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fact-checking articles during training. 8 Limitations We made considerable efforts to prevent temporal leakage. Specifically, we provided annotators with custom search bars to retrieve online evidence published prior to the claim date. However, the exact dates of a small portion of claims ( 5%) were unavailable. In suc... | https://arxiv.org/abs/2505.17978v1 |
Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Ming-Hsuan Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang L... | https://arxiv.org/abs/2505.17978v1 |
Mesnard, Geoffrey Cideron, Jean-Bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penchev, Gaël Liu, Francesco Visin, Kathleen Kenealy, Lucas Beyer, Xiaohai Zhai, Anton Tsitsulin, Róbert Busa-Fekete, Alex Feng, Noveen Sachdeva, Benjamin Coleman, Yi Gao, Basil Mustafa, Iain Barr, Emilio Pari... | https://arxiv.org/abs/2505.17978v1 |
Yang Wang. Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. In Proceedings of The 12th Language Resources and Evaluation Conference, LREC , pages 6149–6157, 2020. Preslav Nakov, David Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar,... | https://arxiv.org/abs/2505.17978v1 |
NeurIPS , volume 36, pages 65128–65167, 2023. Rui Shao, Tianxing Wu, and Ziwei Liu. Detecting and grounding multi-modal media manipulation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR , pages 6904–6913, 2023. Jieun Shin and Kjerstin Thorson. Partisan selective sharing: The biased diffusion o... | https://arxiv.org/abs/2505.17978v1 |
Nakov, and Ivan Koychev. Fact-checking meets fauxtography: Verifying claims about images. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP , pages 2099–2108, 2019. 13 A Statement of Dataset W... | https://arxiv.org/abs/2505.17978v1 |
identified 91 duplicate claims (e.g., those verified by multiple fact-checking organizations). Duplicates were detected automatically by computing cosine similarity and Jaccard distance between N-gram representations of textual part of claims. We manually reviewed claim pairs with a cosine similarity or Jaccard distanc... | https://arxiv.org/abs/2505.17978v1 |
most relevant locations Table 7: Counts of locations associated with claims. Countries are represented with their ISO country code. We do not show countries with fewer than ten occurrences in the table, whereas the complete location information is available in our dataset. Country code Counts IN 417 US 309 GB 74 UA 37 ... | https://arxiv.org/abs/2505.17978v1 |
3) how many QA pairs from the re-annotation are covered by the original QA pairs. The platform used for this agreement check is shown in Figure 3. Justification Evaluation. Although we primarily focused on verdict and QA annotation agreement, we also conducted a small-scale human evaluation to assess the quality of jus... | https://arxiv.org/abs/2505.17978v1 |
assess alignment, we treated the original annotations from our dataset as predictions and the re-annotations as ground truth. We then applied the automatic evaluation methods to compare these “predictions” against the ground truth. Subsequently, we computed alignment scores by comparing the recall obtained from the aut... | https://arxiv.org/abs/2505.17978v1 |
baselines. Q-Eval andEvid-Eval denote for recall scores of generated questions and retrieved evidence, with reference of ground-truth questions and evidence. We report verdict prediction and justification generation scores conditioned on evidence retrieval performance, specifically only considering verdict accuracy and... | https://arxiv.org/abs/2505.17978v1 |
returned web pages published before claim dates. We keep the first 30 search results. For RIS, we employed the google cloud vision service for detecting web pages containing matched images with the querying image. However, the service does not embed arguments to set temporal constraints. Alternatively, we use a post-ho... | https://arxiv.org/abs/2505.17978v1 |
with theQwen/Qwen2.5-7B-Instruct (Qwen), google/gemma-3-12b-it (Gemma), Qwen/Qwen2.5-VL-7B- Instruct (Qwen-VL) and llava-hf/llava-v1.6-mistral-7b-hf (LLaV A), respectively. The version of 8https://www.reddit.com/r/LocalLLaMA/comments/1c858ac/llama3_seems_to_get_stuck_in_loops_sometimes/ 21 Huggingface is 4.50.2. For Ge... | https://arxiv.org/abs/2505.17978v1 |
