age teenager adult 100.0(∆0.0) 100 .0(∆0.0) 77 .7(∆-6.7) Age teenager child 100.0(∆ + 2 .4) 100 .0(∆0.0) 77 .4(∆-8.2) Age teenager older adult 100.0(∆0.0) 10... | https://arxiv.org/abs/2505.16467v1 |
.0(∆0.0) 0 .0(∆-2.9) Race black white 0.0(∆−0.8) 0 .0(∆0.0) 0 .0(∆0.0) Race hispanic asian 0.0(∆0.0) 0 .0(∆0.0) 0 .0(∆0.0) Race hispanic black 0.0(∆0.0) 0 .0(∆0.0) 0 .0(∆0.0) Race hispanic white 0.4(∆−7.6) 0 .0(∆0.0) 0 .0(∆0.0) Race white asian 0.0(∆0.0) 0 .0(∆0.0) 5 .1(∆−1.5) Race white black 0.0(∆0.0) 0 .0(∆0.0) 0 .0... | https://arxiv.org/abs/2505.16467v1 |
27: Results for OLMo for explicit+stereotype-clash conversations, with steering applied towards the explicitly mentioned group. Reported results are for the group corresponding to the Explicit demographic content. Results are from round 6, which is the end of the conversation. In brackets we report the difference with ... | https://arxiv.org/abs/2505.16467v1 |
Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering Kuicai Dong∗Yujing Chang∗Shijie Huang Yasheng Wang Ruiming Tang Yong Liu HUAWEI NOAH’SARKLAB correspond to {kuicai.dong, liu.yong6}@huawei.com Evidence Page: "text1": [ "All of the respondents are used .................................. | https://arxiv.org/abs/2505.16470v1 |
technical manuals, and medical records) present significant challenges for DocVQA: (i) they are typically lengthy, complicating the identification of key evidence, and (ii) they require complex reasoning across various modalities, including images, ∗These authors contributed equally to this work. Preprint. Under review... | https://arxiv.org/abs/2505.16470v1 |
layout. In addition to these annotations, MMDocRAG introduces two novel evaluation features: (1) Quote Selection : We propose a practical evaluation metric that measures a model’s ability to select and integrate relevant multimodal quotes. To increase task difficulty, we include hard text and image negatives2mixed with... | https://arxiv.org/abs/2505.16470v1 |
multimodal integration, while targeted fine-tuning can significantly improve model performance on these tasks. 2MMDocRAG Benchmark As exemplified in Figure 1, MMDocRAG contains annotations: QA pair, page and quote evidence, noisy quotes, and multimodal answer. The construction pipeline and statistics are in Figure 2 an... | https://arxiv.org/abs/2505.16470v1 |
Quotes (Text/Image) 48,618 / 32,071 - Gold Quotes (Text/Image) 4,640 / 6,349 - Noisy Quotes (Text/Image) 43,978 / 25,722 Avg./Med./Max words: question 21.9 / 20 / 73 Avg./Med./Max words: short ans 23.9 / 22 / 102 Avg./Med./Max words: multimodal ans 221.0 / 203 / 768 Avg./Med./Max number of gold quotes 2.7 / 2 / 12 Tabl... | https://arxiv.org/abs/2505.16470v1 |
multi-image questions (involving 2+ image quotes), and 2,503 cross-modal questions (requiring multiple evidence modalities). All questions are categorized into one of eight predefined types. Regarding quotes , the dataset includes 48,618 text quotes (of which 4,640 are gold) and 32,071 image quotes (with 6,349 gold quo... | https://arxiv.org/abs/2505.16470v1 |
