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Lepri, Sara Tonelli, and Marco Guerini. Do LLMs suffer from multi-party hangover? a diagnostic approach to addressee recognition and response selection in conversations. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing ... | https://arxiv.org/abs/2505.17536v1 |
. IEEE, June 2018. [22] Chao-Yuan Wu and Philipp Krahenbuhl. Towards long-form video understanding. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, June 2021. [23] Md Mohaiminul Islam and Gedas Bertasius. Long movie clip classification with state-space video models. arXiv [cs.CV] ,... | https://arxiv.org/abs/2505.17536v1 |
Yu, Heidi Howard, Adam Bloniarz, Jack W Rae, Han Lu, Laurent Sifre, Marcello Maggioni, 11 Fred Alcober, Dan Garrette, Megan Barnes, Shantanu Thakoor, Jacob Austin, Gabriel Barth-Maron, William Wong, Rishabh Joshi, Rahma Chaabouni, Deeni Fatiha, Arun Ahuja, Gaurav Singh Tomar, Evan Senter, Martin Chadwick, Ilya Kornakov... | https://arxiv.org/abs/2505.17536v1 |
Elena Buchatskaya, Yingjie Miao, Mohamed Elhawaty, Aditya Siddhant, Nenad Tomasev, Jinwei Xing, Christina Greer, Helen Miller, Shereen Ashraf, Aurko Roy, Zizhao Zhang, Ada Ma, Angelos Filos, Milos Besta, Rory Blevins, Ted Klimenko, Chih-Kuan Yeh, Soravit Changpinyo, Jiaqi Mu, Oscar Chang, Mantas Pajarskas, Carrie Muir,... | https://arxiv.org/abs/2505.17536v1 |
Idan Heimlich Shtacher, Shachi Paul, Oscar Akerlund, François-Xavier Aubet, Terry Huang, Chen Zhu, Eric Zhu, Elico Teixeira, Matthew Fritze, Francesco Bertolini, Liana-Eleonora Marinescu, Martin Bölle, Dominik Paulus, Khyatti Gupta, Tejasi Latkar, Max Chang, Jason Sanders, Roopa Wilson, Xuewei Wu, Yi-Xuan Tan, Lam Nguy... | https://arxiv.org/abs/2505.17536v1 |
Izhak Shafran, Ivan Petrychenko, Zhe Chen, Johnson Jia, Anselm Levskaya, Zhenkai Zhu, Peter Grabowski, Yu Mao, Alberto Magni, Kaisheng Yao, Javier Snaider, Norman Casagrande, Evan Palmer, Paul Suganthan, Alfonso Castaño, Irene Giannoumis, Wooyeol Kim, Mikołaj Rybi ´nski, Ashwin Sreevatsa, Jennifer Prendki, David Soerge... | https://arxiv.org/abs/2505.17536v1 |
Rivière, Alanna Walton, Clément Crepy, Alicia Parrish, Zongwei Zhou, Clement Farabet, Carey Radebaugh, Praveen Srinivasan, Claudia van der Salm, Andreas Fidjeland, Salvatore Scellato, Eri Latorre-Chimoto, Hanna Klimczak-Pluci ´nska, David Bridson, Dario de Cesare, Tom Hudson, Piermaria Mendolicchio, Lexi Walker, Alex M... | https://arxiv.org/abs/2505.17536v1 |
Petrov, Jeffrey Dean, and Oriol Vinyals. Gemini: A family of highly capable multimodal models. arXiv [cs.CL] , December 2023. [31] OpenAI, Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, A J Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, Aleksander M ˛ adry, Alex Baker-Whitcomb, ... | https://arxiv.org/abs/2505.17536v1 |
McCallum, Lindsey Held, Long Ouyang, Louis Feuvrier, Lu Zhang, Lukas Kondraciuk, Lukasz Kaiser, Luke Hewitt, Luke Metz, Lyric Doshi, Mada Aflak, Maddie Simens, Madelaine Boyd, Madeleine Thompson, Marat Dukhan, Mark Chen, Mark Gray, Mark Hudnall, Marvin Zhang, Marwan Aljubeh, Mateusz Litwin, Matthew Zeng, Max Johnson, M... | https://arxiv.org/abs/2505.17536v1 |
, January 2025. [36] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. arXiv [eess.AS] , December 2022. [37] David Bamman, Rachael Samberg, Richard Jean So, and Naitian Zhou. Measuring diversity in hollywood through th... | https://arxiv.org/abs/2505.17536v1 |
Matt McVicar, Daniel Faronbi, Iran Roman, Matan Gover, Stefan Balke, Scott Seyfarth, Ayoub Malek, Colin Raffel, Vincent Lostanlen, Benjamin van Niekirk, Dana Lee, Frank Cwitkowitz, Frank Zalkow, Oriol Nieto, Dan Ellis, Jack Mason, Kyungyun Lee, Bea Steers, Emily Halvachs, Carl Thomé, Fabian Robert-Stöter, Rachel Bittne... | https://arxiv.org/abs/2505.17536v1 |
Singapore, December 2023. Association for Computational Linguistics. [59] Melanie Walsh, Anna Preus, and Maria Antoniak. Sonnet or not, bot? poetry evaluation for large models and datasets. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Findings of the Association for Computational Linguistics: EMNLP 20... | https://arxiv.org/abs/2505.17536v1 |
often mediated through non-verbal and non-linguistic cues (gaze, posture, etc.). If we follow Goffman and attempt to uncover the social architecture of interaction underlying conversational interactions, we can potentially shed light on the tacit rules and alignments that structure how people participate in talk; in th... | https://arxiv.org/abs/2505.17536v1 |