as a reference question, even if the wording differs.2. Evaluate each question in the reference question set individually: Check whether it is covered by any question in the predicted set ([PRED]), using the same criteria. Do not use additional sources or background knowledge.3. Finally summarise (1.) Count how many pr... | https://arxiv.org/abs/2505.17978v1 |
Phillip wore the Royal Guard uniform in Jan. 14, 2003. 4.The raid in Washington took place on Saturday, Oct. 26, 1999.[REF]: 1. [IMG_1] was taken in Jan. 20, 2003. 2. No evidence can be found related to the type of missle in [IMG_2]. 3. The woman in [IMG_3] for a training is not Ilan Omar. 4. No answer was found regard... | https://arxiv.org/abs/2505.17978v1 |
paragraph is replaced with the few-shot demonstrations. You are a fact -checker to ask questions to verify an image -text claim. . Here is the image -text claim. The textual part is: [CLAIM_TEXT]; with a list of images of the claim: [CLAIM_IMAGE] . Please ask [NUM_QUES] questions related to the textual part or the imag... | https://arxiv.org/abs/2505.17978v1 |
we provide a few demonstrations, each consisting of a question, a question type and the tool should be selected. Specifically, we leverage the annotated metadata information of questions. For questions annotated with the answering method of image- search, we consider the tool of RIS for such cases. For questions with t... | https://arxiv.org/abs/2505.17978v1 |
TRACE for Tracking the Emergence of Semantic Representations in Transformers Nura Aljaafari1†, Danilo S. Carvalho3, André Freitas1,2,3 1Department of Computer Science, University of Manchester, United Kingdom 2Idiap Research Institute, Switzerland 3National Biomarker Centre, CRUK-MI, University of Manchester, United Ki... | https://arxiv.org/abs/2505.17998v1 |
(i) characteristic rise-then-stabilise patterns in intrinsic dimensionality of hidden representations; (ii) transient spikes in loss curvature; (iii) surges in linguistic category alignment, particularly for part-of-speech and semantic accuracy; and (iv) decrease in mutual information between input and hidden represent... | https://arxiv.org/abs/2505.17998v1 |
serves as a proxy for the representational complexity of neural networks [ 1,17,4]. Under the manifold hypothesis, high-dimensional activations are assumed to lie on lower-dimensional submanifolds [8], with the ID reflecting the necessary degrees of freedom to explain observed variation. During training, representation... | https://arxiv.org/abs/2505.17998v1 |
the ARG2 role regardless of whether it is realised as "noun3", or "location22". In this setting, abstraction is evidenced when internal representations align with underlying semantic functions rather than memorised token identities. The following sections detail each aspect, along with our synthetic data generator and ... | https://arxiv.org/abs/2505.17998v1 |
linguistic structures at each training step t: Conf(ℓ,t) c=1 |B|X x∈Bp(ℓ) c(hℓ(x)), (3) where hℓ(x)is the hidden representation, Bis the evaluation batch size and p(ℓ) cis a linear classifier trained on trained model frozen checkpoints. Probes are used to capture the evolving alignment between internal features and abs... | https://arxiv.org/abs/2505.17998v1 |
use ABSynth to generate ABSynth25K , a dataset of 25,000 sentences with complete frame- semantic annotations. Each example includes ground-truth semantic roles and syntactic categories (POS tags) derived directly from the underlying frame structure, enabling precise investigation of how linguistic abstractions emerge i... | https://arxiv.org/abs/2505.17998v1 |
Dim 303540455055 Intrinsic Dim Curvature and Intrinsic Dimension Across Models - Reduced Attention HeadsFigure 2: Coordinated dynamics of Hessian Curvature Score (blue) and Average Intrinsic Dimension (red) across training steps for three model architectures. Each row shows a different architectural variant: standard m... | https://arxiv.org/abs/2505.17998v1 |
geometric shifts correspond to evolving category structure. Layer 2 shows increased category confidence around the same intersection region, mirroring Layer 1’s earlier drop, indicating a hand-off dynamic between middle and upper layers. This tempo- ral complementarity reflects upward abstraction shifts, whereby higher... | https://arxiv.org/abs/2505.17998v1 |
corpus does not fully capture the ambiguity and richness of NL. While probe-based diagnostics offer valuable insights, they provide a static view of representation content and may not reflect the dynamic computational mechanisms that transformers deploy at inference time. Finally, while TRACE establishes strong correla... | https://arxiv.org/abs/2505.17998v1 |