isolate the evaluation of LLM/VLM quote selection and answer generation capabilities. Specifically, we consider two settings: using 15 or 20 candidate quotes as context, denoted as C15andC20, respectively. C15={t1, . . . , t 10, i1, . . . , i 5}consists of 10 text quotes from Tand 5 image quotes fromI.C20={t1, . . . , ... | https://arxiv.org/abs/2505.16470v1 |
404 77.8 46.2 58.0 34.8 32.8 33.8 48.1 0.158 0.300 4.11 2.91 3.33 3.24 3.12 3.34 Qwen2.5-14B-Inst 2.7k 356 77.6 61.9 68.9 39.1 48.6 43.4 59.6 0.151 0.298 4.28 3.13 3.47 3.33 3.29 3.50 -After Fine-tuning 2.7k 296 76.8 73.4 75.1 55.9 7.2 12.7 61.5 0.217 0.370 4.70 3.70 4.02 3.69 3.38 3.90 Qwen3-14B (think) 2.7k 891 77.8 ... | https://arxiv.org/abs/2505.16470v1 |
67.7 72.2 69.9 32.5 68.5 44.1 56.0 0.132 0.274 3.85 2.74 3.22 3.00 3.15 3.19 Gemini-2.0-Flash-Think 2.8k 270 76.5 73.3 74.9 38.8 62.3 47.8 62.2 0.132 0.270 4.13 3.07 3.63 3.30 3.43 3.51 Gemini-2.5-Flash 2.7k 370 73.9 83.5 78.4 32.0 80.1 45.7 61.1 0.134 0.270 4.02 3.08 3.65 3.40 3.61 3.55 Gemini-2.5-Pro 2.7k 380 77.6 89... | https://arxiv.org/abs/2505.16470v1 |
3.52 3.36 3.25 3.46 Llama4-Scout-17Bx16E 7.8k 387 67.2 60.2 63.5 30.9 42.3 35.7 48.5 0.131 0.287 3.95 2.67 3.11 3.14 3.11 3.20 Llama4-Mave-17Bx128E 7.8k 325 72.1 80.0 75.8 43.9 36.4 39.8 61.9 0.154 0.309 4.22 3.30 3.62 3.52 3.58 3.65Proprietary ModelsQwen-VL-Plus 5.0k 243 61.8 22.5 33.0 27.4 26.0 26.7 27.2 0.101 0.278 ... | https://arxiv.org/abs/2505.16470v1 |
302 66.5 45.5 54.0 36.2 28.2 31.7 45.8 0.159 0.313 4.27 2.93 3.21 3.22 3.07 3.34 -After Fine-tuning 3.6k 223 71.2 66.8 69.0 38.5 2.6 4.9 56.0 0.199 0.353 4.59 3.38 3.70 3.36 2.98 3.60 Llama3.1-8B-Inst 3.4k 435 54.1 51.8 52.9 24.1 38.1 29.5 41.0 0.112 0.254 3.61 2.40 2.82 2.75 2.70 2.86 Qwen3-8B (think) 3.6k 1018 71.3 6... | https://arxiv.org/abs/2505.16470v1 |
3.6k 316 70.2 62.5 66.1 36.2 53.1 43.1 55.4 0.169 0.318 4.35 3.28 3.57 3.51 3.44 3.63 Qwen-Max 3.6k 426 71.7 66.9 69.3 39.7 51.5 44.8 58.9 0.165 0.315 4.42 3.47 3.71 3.64 3.59 3.77 Qwen-QwQ-Plus 3.6k 1266 67.4 66.1 66.7 35.7 62.6 45.5 59.6 0.126 0.284 4.17 3.29 3.63 3.54 3.51 3.63 Gemini-1.5-Pro 3.6k 290 66.8 72.9 69.7... | https://arxiv.org/abs/2505.16470v1 |
4.20 3.32 3.70 3.69 3.71 3.73 InternVL2.5-38B 17.1k 470 25.2 40.1 31.0 24.5 11.5 15.7 31.3 0.098 0.257 3.16 1.46 1.82 2.49 2.60 2.30 InternVL3-38B 17.1k 359 67.7 51.0 58.2 33.1 64.7 43.8 53.9 0.155 0.301 4.08 3.07 3.47 3.36 3.27 3.45 Qwen2.5-VL-72B-Inst 7.1k 320 68.9 72.1 70.5 36.0 52.9 42.8 57.5 0.151 0.298 4.15 3.08 ... | https://arxiv.org/abs/2505.16470v1 |
pure-text inputs. Gemini-1.5-Pro 3.8k 3.6k -5.3 59.3 56.2 -5.2 2.77 3.03 +9.4 Gemini-2.0-Pro 3.8k 3.6k -5.3 62.0 62.8 +1.3 3.31 3.51 +6.0 Gemini-2.0-Flash 3.8k 3.6k -5.3 60.0 54.4 -9.3 3.11 3.19 +2.6 Gemini-2.0-Flash-Think 3.8k 3.6k -5.3 66.2 61.0 -7.9 3.61 3.47 -3.9 Gemini-2.5-Pro 3.7k 3.6k -2.7 65.4 65.1 -0.5 3.88 3.... | https://arxiv.org/abs/2505.16470v1 |