parent utterance is the immediately preceding line; if there is no logical antecedent utterance, the UOI is the beginning of a new thread (more below). Since we present each utterance at the sentence level, a special form of reply-to is that of a continuation : if the speaker is still in the midst of their turn, and th... | https://arxiv.org/abs/2505.17536v1 |
Table 6: Participant role matrix. Role addressed ratified known Addressee + + + Side-participants − + + Bystanders − − ± The first role is the speaker , the animator, the source of the utterance at time t. Following Goodwin’s conversational analysis [ 43] and Clark’s role taxonomy [ 44,45], we treat conversational role... | https://arxiv.org/abs/2505.17536v1 |
Your judgment should be entirely based on the clip and the clip alone. 21 Review/correct the speaker 2 Review/correct reply-to 3 Tag addressee(s) and side-participant(s) — move the badge from the list of participants abovez 4 Watch the video 1 use those features if helpfulFigure 2: Annotation interface for multimodal c... | https://arxiv.org/abs/2505.17536v1 |
“we” still invokes group reference, but the syntactic subject now reclaims authority over the seating decision. Here, we see Stephanie looking directly at, and speaking directly to, Sheldon, which casts Leonard as a side-participant. Leonard’s utterance (#3) is a reply to #2, seeing as it builds on Stephanie’s claim of... | https://arxiv.org/abs/2505.17536v1 |
television to watch a game show, which is then shown on screen with recognizable characters and dialogue. Annotators marked all game show dialogue as extra-diegetic and did not include the TV-watchers as addressees or side-participants. A.1.4 Annotation quality We report inter-annotator agreement in Table 7, using the ... | https://arxiv.org/abs/2505.17536v1 |
the same, you can treat it as ,→continuation and put the index of last line as the reply-to. * If no previous line triggers this line, then write the current line ,→index, indicating the current line replies to itself, which marks ,→the start of a new conversational thread. Here’s how to determine each role: * **Speake... | https://arxiv.org/abs/2505.17536v1 |
arXiv:2505.17537v1 [cs.CL] 23 May 2025How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception Shiyu Ni1,2,3Keping Bi1,2,3Jiafeng Guo1,2,3Xueqi Cheng1,2,3 1CAS Key Lab of Network Data Science and Technology, ICT, CAS 2State Key Laboratory of AI Safety 3University of Chinese Academy of Sciences... | https://arxiv.org/abs/2505.17537v1 |
from the following three perspectives: 1) Question popular- ityPopQ: popularity of the entity in the question. 2)Ground-truth answer popularity PopGT: popu- larity of the entity in the ground-truth answer. 3) Ground-truth relation popularity RPopGT: the co- occurrence frequency of the question and ground- truth entitie... | https://arxiv.org/abs/2505.17537v1 |
tion by an average of 5.24% across all models and datasets. Moreover, leveraging model-estimated popularity also performs well for confidence cali- bration. The choice between external corpora and self-estimation ultimately hinges on the trade-off between performance and efficiency. 2 Related Work Existing research on ... | https://arxiv.org/abs/2505.17537v1 |
expected to align with each other. 2) Models without special- ized training often struggle to verbalize confidence accurately (Ni et al., 2024b); consistency-based methods require multiple generations and incur high inference costs; and internal-state-based ap- proaches require access to hidden representations and addi... | https://arxiv.org/abs/2505.17537v1 |
LLMs’ QA performance, confidence, alignment and the correlations between knowledge popularity and accuracy, confidence, and alignment across different datasets. ground-truth answer popularity follows Movies < Songs < Basketball. Table 1 lists the knowledge triplets and data counts for each dataset, and Fig- ure 1 prese... | https://arxiv.org/abs/2505.17537v1 |
30 35 40 45 50 55 Question Popularity0.860.880.900.920.940.960.981.00Values ChatGPT on Movies Accuracy Confidence Alignment 5 10 15 20 25 Question Popularity0.550.600.650.700.750.800.850.900.951.00Values ChatGPT on Songs Accuracy Confidence Alignment 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 Question Popularity0.20.30... | https://arxiv.org/abs/2505.17537v1 |