Learning Representations , 2025. URL https: //openreview.net/forum?id=0fD3iIBhlV . [11] Kenzo Clauw, Daniele Marinazzo, and Sebastiano Stramaglia. Information-theoretic progress measures reveal grokking is an emergent phase transition. In ICML 2024 Workshop on Mecha- nistic Interpretability , 2024. URL https://openrevi... | https://arxiv.org/abs/2505.17998v1 |
In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015. URL http://arxiv.org/abs/1412.6980 . [29] Brenden Lake and Marco Baroni. Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent net... | https://arxiv.org/abs/2505.17998v1 |
dimensionality. In 2007 15th European signal processing conference , pages 606–610. IEEE, 2007. [44] Adepu Ravi Sankar, Yash Khasbage, Rahul Vigneswaran, and Vineeth N Balasubramanian. A deeper look at the hessian eigenspectrum of deep neural networks and its applications to regularization. In Proceedings of the AAAI C... | https://arxiv.org/abs/2505.17998v1 |
ABSynth operationalises frame semantics by representing events as structured predicate-argument frames. Each frame specifies: (i) frame elements; (ii) core semantic roles (e.g., AGENT ,PATIENT , INSTRUMENT ); (iii) corresponding syntactic categories (e.g., NOUN ,VERB ), and (iv) complexity con- straints based on sequen... | https://arxiv.org/abs/2505.17998v1 |
verb5 noun6 verb7 noun9" A.3 Dynamic Entropy Adjustment Algorithm To enforce statistical balance across complexity levels, the sentence generator incorporates an entropy- aware sampling mechanism. During generation, the system maintains a global entropy profile, defined as the frequency distribution of sentence positio... | https://arxiv.org/abs/2505.17998v1 |
FFN size ( dffn) Small 1 64 2 128 Medium 2 96 3 384 Large 3 128 4 512 Scaling Laws and Training Budget. We follow the Chinchilla scaling principles from Hoffmann et al. [22], which demonstrate that model size and training tokens should scale together. Specifically, Chinchilla findings show that optimal training require... | https://arxiv.org/abs/2505.17998v1 |
hidden states and gradients from all layers to compute our diagnosis metrics. We applied minimal regularisation and continuously monitored loss, accuracy, gradient dynamics, representational similarity, and mutual information (MI) throughout training to detect potential phase transitions. This methodological rigour ena... | https://arxiv.org/abs/2505.17998v1 |
NOUN 279984 1.000 1.000 1.000 1.000 TRANSITIVE_VERB 151232 0.779 0.655 0.779 0.711 INTRANSITIVE_VERB 59584 0.291 1.000 0.291 0.451 COMMUNICATION_VERB 54352 0.476 1.000 0.476 0.645 MOTION_VERB 68368 0.718 0.678 0.718 0.697 CHANGE_VERB 14240 0.875 0.510 0.875 0.644 ADJ 59072 0.603 0.558 0.603 0.580 LOCATION 101984 0.641 ... | https://arxiv.org/abs/2505.17998v1 |
ACTION 279984 1.000 1.000 1.000 1.000 LOCATION 101984 0.315 0.858 0.315 0.461 RELATION 115264 0.387 0.823 0.387 0.526 CONNECTOR 51248 0.321 0.950 0.321 0.480 RESULT 68592 0.305 0.731 0.305 0.431 OTHER 153248 0.874 0.642 0.874 0.740 Table 8: Semantic Probe Evaluation – Large Model, Layer 1 Label Count Accuracy Precision... | https://arxiv.org/abs/2505.17998v1 |
using a neural critic function Tθ:X × Z → R, parameterised by a multilayer perceptron (MLP). Given joint samples (x, z)∼PXZand marginal samples formed by pairing x∼PXwith independently sampled z′∼PZ, the MI is estimated via: ˆIθ(X;Z) =EPXZ[Tθ(x, z)]−logEPX⊗PZ[eTθ(x,z′)] (8) Our implementation uses a 3-layer MLP with hi... | https://arxiv.org/abs/2505.17998v1 |
arXiv:2505.18011v1 [cs.CL] 23 May 2025Training with Pseudo-Code for Instruction Following Prince Kumar∗ IBM Research AI prince.kumar12@ibm.comRudra Murthy∗ IBM Research AI rmurthyv@in.ibm.comRiyaz Bhat∗ IBM Research AI Riyaz.Bhat@ibm.com Danish Contractor∗ IBM Research AI danish.contractor@ibm.com Abstract Despite the ... | https://arxiv.org/abs/2505.18011v1 |
natural language, including being better structured, clearer, and more concise. This clarity and conciseness can improve model performance by reducing the complexity of instructions. Thus, instead of using verbose natural language instructions, prompting models with pseudo-code [Mishra et al., 2023] or using pseudo-cod... | https://arxiv.org/abs/2505.18011v1 |