and open-source models score between 3.0 and 3.6, primarily due to citation, reasoning, and factuality errors. •Multimodal vs Pure-text Quotes. Proprietary VLMs using multimodal inputs generally achieve better or comparable performance compared to pure-text inputs, albeit with significant compu- tational overhead and i... | https://arxiv.org/abs/2505.16470v1 |
Further qualitative analysis is provided in Appendix F.2. 4.5 Multimodal Quotes as text: VLM-text vs OCR-text We compare model performance using OCR-extracted text versus VLM-generated text, as shown in Table 6 (complete results in Table 11). Models utilizing VLM-text significantly outperform those using OCR-text in bo... | https://arxiv.org/abs/2505.16470v1 |
With the advancement of multimodal LLMs, newer approaches treat images as part of the next-token prediction within autoregressive frameworks. Methods such as [ 8,18,68,78] demonstrate end-to-end interleaved text-image generation via autoregressive training. However, these models mainly generate images from scratch, mak... | https://arxiv.org/abs/2505.16470v1 |
Kauffmann, Mahoud Khademi, Dongwoo Kim, Young Jin Kim, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Xihui Lin, Zeqi Lin, Ce Liu, Liyuan Liu, Mengchen Liu, Weishung Liu, Xiaodong Liu, Chong Luo, Piyush Madan, Ali Mahmoudzadeh, David Majercak, Matt Mazzola, Caio César Teodoro... | https://arxiv.org/abs/2505.16470v1 |
Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling, 2025. URL https://arxiv.org/abs/2412.05271 . 11 [8]Ethan Chern, Jiadi Su, Yan Ma, and Pengfei Liu. Anole: An open, autoregre... | https://arxiv.org/abs/2505.16470v1 |
Li, Ruiming Tang, and Yong Liu. Mmdocir: Benchmarking multi-modal retrieval for long documents, 2025. URL https://arxiv.org/ abs/2501.08828 . [17] Manuel Faysse, Hugues Sibille, Tony Wu, Bilel Omrani, Gautier Viaud, Céline Hudelot, and Pierre Colombo. Colpali: Efficient document retrieval with vision language models, 2... | https://arxiv.org/abs/2505.16470v1 |
Mach. Learn. Res. , 2022, 2022. URL https://openreview.net/forum?id= jKN1pXi7b0 . [29] Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre S... | https://arxiv.org/abs/2505.16470v1 |
Lifu Huang. Holistic evaluation for interleaved text-and-image generation. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 22002–22016, Miami, Florida, USA, Novem- ber 2024. Association for Computational Lingui... | https://arxiv.org/abs/2505.16470v1 |
deeply on the boundaries of the unknown, 2024. URL https: //qwenlm.github.io/blog/qwq-32b-preview/ . [56] Qwen-Team. Qwen3: Think deeper, act faster, 2025. URL https://qwenlm.github.io/ blog/qwen3/ . 15 [57] Qwen-Team. Qwen2.5-max: Exploring the intelligence of large-scale moe model, 2025. URL https://qwenlm.github.io/... | https://arxiv.org/abs/2505.16470v1 |