the Spearman correlation coeffi- cients between LLMs’ QA performance and knowl- edge popularity based on model-generated entities. Due to space constraints, results on LLMs’ confi- dence and perception levels are provided in Table 7 in the Appendix. We observe the following. The popularity of generated entities ( PopGe... | https://arxiv.org/abs/2505.17537v1 |
reliance on external corpora and reduce the overhead of collecting popularity, we investigate whether LLMs can self-assess their familiarity with a given the entity or the relation. Familiarity is measured on a 10-point scale, where 1 denotes the lowest and 10 the highest level. The model is asked to provide its famili... | https://arxiv.org/abs/2505.17537v1 |
dimension. We use a 3-layer MLP with 64, 32, and 2 neurons in each layer, respec- tively. The activation function in MLP is ReLU. We employ cross-entropy loss as the training objective: LCE=−NX i=1yilog(Pi)+(1−yi) log(1 −Pi),(5) where yiis the ground-truth correctness for the i-th training sample, Nis the count of trai... | https://arxiv.org/abs/2505.17537v1 |
on model self- generated knowledge popularity under the zero- shot setting can be found in the lower half of Ta- ble 5. It show that: 1) All three types of self- generated popularity contribute to confidence cali- bration. On average, all three signals can calibrate PC, and their combination achieves the best calibra-t... | https://arxiv.org/abs/2505.17537v1 |
Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 . Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Wein- berger. 2017. On calibration of modern neural net- works. In International conference on machine learn- ing, pages 1321–1330. PMLR.Jan Hauke and Tom... | https://arxiv.org/abs/2505.17537v1 |
arXiv:2403.06448 . Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023. Just ask for cali- bration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. arXiv preprint arXiv:2305.14975 ... | https://arxiv.org/abs/2505.17537v1 |
NMI calculated based on ChatGPT. R-Pop means relation popularity, where P(R|Q)andP(R|A)denote the co-occurrence propor- tion of question and answer entities relative to their individual occurrences in documents. B Analysis on Relationship Strength We hypothesize that the strength of the relationship between entities ma... | https://arxiv.org/abs/2505.17537v1 |
cost rises. Therefore, we recommend prompt- ing LLMs to assess their familiarity with enti- ties and their relationships in a zero-shot setting. Due to API costs, we first conduct experiments on LLaMA3-8B-Instruct and Qwen2-7B-Instruct and find that increasing the number of samples in the prompt does not yield more eff... | https://arxiv.org/abs/2505.17537v1 |
868074 121416 2610 MoviesSongsBasketballSameWinFailFigure 8: The difference in answer correctness predic- tion on LLaMA3 between using PC+ALL and using PC. Blue indicates that both methods make the same prediction, yellow indicates cases where only PC+ALL predictes correctly, and red indicates cases where only PC predi... | https://arxiv.org/abs/2505.17537v1 |
of knowledge popularity in confidence calibration. F Prompts We display all the prompts used in this paper here and show some examples. QA prompt. We just ask the model to give a short answer without any other words. The exam- ple is shown in Figure 20. Prompts for knowledge popularity generation. Examples for instruct... | https://arxiv.org/abs/2505.17537v1 |
Llama3-8B on Movies Accuracy Confidence Alignment 20 40 60 80 100 120 140 160 Answer Popularity0.00.20.40.60.81.0Values Llama3-8B on Songs Accuracy Confidence Alignment 0 50 100 150 200 250 300 Answer Popularity0.00.20.40.60.81.0Values Llama3-8B on Basketball Accuracy Confidence Alignment Figure 17: The QA performance,... | https://arxiv.org/abs/2505.17537v1 |
10 means you are highly familiar with it, and 1 means you have little to no knowledge about it. Your answer needs to be a precise integer. Provide only the number, without any additional explanation.Here are some examples:The movie: Matchstick MenNumber: 2The movie: Kick-AssNumber: 5The movie: SkyfallNumber: 8Rate how ... | https://arxiv.org/abs/2505.17537v1 |