mentioned previously, we enhance existing instruction-tuning datasets by re-expressing instructions in pseudo-code as part of the output. Since manually crafting pseudo- code programs for instruction-tuning is prohibitively expensive, we develop a multi-step data conver- sion pipeline to create pseudo-code instruction ... | https://arxiv.org/abs/2505.18011v1 |
natural language instructions and trigger the Repair step as needed. We run the Repair twice with different temperature parameters and select the best program among the two runs. Table 8 in the Appendix reports the performance of the generated pseudo-code on the *https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v... | https://arxiv.org/abs/2505.18011v1 |
All models were SFT trained on a full NVIDIA A100 80GB node with Flash Attention 2 [Dao, 2024] and bf16 precision using the Dolomite Engine Framework.*All models were trained for2epochs using the PyTorch’s implementation of AdamW [Loshchilov and Hutter, 2019] as the optimizer, with a starting learning rate of 1e-5 and ... | https://arxiv.org/abs/2505.18011v1 |
included in the original datasets. We use vLLM [Kwon et al., 2023] to speed up inference on the Mathematics, Reasoning and Code Tasks. Output Post Processing: We develop robust task-specific output-processing pipelines for both NL and pseudo-code models. (See Appendix Section F for more details). All tasks are evaluate... | https://arxiv.org/abs/2505.18011v1 |
on instructions that do not have distractor instructions*(EM) vs. ones that do (EM-D). As can be seen, all models (with the exception of Qwen2.5 on distractors) universally perform better on both categories of instructions when trained with pseudo-code instructions. Answer-Modifying Instructions: On the right hand side... | https://arxiv.org/abs/2505.18011v1 |
instruc- tions (string, numeric or format manipulations, operations on lists regardless of whether they are conditional on the answering task) result in a steep drop in performance. It is noteworthy, that while all models (independent of the training paradigm) have steep deterioration in performance, training with pseu... | https://arxiv.org/abs/2505.18011v1 |
52.33 50.31 56.20 Granite 8B Code -NL 44.92 36.70 33.13 21.01 13.61 29.88 44.92 50.35 50.36 40.70 39.44 45.15 Granite 8B Code-PC 43.86 30.42 25.18 12.68 10.28 24.48 43.86 41.33 40.84 31.12 31.25 37.68 Granite 3.1 8B -NL 67.38 55.59 49.77 38.04 20.69 46.29 67.38 66.51 66.17 60.26 52.68 62.60 Granite 3.1 8B -PC 61.01 51.... | https://arxiv.org/abs/2505.18011v1 |
0.122 0.040 0.071 Qwen2.5 7B -NL 0.720 0.205 0.500 0.160 0.604 0.184 0.543 0.146 0.383 Qwen2.5 7B -PC 0.268 0.154 0.457 0.12 0.055 0.184 0.226 0.095 0.195 Granite 8B Code -NL 0.634 0.200 0.396 0.129 0.537 0.155 0.427 0.133 0.326 Granite 8B Code -PC 0.512 0.145 0.299 0.111 0.372 0.156 0.360 0.132 0.261 Granite 3.1 8B -N... | https://arxiv.org/abs/2505.18011v1 |
or contain other forms of instruction enrichment [Qi et al., 2024, Lou et al., 2024]. Further, our results from Table 2 suggests that for some model-task combinations involving common-sense and reasoning, training with vanilla NL instructions may be better. This suggests it may be useful to explore techniques to combin... | https://arxiv.org/abs/2505.18011v1 |
have solved question answering? try arc, the ai2 reasoning challenge. arXiv:1803.05457v1 , 2018. K. Cobbe, V . Kosaraju, J. Tworek, H. Jun, M. Plappert, D. Hendrycks, S. Goel, M. Fath, S. Jha, and P. Ghasemi. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168 , 2021. Tri Dao. Flashattention... | https://arxiv.org/abs/2505.18011v1 |
Chang Zhou, and Jingren Zhou. Self-play with execution feedback: Improving instruction-following capabilities of large language models. In The Thirteenth International Conference on Learning Representations , 2025. URL https://openreview.net/forum?id=cRR0oDFEBC . Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Lon... | https://arxiv.org/abs/2505.18011v1 |
augmented code emulator. In Proceedings of the 41st International Conference on Machine Learning , ICML’24. JMLR.org, 2024a. Jia Li, Ge Li, Yongmin Li, and Zhi Jin. Structured chain-of-thought prompting for code generation. ACM Transactions on Software Engineering and Methodology , 34(2):1–23, 2025. Shiyang Li, Jun Yan... | https://arxiv.org/abs/2505.18011v1 |