models, 2024. URL https://arxiv.org/abs/2405.09818 . 16 [69] Changyao Tian, Xizhou Zhu, Yuwen Xiong, Weiyun Wang, Zhe Chen, Wenhai Wang, Yuntao Chen, Lewei Lu, Tong Lu, Jie Zhou, Hongsheng Li, Yu Qiao, and Jifeng Dai. Mm-interleaved: Interleaved image-text generative modeling via multi-modal feature synchronizer, 2024.... | https://arxiv.org/abs/2505.16470v1 |
Lisboa, Portugal, October 10 - 14, 2022 , pages 4857–4866, Lisboa Portugal, 2022. ACM. doi: 10.1145/3503161.3548422. URL https://doi.org/10.1145/3503161.3548422 . 17 [82] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Xuehui ... | https://arxiv.org/abs/2505.16470v1 |
(see Section 4.1). A.1 Related Work of multimodal generation Multimodal generation, particularly interleaved image-text sequence generation, involves generating outputs that integrate visual and textual information in a cohesive manner (see Section 5). This capability facilitate applications such as storytelling, quest... | https://arxiv.org/abs/2505.16470v1 |
length mismatch. (ii) ROUGE-L (Recall-Oriented Understudy for Gisting Evaluation) focuses on the Longest Common Subsequence (LCS) between the generated and reference answers. ROUGE-L combines recall and precision using: ROUGE-L =(1 +β2)·RLCS·PLCS RLCS+β2PLCS, P LCS=LCS(Gen,Ref) |Gen|, RLCS=LCS(Gen,Ref) |Ref| (3) where ... | https://arxiv.org/abs/2505.16470v1 |
+22.1 3.36 3.77 +12.2 QVQ-Max QwQ-32B 4.7k 2.7k -42.6 25.8 52.0 +101.6 2.44 3.64 +49.2 Qwen2.5-VL-7B Qwen2.5-7B 5.0k 2.7k -46.0 23.0 48.4 +110.4 2.62 3.37 +28.6 Qwen2.5-VL-32B Qwen2.5-32B 4.9k 2.7k -44.9 39.8 63.0 +58.3 3.76 3.68 -2.1 Qwen2.5-VL-72B Qwen2.5-72B 5.0k 2.7k -46.0 60.0 62.9 +4.8 3.49 3.76 +7.7 Table 8: Usi... | https://arxiv.org/abs/2505.16470v1 |
Qwen3-30B-A3B 949 378 -60.2 64.8 58.6 -9.6 3.62 3.52 -2.820 QuotesQwen3-4B 1072 271 -74.7 58.2 51.1 -12.2 3.58 3.14 -12.3 Qwen3-8B 1018 337 -66.9 59.7 54.9 -8.0 3.51 3.29 -6.3 Qwen3-14B 920 352 -61.7 59.9 54.5 -9.0 3.65 3.57 -2.2 Qwen3-30B-A3B 969 401 -58.6 61.4 53.6 -12.7 3.60 3.52 -2.2 Qwen-QVQ-Max 1137 1129 -0.7 12.... | https://arxiv.org/abs/2505.16470v1 |
settings in which the model performs step-by-step reasoning before gen- erating a final answer [ 56], making it well-suited for complex tasks requiring deeper reasoning. In contrast, non-thinking mode directs the model to provide rapid, near-instant responses, which is preferable for simple questions where speed is pri... | https://arxiv.org/abs/2505.16470v1 |
3.63 3.54 3.75 -Using OCR-text 2.3k 228 70.9 69.6 70.2 38.6 66.3 48.8 59.5 0.150 0.316 4.49 3.23 3.70 3.56 3.44 3.68 Gemini-2.0-Pro 2.8k 307 75.7 79.2 77.4 38.5 64.4 48.2 63.5 0.161 0.302 4.13 3.05 3.56 3.31 3.45 3.50 -Using OCR-text 2.4k 270 71.5 78.6 74.9 38.3 63.9 47.9 62.0 0.146 0.292 4.08 2.85 3.44 3.33 3.37 3.41 ... | https://arxiv.org/abs/2505.16470v1 |