arXiv:2505.17538v1 [cs.CL] 23 May 2025Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition Leonora Vesterbacka, Faton Rekathati, Robin Kurtz, Justyna Sikora, Agnes Toftg ˚ard 1KBLab, National Library of Sweden, Sweden {leonora.vesterbackaolsson,faton.rekathati,robin.kurtz,justyna.sikora,a... | https://arxiv.org/abs/2505.17538v1 |
training data are unlocked. The authors show that even with imperfectly labeled training data, such as subtitles, it is still possible to achieve speech recognition performance approaching human-level robustness in English. Using the latest official Whisper models as a start- ing point, we fine-tune them on our herewit... | https://arxiv.org/abs/2505.17538v1 |
a means to aid accessibility for the hearing impaired. The Swedish subtitling tradition tends to condense the content rather than provide verbatim transcriptions, in order to provide the viewer enough time to read. In our filtering, we select only Swedish TV broadcasts and web content where metadata indicates the subti... | https://arxiv.org/abs/2505.17538v1 |
for developing a model which is representative of the whole population. 3.4. Other 3.4.1. NST The NST dataset, collected by Nordic Language Technology (Nordisk Spr ˚akteknologi), is hosted by Language Bank at the National Library of Norway3. It consists of hundreds of hours of recordings and transcriptions of up to 100... | https://arxiv.org/abs/2505.17538v1 |
(ROUGE-N) [11] is a recall-based metric that measures the overlap of n-grams between a model’s output and a reference text. • Approximate string matching is the technique of finding strings that match a pattern approximately (rather than ex- actly). This is used to match the first and last words in a chunk. We addition... | https://arxiv.org/abs/2505.17538v1 |
One hypothesis behind hallucinations is the lack of training examples with no speech and no corre- sponding transcriptions. To this end we include examples in our training data with no speech or transcription to help the model learn not to output any transcriptions when there is silence. The total amount of non-speech ... | https://arxiv.org/abs/2505.17538v1 |
We report test results as a comparison between OpenAI’s Whis- per and our models evaluated using the metrics word error rate (WER) and BLEU scores. The splits of three hold-out datasets which have not been employed for validation purposes during training are used: namely FLEURS (train and test set), NST (test set), and... | https://arxiv.org/abs/2505.17538v1 |
as training data with rare dialects. So data that had otherwise been filtered out due to a bad model performance, could in fact be included in the training data now, and further widen the range of quality in the training data. With the same reasoning, the use of a better Wav2Vec2.0 model than the V OXREXused in the dat... | https://arxiv.org/abs/2505.17538v1 |
T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics , P. Isabelle, E. Charniak, and D. Lin, Eds. Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, Jul. 2002, pp. 311... | https://arxiv.org/abs/2505.17538v1 |
arXiv:2505.17553v1 [cs.LG] 23 May 2025CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning Jinyuan Feng12∗Chaopeng Wei3∗Tenghai Qiu2†Tianyi Hu12Zhiqiang Pu12 1Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of ... | https://arxiv.org/abs/2505.17553v1 |
more experts does not linearly improve performance; instead, it leads to a performance bottleneck. Existing studies propose load balance loss (Li et al., 2024) and localized bal- ancing constraint (Dou et al., 2023) to alleviate the mentioned issues, but that is still far from enough. In this paper, we propose a novel ... | https://arxiv.org/abs/2505.17553v1 |
which sparsely activates a subset of the ex- perts. Specifically, only the top kexperts with the highest values in g(x;G)are activated. Then, g(x;G)is renormalized for the activated experts. The renormalization is computed as follows: ˆgi(x) =(gi(x)P j∈top(g(x),k)gj(x)ifi∈top(g(x), k) 0 ifi /∈top(g(x), k), (3) where to... | https://arxiv.org/abs/2505.17553v1 |
knowledge minimizes the utiliza- tion of capacity, which exacerbates performance degradation in heterogeneous tasks. Existing MoE variants (Li et al., 2024; Liu et al., 2023; Luo et al., 2024) leverage balance loss to promote specializa- tion among the experts, but fall far short. Ideally, experts should exhibit modula... | https://arxiv.org/abs/2505.17553v1 |