agentic flows, 2024. URL https://arxiv.org/abs/2407.03502 . Niklas Muennighoff, Qian Liu, Armel Randy Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro V on Werra, and Shayne Longpre. Octopack: Instruction tuning code large language models. In The Twelfth International Conference on... | https://arxiv.org/abs/2505.18011v1 |
llm reasoning, 2025. URL https://arxiv.org/abs/2501.02152 . Joshua Vendrow, Edward Vendrow, Sara Beery, and Aleksander Madry. Do large language model benchmarks test reliability?, 2025. URL https://arxiv.org/abs/2502.03461 . Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva ... | https://arxiv.org/abs/2505.18011v1 |
reasoning with large language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024b. URL https://openreview.net/forum?id=ANO1i9JPtb . Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving ... | https://arxiv.org/abs/2505.18011v1 |
time, her dream job at a rival company had finally opened up, and she had been invited for an interview. The thought of leaving her comfortable life behind, to take a chance on something new and uncertain, made Sophie feel anxious, but also excited. She had always been one to take risks, and it had paid off in the past... | https://arxiv.org/abs/2505.18011v1 |
w i k i p e d i a page " h t t p s : / / en . w i k i p e d i a . org / w iki / Raymond_III , _ C o u n t _ o f _ T r i p o l i " . Do n o t use any commas and h i g h l i g h t a t l e a s t 3 s e c t i o n s t h a t has t i t l e s i n 16 markdown format , f o r example *h i g h l i g h t e d s e c t i o n p a r t 1 ... | https://arxiv.org/abs/2505.18011v1 |
t h e son of Count Pons of T r i p o l i and h i s wife , Melisende of J e r u s a l e m . Raymond was a member of t h e House of T r i p o l i , a b r a n c h of t h e House of Anjou . Raymond I I I i s b e s t known f o r h i s r o l e i n t h e T h i r d Crusade , where he was a v a s s a l of King R i c h a r d t h... | https://arxiv.org/abs/2505.18011v1 |
Armenia , wi th whom he had two sons , Bohemond and Baldwin . A f t e r h e r death , he m a r r i e d A l i c e of Champagne , w it h whom he had two d a u g h t e r s , Melisende and P h i l i p p a . Raymond I I I d i e d i n 1187 d u r i n g t h e B a t t l e of Cresson . His d e a t h was a major blow t o t h e C ... | https://arxiv.org/abs/2505.18011v1 |
t h e C r u s a d e s and was c a p t u r e d by S a l a d i n i n 1187 d u r i n g t h e B a t t l e of H a t t i n . He was r e l e a s e d a f t e r t h e payment of a ransom . Raymond I I I was a l s o i n v o l v e d i n t h e n e g o t i a t i o n s f o r t h e T h i r d Crusade . Raymond I I I , Count of T r i p... | https://arxiv.org/abs/2505.18011v1 |
r e v i e w _ m o v i e _ s o c i a l _ n e t w o r k ( ) −> s t r : " " " Write a d e t a i l e d r e v i e w of t h e movie " The S o c i a l Network " . The e n t i r e r e s p o n s e s h o u l d be i n E n g l i s h and a l l lower c a s e ( no c a p i t a l l e t t e r s w h a t s o e v e r ) . 17 R e t u r n s :... | https://arxiv.org/abs/2505.18011v1 |
i s well − ba la n ce d , making f o r a smooth and e n j o y a b l e viewing e x p e r i e n c e . t h e f i l m a l s o does a g r e a t j o b of e x p l o r i n g t h e e t h i c a l and s o c i a l i m p l i c a t i o n s of facebook , making i t a t h o u g h t − p r o v o k i n g and r e l e v a n t a d d i t i o... | https://arxiv.org/abs/2505.18011v1 |
. The f i l m i s well − c r a f t e d and engaging , w it h a s t r o n g p e r f o r m a n c e from J e s s e E i s e n b e r g as Z u c k e r b e r g . E i s e n b e r g p e r f e c t l y c a p t u r e s t h e c h a r a c t e r ’ s mix of i n t e l l i g e n c e , charm , and a r r o g a n c e , making him b ot h l ... | https://arxiv.org/abs/2505.18011v1 |
of t h e l e g a l b a t t l e between Z u c k e r b e r g and h i s f o r m e r b u s i n e s s p a r t n e r s . The t e n s i o n and drama of t h e courtroom s c e n e s a r e p a l p a b l e , and t h e f i l m does a good j o b of showing t h e i m p a c t t h a t t h e l a w s u i t had on Z u c k e r b e r g ’ ... | https://arxiv.org/abs/2505.18011v1 |
t h : C P r o c e s s e d Ground T r u t h : C Output from Granite 3.1 8B-NL : G e n e r a t e d Output : C . Response : C . P r o c e s s e d G e n e r a t e d Output : C 18 C.2.2 Example 2 Input : Which s t a t e m e n t b e s t e x p l a i n s why p h o t o s y n t h e s i s i s t h e f o u n d a t i o n of most foo... | https://arxiv.org/abs/2505.18011v1 |