section 4.5, we analyze the performance difference by using OCR-text and VLM-text. The complete results (with more fine-grained scores breakdown) of quote selection and interleaved answer generation is illustrated in Figure 11. B.4 Fine-grained Results by Document Domains As illustrated in Figure 8, different models ex... | https://arxiv.org/abs/2505.16470v1 |
- ✓ OpenGVLab/InternVL2_5-8B InternVL2.5-26B [7] 26B - ✓ OpenGVLab/InternVL2_5-26B InternVL2.5-38B [7] 38B - ✓ OpenGVLab/InternVL2_5-38B InternVL2.5-78B [7] 78B - ✓ OpenGVLab/InternVL2_5-78B InternVL3-8B [82] 8B - ✓ OpenGVLab/InternVL3-8B InternVL3-9B [82] 9B - ✓ OpenGVLab/InternVL3-9B InternVL3-14B [82] 14B - ✓ OpenGV... | https://arxiv.org/abs/2505.16470v1 |
Details of LLM Finetuning As described in Section 4.2, we finetune five Qwen2.5 LLMs: Qwen2.5-3B-Instruct, Qwen2.5-7B- Instruct, Qwen2.5-14B-Instruct, Qwen2.5-32B-Instruct, and Qwen2.5-72B-Instruct. Data Preparation. Training is conducted on the MMDocRAG development set, comprising 2,055 questions, each annotated with ... | https://arxiv.org/abs/2505.16470v1 |
such as ColPali [ 17] and DSE [ 40] leverage PaliGemma [ 4] and Phi3-Vision [ 1] to directly encode document page screenshots for multimodal retrieval. ColPali utilizes fine-grained, token-level question-document interactions similar to ColBERT, while DSE adopts a global dense embedding approach as in DPR. Visual retri... | https://arxiv.org/abs/2505.16470v1 |
'Leveling the playing field on information Up sell opportunities/Of fers to .... ', 'page_id': 38, 'layout_id': 142}, {'quote_id': 'text3', 'type': 'text', 'text': 'Survey Respondents by Segment applied to average retailer sizes per ...', 'page_id': 39, 'layout_id': 145}, {'quote_id': 'text4', 'type': 'text', 'text': '... | https://arxiv.org/abs/2505.16470v1 |
with live DJsand40%more on music festivals. ', 'page_id': 22, 'layout_id': 66}, {'quote_id': 'text7', 'type': 'text', 'text': 'Overall,THE most tweeted about show since its premiere(Cable or Broadcast) ', 'page_id': 27, 'layout_id': 92}, {'quote_id': 'text8', 'type': 'text', 'text': 'ROCK IS THE BIGGEST GENRE,BUTR&B/HI... | https://arxiv.org/abs/2505.16470v1 |
the surge in internet users to 330 million, 45% of the Indian population were using debit cards. Figure 11: This example demonstrates a multimodal alignment task involving both numerical and categorical reasoning. The solution requires aligning and synthesizing temporal and quantitative information across multiple imag... | https://arxiv.org/abs/2505.16470v1 |
political affiliation, ethnicity, and gender, and performing numerical comparisons across multiple image quotes. 32 Multimodal answer:Question: Short answer: BERT+DSC achieves the highest F1 score across multiple datasets. BERT+DSC consistently achieves the highest F1 scores across various datasets, both in Chinese and... | https://arxiv.org/abs/2505.16470v1 |
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