defined by: ∆I=Itop-k(x, M+)−I¬top-k(x, M−),(7) where Itop-kis the MI between input token xand ac- tivated experts M+, and I¬top-kis the MI between input token xand inactivated experts M−. The MI terms Itop-k andI¬top-k can be converted into a similar form as Eq. 5. To maximize the MI gap∆I, it is necessary to maximize... | https://arxiv.org/abs/2505.17553v1 |
representations. Here, τis a tempera- ture hyperparameter. In optimization, the score function assigns high scores to the representations of activated experts and low scores to inactivated experts. The de- rived InfoNCE loss in Eq. 10 can be generalized as a common contrastive loss: for each query qi=E+ i(x), its posit... | https://arxiv.org/abs/2505.17553v1 |
activated dur- ing inference. Based on our analysis and deriva- tions in Sections 4.2 and 4.3, we incorporate the contrastive loss as an auxiliary optimization ob- jective, with the complete workflow illustrated in Fig. 2. The contrastive loss effectively enhances the distinctiveness of experts, thereby promoting speci... | https://arxiv.org/abs/2505.17553v1 |
applied to the q,k,v, andoparameters in the attention layers. For all settings, we adopt su- pervised fine-tuning only. Due to space limitations, the detailed experimental settings for baselines and hyperparameters are provided in Appendix D. 5.2 Main Results Multi-task Setup. Table 1 summarizes the multi- task perform... | https://arxiv.org/abs/2505.17553v1 |
2 Expert 3 Expert 4 (b) w/ contrastive loss Figure 5: Comparison of expert representations in obqa before and after contrastive loss incorporation in a multi- task setting. (a) Without contrastive loss. (b) With contrastive loss. MoE benefits from more experts. Ablation on Different Backbones. We conduct multi-task exp... | https://arxiv.org/abs/2505.17553v1 |
define a mutual information (MI) gap between ac- tivated and inactivated experts and approximate it through a contrastive objective. This objective ef- fectively captures the MI gap and is incorporated into supervised fine-tuning as an auxiliary optimiza- tion term. Experiments on various tasks demon- strate that CoMoE... | https://arxiv.org/abs/2505.17553v1 |
2022. Sparse structure search for parameter-efficient tuning. arXiv preprint arXiv:2206.07382 . Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee- Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, and Roy Lee. 2023. Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. In Proceedings... | https://arxiv.org/abs/2505.17553v1 |
, 64(9):99–106. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. 2019. Socialiqa: Com- monsense reasoning about social interactions. arXiv preprint arXiv:1904.09728 . Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large n... | https://arxiv.org/abs/2505.17553v1 |
+Np(e+) p(e+|x)(1 N)X e−∈D¬top-kp(e−|x) p(e−) ≥Ep(x,e+)log Np(e+) p(e+|x)(1 N)X e−∈D¬top-kp(e−|x) p(e−) =Ep(x,e+)log (1 N)X e−∈D¬top-kNp(e+) p(e+|x)p(e−|x) p(e−) (15) We derive Eq. 15 using Jensen’s inequality, noting that logis a concave function. LNCE≥Ep(x,e+) (1 N)X e−∈D¬top-klog Np(e+) p(e+|x)p(e−|x)... | https://arxiv.org/abs/2505.17553v1 |
on Other Baselines In the main paper, we compared CoMoE with three widely recognized and well-performing base- lines (LoRA, DoRA, and MixLoRA) using the Datasets #train #test Type Metrics BoolQ 9,427 3,270 Text Classification acc OBQA 4,957 500 Question Answering acc ARC-e 2,251 2,376 Question Answering acc ARC-c 1,119... | https://arxiv.org/abs/2505.17553v1 |
22.4 24.0 62.2 27.6 34.1 CoMoE-LoRA 25.7 23.7 62.2 25.2 34.2 Table 7: Comparison of MixLoRA and CoMoE in multi-task learning. The backbone model is Gemma 2B. Method Latency (ms) Memory (MiB) Training time (h) LoRA 2,096 +1,630 1.8h DoRA 1,748 +2,184 1.7h MixLoRA 4,217 +1,776 2.2h OMoE(Top-2) 4,863 +1,776 2.3h CoMoE 3,7... | https://arxiv.org/abs/2505.17553v1 |
arXiv:2505.17558v1 [cs.CL] 23 May 2025Teaching with Lies: Curriculum DPO on Synthetic Negatives for Hallucination Detection Shrey Pandit*†, Ashwin Vinod† Liu Leqi ,Ying Ding /g♀beWebpage: https://teachingwithlies.github.io/ The University of Texas at Austin Abstract Aligning large language models (LLMs) to ac- curately... | https://arxiv.org/abs/2505.17558v1 |
ranked by difficulty. In each batch, gold references (chosen) and top -ranked hallucinations (rejected) form preference pairs. These pairs optimize the DPO objective, ensuring training against vetted, high-quality negatives rather than arbitrary failures. Our contributions are summarized as follows: 1.We introduce a no... | https://arxiv.org/abs/2505.17558v1 |