" \ " \ " # r e a d t h e q u e s t i o n and t h e o p t i o n s # use our knowledge t o s e l e c t t h e b e s t o p t i o n # r e t u r n t h e s e l e c t e d o p t i o n >>> g e n e r a t e _ a n s w e r ( Given a q u e s t i o n and 4 o p t i o n s , g e n e r a t e one of A, B , C , or D which b e s t answers t... | https://arxiv.org/abs/2505.18011v1 |
e s as many sheep as S e a t t l e . How many sheep do Toulouse , C h a r l e s t o n , and S e a t t l e have t o g e t h e r i f S e a t t l e has 20 sheep ? Ground Truth: Ground T r u t h : I f S e a t t l e has 20 sheep , C h a r l e s t o n has 4 *20 sheep = <<20 *4=80>>80 sheep \ nToulouse has t w i c e as many s... | https://arxiv.org/abs/2505.18011v1 |
= $ < <10 *40=400 > >400.\ nThus , E l i z a w i l l r e c e i v e a t o t a l of $400 + $60 = $ < <400+60=460 > >460 f o r t h i s week ’ s work . \ n #### 460 P r o c e s s e d Ground T r u t h : 460 Output from Llama 3.1 8B-PC : G e n e r a t e d Output : [PSEUDOCODE] d e f e l i z a _ w e e k l y _ e a r n i n g s ... | https://arxiv.org/abs/2505.18011v1 |
a y ( h o u r s = hours_worked − 40 , h o u r l y _ r a t e =10) # t h i s f u n c t i o n c a l c u l a t e s t h e t o t a l pay t o t a l _ p a y = r e g u l a r _ p a y + o v e r t i m e _ p a y r e t u r n \ " E l i z a ’ s e a r n i n g s f o r t h i s week i s $ \ " + s t r ( t o t a l _ p a y ) >>> e l i z a _ ... | https://arxiv.org/abs/2505.18011v1 |
n# ### 7 P r o c e s s e d Ground T r u t h : 7 Output from Granite 3.1 8B-NL : 20 G e n e r a t e d Output : Response : 16 P r o c e s s e d G e n e r a t e d Output : 16 C.4.2 Example 2 Input : Greg has an alarm s e t t o r i n g t h r e e t i m e s a day as a r e m i n d e r . When t h e alarm goes o f f , i t c o n... | https://arxiv.org/abs/2505.18011v1 |
r d tim e t h e alarm went o f f , i t r an g f o r 12 / 2 = 6 t i m e s . In t o t a l , t h e alarm r a ng 4 + 12 + 6 = 22 t i m e s . [PSEUDOCODE] d e f a n s w e r _ q u e s t i o n ( q u e s t i o n : s t r ) −> s t r : \ " \ " \ " Th is f u n c t i o n answers t h e g i v e n q u e s t i o n P a r a m e t e r s :... | https://arxiv.org/abs/2505.18011v1 |
t ti me i t went o f f today , i t r a ng f o u r t i m e s . The second ti me i t went o f f , i t r an g f o r t h r e e t i m e s as l ong as t h e f i r s t t ime . The t h i r d time , i t ra ng f o r h a l f as lo ng as t h e second ti me . How many t i m e s d i d t h e alarm r i n g i n a l l ? Answer : The sec... | https://arxiv.org/abs/2505.18011v1 |
and by t h e camera s t a r e s o f f i n t o t h e d i s t a n c e . 1 . p l a c e s t h e hands back i n t o t h e d e s s e r t . 2 . u s e s h i s hands t o e a t t h e food . 3 . s t a n d s and walks t o w a r d s t h e camera . 21 Ground Truth: Ground T r u t h : 2 P r o c e s s e d Ground T r u t h : 2 Output f... | https://arxiv.org/abs/2505.18011v1 |
r r e c t _ e n d i n g ( s e n t e n c e : s t r , o p t i o n s : L i s t [ s t r ] ) −> i n t : \ " \ " \ " Given a p a r t i a l s e n t e n c e and f o u r o p t i o n s 0 , 1 , 2 , and 3 . G e n e r a t e t h e o p t i o n number c o r r e s p o n d i n g t o t h e c o r r e c t e n d i n g . P a r a m e t e r s ... | https://arxiv.org/abs/2505.18011v1 |
u t t i n g t h e g r a s s : A man walks o u t s i d e p l u g s h i s lawn mower i n and g e t s r e a d y t o mow . He s t a r t s mowing h i s lawn c a s u a l l y going o ve r a l l t h e g r a s s . His dog O p t i o n s : 0 . comes o u t s i d e and s t a r t s t o f o l l o w him a l i t t l e w h i l e he ’ s ... | https://arxiv.org/abs/2505.18011v1 |
s e Ground Truth: Ground T r u t h : c P r o c e s s e d Ground T r u t h : 12 h o u r s Output from Granite 8B Code-NL : G e n e r a t e d Output : cResponse : c P r o c e s s e d G e n e r a t e d Output : 12 h o u r s C.6.2 Example 2 Input : t h e p e r i m e t e r of a rhombus i s 68 cm and one of i t s d i a g o n... | https://arxiv.org/abs/2505.18011v1 |
r _ q u e s t i o n ( q u e s t i o n : s t r , o p t i o n _ 0 : s t r , o p t i o n _ 1 : s t r ) −> i n t : \ " \ " \ " Given a q u e s t i o n wi th two p o s s i b l e o p t i o n s 0 and 1 . G e n e r a t e e i t h e r 0 or 1 which answers t h e q u e s t i o n . P a r a m e t e r s : q u e s t i o n ( s t r ) : ... | https://arxiv.org/abs/2505.18011v1 |