provides carefully crafted adversarial an- swers that are ideal for our alignment approach. For the purpose of this work we choose MedHallu and HaluEval for the DPO alignment, as they have high quality hallucinated samples. Our proposed method is agnostic of task, and can be extended to other hallucination detection ta... | https://arxiv.org/abs/2505.17558v1 |
with curated hallucinated answers as negatives achieves superior performance. Finally, in Sec. A.1 and A.2, we conduct ablations demonstrating HaluCheck’s superior transferable skills when trained on in- dividual datasets, and highlight the benefits of curriculum-based sampling over random selection. 5.1 HaluCheck vs B... | https://arxiv.org/abs/2505.17558v1 |
failures with carefully curated, difficulty-ranked hallucinated samples as negative preferences during DPO alignment. This structured curriculum yields consistent gains, out- performing larger state-of-the-art models on mul- tiple benchmarks and zero-shot tasks. Ablation results further validate that difficulty-aware n... | https://arxiv.org/abs/2505.17558v1 |
Association for Computational Lin- guistics. Jeffrey L Elman. 1993a. Learning and development in neural networks: The importance of starting small. Cognition , 48(1):71–99. Jeffrey L Elman. 1993b. Learning and development in neural networks: The importance of starting small. Cognition , 48(1):71–99. Edward J Hu, Yelong... | https://arxiv.org/abs/2505.17558v1 |
on curriculum learning for question answering. In Proceedings of the 54th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 453–463.Thibault Sellam, Dipanjan Das, and Ankur P. Parikh. 2020. Bleurt: Learning robust metrics for text gener- ation. Preprint , arXiv:2004.04696... | https://arxiv.org/abs/2505.17558v1 |
πθ, frozen ref. policy πref, stages S Ensure: Fine-tuned detector πθ 1:# Score difficulty 2:foreach(x,ytrue,yhall, l)do 3: pl← F (yl|x) 4:end for 5:# Partition into stages 6: sort by pl(asc.) and split into {Bs}S s=1 7:# Generate preference pairs 8:fori= 1, . . . , N do 9: y(i) w←y(i) true 10: y(i) l←y(i) hall 11: stor... | https://arxiv.org/abs/2505.17558v1 |
al. (2022): rank=8, α=32, dropout=0.05, and target modules q_proj ,k_proj ,v_proj , and o_proj . Training data comprise 9 000 examples from MedHallu’s pqa_artificial split plus 8 000 items (80 %) from the HaluEval training partition, forming 17 000 DPO preference pairs. Evaluation is conducted on the 1 000-example MedH... | https://arxiv.org/abs/2505.17558v1 |
0.622 0.494 0.491 HaluCheck 3B 0.729 0.845 0.782 Llama-3.2 3B-SN 0.691 0.772 0.717 Table 7: Hallucination detection on the MedHallu dataset . “SN” models were aligned with standard neg- ative samples in DPO, while HaluCheck models were aligned with curated hallucinated negatives. Higher is better on all metrics. traini... | https://arxiv.org/abs/2505.17558v1 |
PPT: A Process-based Preference Learning Framework for Self Improving Table Question Answering Models Wei Zhou1,3Mohsen Mesgar1Heike Adel2Annemarie Friedrich3 1Bosch Center for Artificial Intelligence, Renningen, Germany 2Hochschule der Medien, Stuttgart, Germany3University of Augsburg, Germany {wei.zhou|mohsen.mesgar}... | https://arxiv.org/abs/2505.17565v1 |
roll out parent states to obtain paired child states and select state pairs with value differences larger than a specified thresh- old. After obtaining preference data, we perform process-based preference learning using direct pref- erence optimization (DPO) (Rafailov et al., 2024) to self-improve the TQA model. Experi... | https://arxiv.org/abs/2505.17565v1 |
accumulate steps to construct states: si={s0, k1, ...k i}. If all reasoning traces lead to correct answers, we abandon the problem as it might be too easy and only lead to high value states. State Value Estimation. A state value function Vtakes in a state and returns its value. We ap- proximate a state’s value by Monte... | https://arxiv.org/abs/2505.17565v1 |
accounts for approximately 36% of data (Figure 5). Baselines forTQA models include both end-to-end (Zhang et al., 2023a; Wu and Feng, 2024) and training-free frameworks (Zhou et al., 2025; Nahid and Rafiei, 2024). Details of the base methods and models can be found in Appendix A.4. Datasets. We train Mftusing the train... | https://arxiv.org/abs/2505.17565v1 |