u n c t i o n r e t u r n s 0 i f t h e answer i s f a l s e i f a n s w e r _ i s _ f a l s e ( q u e s t i o n ) : r e t u r n 0 # t h i s f u n c t i o n r e t u r n s 1 i f t h e answer i s t r u e e l i f a n s w e r _ i s _ t r u e ( q u e s t i o n ) : r e t u r n 1 e l s e : # t h i s f u n c t i o n r e t u r ... | https://arxiv.org/abs/2505.18011v1 |
c a t had an i n f e c t i o n , b u t _ was l y i n g . O p t i o n s : 1 . Tanya 2 . Emily Ground Truth: Ground T r u t h : 1 P r o c e s s e d Ground T r u t h : Tanya Output from Mistral 7B v0.3-NL : G e n e r a t e d Output : 2 Response : Tanya P r o c e s s e d G e n e r a t e d Output : Tanya C.8.2 Example 2 Inp... | https://arxiv.org/abs/2505.18011v1 |
" # t h i s f u n c t i o n c h e c k s i f t h e u n d e r s c o r e i s p r e s e n t i n t h e s e n t e n c e i f u n d e r s c o r e _ p r e s e n t _ i n _ s e n t e n c e ( s e n t e n c e ) : # t h i s f u n c t i o n c h e c k s i f t h e o p t i o n 1 i s a v a l i d r e p l a c e m e n t f o r t h e u n d e ... | https://arxiv.org/abs/2505.18011v1 |
c h e d g l a s s e s c r a c k b u t i t di dn ’ t f i t . The _ was t o o narrow . O p t i o n s : 1 . b r u s h 2 . c r a c k Answer : 1 \ " , \ " Judy bought new b r u s h e s t o p a i n t t h e e t c h e d g l a s s e s c r a c k b u t i t di dn ’ t f i t . The _ was t o o narrow . O p t i o n s : 1 . b r u s h 2... | https://arxiv.org/abs/2505.18011v1 |
is an example of kinetic energy? (A) a book sitting on a shelf (B) a ball rolling down a hill (C) a runner about to start a race (D) a window getting hot from the sun." "output": "B.", "generated output": "’C’" "input": "Question: A student spills a cup of water onto a table. What property of the water has changed? (A)... | https://arxiv.org/abs/2505.18011v1 |
incontext example for Pseudocode Generation You are provided a natural language description of a task. Your task is to generate a pseudocode instruction to solve the task and not generate a solution. The pseudocode instruction should use pythonic constructs and resemble a step-by-step algorithm to the task provided. Yo... | https://arxiv.org/abs/2505.18011v1 |
answer_with_explanation # program { "method": "answer_question_given_options", "arguments": {"question_with_options": "str"}, "return": "str", "execute": "answer_question_given_options(question_with_options)", } 31 Figure 10: Pseudocode for CoT ESNLI Dataset def perform_textual_entailment_with_explanation(task_with_inp... | https://arxiv.org/abs/2505.18011v1 |
the provided text sentence_1, sentence_2 = extract_inputs_to_instruction(instruction=instruction) # this function performs the instruction on the two sentences provided response = apply_instruction(instruction=instruction, input_1=sentence_1, input_2=sentence_2) # this function looks at the instruction, the inputs and ... | https://arxiv.org/abs/2505.18011v1 |
text span copied from the input passage. - Effect: The effect of the intervention on the outcome, relative to the comparator. The effect should be one of the following three values: (\"significantly increased\", \"significantly decreased\", \"no significant difference\"). - Evidence: The evidence for the effect. This s... | https://arxiv.org/abs/2505.18011v1 |
response to the actual task. In the first stage, we search for well-defined delimiters such as ‘ [/PSEUDOCODE] ’ or its variant ‘ [/[PSEUDOCODE]] ’. If found, the text following the last occurrence of these markers is extracted, assuming it represents the final answer generated after pseudo-code. If these markers are a... | https://arxiv.org/abs/2505.18011v1 |
(EM) metric. For each model family and dataset pair, we randomly sampled 10% of the total data without replacement and computed the EM accuracy for both model types. This sampling process was repeated 500 times to account for variability, and the average EM scores were recorded across trials. A paired t-test was then a... | https://arxiv.org/abs/2505.18011v1 |
arXiv:2505.18034v2 [cs.AI] 27 May 2025Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks Wentao Sun École Polytechnique wentao.sun@polytechnique.edu wentao.sun@nokia.comJoão Paulo Nogueira Institut Polytechnique de Paris joaopaulo.fontoura@ip-paris.fr joao.fontoura_nogueira@nokia.com A... | https://arxiv.org/abs/2505.18034v2 |
the final causal judgment. Rather than guessing from surface cues, the model is prompted to identify paths, confounders, and potential interventions—just as a human analyst would. To implement this framework, we leverage Qwen3-32B , an open-source LLM with native tool- calling capabilities . Qwen3 can generate intermed... | https://arxiv.org/abs/2505.18034v2 |
extremely low recall of true causal relations (F1 well under 5% 1DeepSeek-R1 explicitly does not support tool-calling; see https://github.com/deepseek-ai/ DeepSeek-R1/issues/9 . Phi-4-Reasoning-Plus similarly lacks tool-call capabilities; see https:// huggingface.co/microsoft/Phi-4-reasoning-plus/discussions/13 . 2 in ... | https://arxiv.org/abs/2505.18034v2 |
finalizing its answer, thereby encouraging more systematic reasoning. 3.1 Knowledge Graph Generation In the first stage, the model constructs a skeleton graph that captures the observed statistical dependencies. We prompt the model with a list of observed correlations and independencies, and instruct it to output a JSO... | https://arxiv.org/abs/2505.18034v2 |
causal query. The model’s task is now framed as: given this candidate knowledge graph, determine whether the stated hypothesis holds. Because the model has an explicit graph to consult, its reasoning can be grounded in the graph’s structure rather than relying on superficial cues from the textual premise. Concretely, t... | https://arxiv.org/abs/2505.18034v2 |
the premise. This graph is constructed using a predefined tool-calling schema, capturing correlations, independencies, and other potential links. Subsequently, the model reasons over this structured graph to assess whether the hypothesized causal relation follows. This two-step process—(1) parsing the premise into a fo... | https://arxiv.org/abs/2505.18034v2 |
affects model comprehension. We conduct a controlled experiment using a smaller model variant (Qwen3-4B), holding all other components fixed while varying the KG output style. 6 Table 1 reports the results of three KG notations: Among these, Style 2 consistently yields the highest F 1score (0.3617) and recall (0.5075),... | https://arxiv.org/abs/2505.18034v2 |
2023] or chain-of-thought [Wei et al., 2022] strategies, the graph-based pipeline has several key advantages. The intermediate graph representation makes the model’s reasoning more transparent and allows explicit consistency checks, reducing reliance on spurious linguistic patterns in the input. Grounding the model’s d... | https://arxiv.org/abs/2505.18034v2 |
Can large language models infer causation from correlation? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024. URL https://openreview.net/forum?id=vqIH0ObdqL . Markus Kalisch and Peter Bühlman. Estimating high-dimensional directed acyc... | https://arxiv.org/abs/2505.18034v2 |
arXiv:2505.18040v1 [cs.CL] 23 May 2025Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition Minxue Niu Emily Mower Provost University of Michigan, Ann Arbor, Michigan, USA {sandymn, emilykmp}@umich.edu Abstract The ability to handle various emotion labels without dedicated training i... | https://arxiv.org/abs/2505.18040v1 |
differ- ent needs. Common design choices include la- bel schema (e.g., whether to use categorical or dimensional labels), label granularity (whether to adopt an extensive set of fine-grained emotions or a smaller set of distinct categories), and label selection (which specific emotions or dimensions to include in the l... | https://arxiv.org/abs/2505.18040v1 |
a contrastive framework that extends CLIP to multi-label tasks, enabling zero-shot ER across variable label spaces with- out additional human annotation. 3) We evaluate our model against multiple supervised and zero- shot baselines, showing strong performance with a compact model size. Our data and code are re- leased ... | https://arxiv.org/abs/2505.18040v1 |
mann et al., 2024; Stanley et al., 2023; Bhaumik and Strzalkowski, 2024b; Niu et al., 2024). Gen- erative descriptions enhance label richness and ac- curacy, but there is no standardized way to map open-ended textual descriptions to existing label sets or apply them in downstream applications. 3 Methods 3.1 Obtaining R... | https://arxiv.org/abs/2505.18040v1 |
linear projection to their [CLS] “I finally got the promotion!” Slight frustrationApprovalA little bummed… UneaseText Encoder Label EncoderT0 T1 T2 T3… TM L0 L1 L2 L3 … LNL4… L0·T0L0·T1L0·T2L0·T3 L0·TM … L1·T0L1·T1L1·T2L1·T3 L1·TM … L2·T0L2·T1L2·T2L2·T3 L2·TM … L3·T0L3·T1L3·T2L3·T3 L3·TM … L4·T0L4·T1L4·T2L4·T3 L4·TM ……... | https://arxiv.org/abs/2505.18040v1 |
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