3 shows, applying PPT en- hances the performance of Mftby 3.5% on average on in-domain datasets. The gains are more obvious on WTQ (5%) compared to TabFact (2%). This might be attributed to dataset features: TabFact is a binary classification dataset, thus it is easier for models to achieve high performance and harder ... | https://arxiv.org/abs/2505.17565v1 |
emphasize the inference efficiency of Msi: it requires eight times less inference time than MACT and five times less than TabSQLify, yet provides competitive results. 6 Conclusions In this study, we provided the first self- improvement framework for TQA, using process- based preference learning. Our framework effec- ti... | https://arxiv.org/abs/2505.17565v1 |
Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wanjia Zhao, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaokang Zhang, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxia... | https://arxiv.org/abs/2505.17565v1 |
Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kam- badur, Mike Lewis, Min Si, Mitesh Kumar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Niko- lay Bashlykov, Nikolay Bogoychev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Va- sic,... | https://arxiv.org/abs/2505.17565v1 |
Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe Cummings, Jon Carvill, Jon Shepard, Jonathan Mc- Phie, Jonathan Torres, Josh Ginsburg, Junjie Wang, Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khan- delwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan, Kelly Michelena, Keqian Li,... | https://arxiv.org/abs/2505.17565v1 |
Ilya Sutskever, and Karl Cobbe. 2023. Let’s verify step by step. Xinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov, and Min-Yen Kan. 2023. SCITAB: A challenging benchmark for compositional reasoning and claim verification on scientific tables. In Proceedings of the 2023 Conference on Empirical Methods in Natural Langu... | https://arxiv.org/abs/2505.17565v1 |
Miami, Florida, USA. Association for Computational Linguistics. Weimin Xiong, Yifan Song, Xiutian Zhao, Wenhao Wu, Xun Wang, Ke Wang, Cheng Li, Wei Peng, and Sujian Li. 2024. Watch every step! LLM agent learning via iterative step-level process refinement. InProceedings of the 2024 Conference on Empiri- cal Methods in ... | https://arxiv.org/abs/2505.17565v1 |
Appendix A.1 Prompts Figure 6, 7 and 8 show prompts for generating a full reasoning trace, completing a reasoning trace and LLM judge evaluation for a reasoning trace. A.2 Datasets Table 2 shows number of instances and domains for the test data we used. A.3 Fine-tune TQA Models Following previous work (Wu and Feng, 202... | https://arxiv.org/abs/2505.17565v1 |
to LLMs to obtain final answers. A.5 Sampled Dataset Statistics Table 3 shows sampling size for each method. We find MC-Bresults in the most data while RFT the least. A.6 Hyper-parameters Table 4 shows the hyper-parameters used for model fine-tuning. A.7 Additional Results Table 5 shows different models’ performance on... | https://arxiv.org/abs/2505.17565v1 |
68.15 52.29 46.15 66.33 55.53 +FDPO 61.10 84.65 63.83 72.04 56.94 52.06 69.86 60.34 PPT(MC) 60.80 82.98 65.72 70.08 52.04 50.24 69.83 57.45 PPT(MIX) 63.86 85.32 64.20 71.44 55.56 52.47 71.63 59.82 PPT(τ= 0.9) 63.10 84.88 67.55 71.97 56.94 51.37 71.84 60.09 LlaMA-3.1-8B 30.64 63.91 26.20 31.87 43.38 32.55 40.25 35.93 + ... | https://arxiv.org/abs/2505.17565v1 |
for each decision (from 0 to 1). Higher confidence value suggests you are more certain that your decision is correct. In contrast, lower confidence suggests you are more uncertain about your decision and your decision might be incorrect. Please do not be over confident about your decision. You should be honest if you a... | https://arxiv.org/abs/2505.17565v1 |
arXiv:2505.17571v1 [cs.CL] 23 May 2025Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation Sichun Luo1,2Guanzhi Deng2Jian Xu3 Xiaojie Zhang4Hanxu Hou1*Linqi Song2∗ 1Dongguan University of Technology2City University of Hong Kong 3Tsinghua University4Guangzhou Uni... | https://arxiv.org/abs/2505.17571v1 |
in convergent reasoning for well-defined problems like equation solving but lack the divergent thinking needed to capture nuanced user preferences, often yielding sub-optimal outputs. Second, they struggle to produce consistent, structured responses aligned with required formats. Finally, LRMs inefficiently leverage re... | https://arxiv.org/abs/2505.17571v1 |
R-L↑ 0.083 0.131 0.108 0.125 0.110 0.133 0.157 0.148 LaMP-5 R-1 ↑ 0.114 0.381 0.130 0.389 0.148 0.406 0.304 0.413 R-L↑ 0.106 0.308 0.114 0.314 0.132 0.341 0.272 0.344 LaMP-7 R-1 ↑ 0.351 0.426 0.379 0.318 0.370 0.338 0.415 0.415 R-L↑ 0.291 0.370 0.325 0.272 0.320 0.293 0.362 0.362 Table 2: Average token length compariso... | https://arxiv.org/abs/2505.17571v1 |
0.378 LaMP-4 R-1 ↑ 0.153 0.137 0.151 0.136 0.129 0.147 0.162 0.157 R-L↑ 0.134 0.126 0.132 0.117 0.115 0.131 0.142 0.138 LaMP-5 R-1 ↑ 0.455 0.444 0.395 0.304 0.363 0.414 0.444 0.435 R-L↑ 0.385 0.374 0.329 0.264 0.302 0.344 0.376 0.364 LaMP-7 R-1 ↑ 0.381 0.461 0.460 0.415 0.317 0.350 0.406 0.447 R-L↑ 0.332 0.408 0.398 0.... | https://arxiv.org/abs/2505.17571v1 |
LaMP-4 16.9 38.3 26.8 42.5 448.0 446.7 412.8 386.9 LaMP-5 19.9 22.8 35.5 49.2 477.1 381.4 410.8 376.1 LaMP-7 24.8 24.6 25.5 27.0 394.9 402.8 432.0 377.6 Avg. 10.2 13.4 14.3 18.3 417.3 436.3 369.3 349.9 Table 6: Average token length comparison of Qwen and DeepSeek-Qwen on LaMP Tasks with RAG (k= 4). Task Qwen1.5 Qwen7 Q... | https://arxiv.org/abs/2505.17571v1 |
Comparison between Model Scaling To investigate the impact of model size on personalization performance, we evaluate Qwen2.5- Instruct series models [ 35] (with 1.5B, 7B, 14B, 32B parameters) and their DeepSeek-Distill-Qwen counterparts [ 6], which are optimized for reasoning, on LaMP tasks under RAG settings ( k= 1and... | https://arxiv.org/abs/2505.17571v1 |
alignment with task requirements. To solve this, we propose a hierarchical reasoning thought template, which offers a structured approach to guide LRMs in personalization tasks. By providing a clear framework, it ensures critical RAG information is utilized effectively, enhancing the focus and consistency of outputs. T... | https://arxiv.org/abs/2505.17571v1 |
Acc↑ F1↑ MAE↓RMSE ↓ R-1↑ R-L↑R-1↑R-L↑ k= 1RAG 0.650 0.649 0.698 0.421 0.332 0.257 0.383 0.678 0.152 0.133 0.406 0.341 PAG 0.648 0.648 0.715 0.398 0.298 0.227 0.395 0.717 0.143 0.125 0.300 0.253 Self-Verification 0.613 0.611 0.713 0.425 0.307 0.209 0.432 0.746 0.117 0.103 0.249 0.210 R2P 0.673* 0.673 0.697 0.394 0.282 0... | https://arxiv.org/abs/2505.17571v1 |
5: Mean token lengths of generated outputs across four LaMP tasks under different configura- tions. generating structured output. Moreover, removing the reasoning process intervention can degrade model performance. This may be because the model does not align well with the instructions and needs intervention to better ... | https://arxiv.org/abs/2505.17571v1 |
Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403 , 2023. [3]T. Araujo and N. Bol. From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents. Computers in Human Behavior: Artif... | https://arxiv.org/abs/2505.17571v1 |
knowledge-intensive nlp tasks. Advances in neural information processing systems , 33:9459–9474, 2020. [18] C.-Y . Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out , pages 74–81, 2004. [19] F. Liu, Y . Liu, L. Shi, H. Huang, R. Wang, Z. Yang, L. Zhang, Z. Li, and Y . Ma. E... | https://arxiv.org/abs/2505.17571v1 |
11 [33] Y . Weng, M. Zhu, F. Xia, B. Li, S. He, S. Liu, B. Sun, K. Liu, and J. Zhao. Large language models are better reasoners with self-verification. In Findings of the Association for Computational Linguistics: EMNLP 2023 , pages 2550–2575, 2023. [34] F. Xu, Q. Hao, Z. Zong, J. Wang, Y . Zhang, J. Wang, X. Lan, J. G... | https://arxiv.org/abs/2505.17571v1 |
output meets requirements: 1. Question Analysis: Carefully analyze the user input to clarify the intent and specific needs of the query. Extract key information such as task type (e.g., judgment, generation, summary) and constraints (e.g., format, scope). 2. User Profile Integration: Use the background infor- mation pr... | https://arxiv.org/abs/2505.17571v1 |
categories: the rational and the aggressive, and find that rational users intend to develop close and reciprocated relationship, whereas aggressive users have no consistent behaviors. Third, we propose a simple model to highlight the constraints of time and cognition that may affect the evolution of OSNs heavily. Final... | https://arxiv.org/abs/2505.17571v1 |
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