ResearchArcade
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ICLR.cc/2025/Conference | zkNCWtw2fd | 1 | Title | SYNERGISTIC APPROACH FOR SIMULTANEOUSOPTIMIZATION OF MONOLINGUAL, CROSS-LINGUAL,AND MULTILINGUAL INFORMATION RETRIEVAL |
ICLR.cc/2025/Conference | zkNCWtw2fd | 2 | Abstract | Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingualpre-trained language models have achieved remarkable success, yet they oftenoptimize for either monolingual, cross-lingual, or multilingual retrieval performance... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 3 | 1 INTRODUCTION | Information retrieval (IR) across different languages is an increasingly important challenge in naturallanguage processing. However, optimizing information retrieval systems for multilingual scenarios isnot a straightforward task, as it requires considering multiple distinct retrieval settings, each withits own set of ... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 4 | 1 INTRODUCTION | Recent approaches to multilingual information retrieval have leveraged multilingual pre-trainedlanguage models such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) to encodequeries and documents (Karpukhin et al., 2020). While these models can transfer relevance matchingcapabilities across languages, th... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 5 | 2.1 CONTRASTIVE LEARNING | Throughout the paper, we utilize the dual-encoder architecture with shared parameters, which iscommonly used for dense retrieval (DR; Ni et al., 2022). Contrastive learning is a method for trainingDR models by contrasting positive pairs against negatives. Specifically, given a batch of triplets, eachof which consists o... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 6 | 2.2 BATCH SAMPLING | Baseline Batch Sampling. We study the following training batching procedures introduced by(Roy et al., 2020). (i) Monolingual batching (coined as X-X-mono model) creates each batch withmono language, where all the triplets consist of queries and passages in the same language. Notethat we sample the language used to cre... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 7 | 2.2 BATCH SAMPLING | As shown in (Roy et al., 2020), the X-Y model is more effective in cross-lingual retrieval scenariosand shows reduced language bias; however, the X-X-mono surpasses the X-Y model in monolingualretrieval. These results inspire us to explore whether simply combining the two batch samplingapproaches can achieve improvemen... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 8 | 2.2 BATCH SAMPLING | Figure 1: Illustrative example of monolingual, cross-lingual, and multilingual information retrieval. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 9 | 2.2 BATCH SAMPLING | Figure 2: Illustrations of the proposed hybrid batch sampling (assuming we only have training datain English, Arabic, and Japanese), where our model is exposed to monolingual and cross-lingualbatches with the respective probability of α and β = 1 − α. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 10 | 2.2 BATCH SAMPLING | Hybrid Batch Sampling.In this work, we propose to combine the two aforementioned baselinesampling strategies. Specifically, when creating batch training data, we set α and β = 1 − α as therespective probability of using monolingual and cross-lingual batching as shown in Fig. 2.1 |
ICLR.cc/2025/Conference | zkNCWtw2fd | 11 | 2.2 BATCH SAMPLING | 1In the experiments, we found out that setting the hyperparameters α and β to 0.5 resulted in the best balance |
ICLR.cc/2025/Conference | zkNCWtw2fd | 12 | 2.2 BATCH SAMPLING | between the performance of the proposed model on monolingual and multilingual evaluations. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 13 | 3 EXPERIMENTAL SETUP | This section presents the experimental setup for evaluating the proposed hybrid batch training strategy.We first discuss the training process, including datasets, and multilingual pre-trained models. Next,we introduce the evaluation datasets and metrics used to assess the performance of the fine-tunedmodels. Finally, w... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 26 | 4.1 SUMMARY OF MAIN RESULTS | In particular, Tables 3 through 6 showcase the MAP and Recall scores for zero-shot monolingual,cross-lingual, and multilingual retrieval tasks on the XQuAD-R and MLQA-R datasets, consideringboth fine-tuned XLM-R and LaBSE models. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 37 | 4.2 | Table 4: Performance comparison of MAP and Recall scores across zero-shot monolingual, crosslingual, and multilingual retrieval tasks on the MLQA-R dataset for a fine-tuned XLM-R model anddifferent training batch types. The best result is highlighted in bold, and the second-best result isunderlined. |
ICLR.cc/2025/Conference | vVlNBaiLdN | 1 | Title | 002003004005006007 |
ICLR.cc/2025/Conference | zkNCWtw2fd | 14 | 3.1 TRAINING | Datasets. To conduct the study of batch sampling, parallel query-passage training pairs are requiredsuch that we can construct cross-lingual triplets, where each query and its relevant (or irrelevant)passage are in different languages. mMARCO (Bonifacio et al., 2021) is the only dataset with parallelqueries and passage... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 15 | 3.1 TRAINING | Training Setup. We apply the baseline and our proposed hybrid batching to fine-tune two representative multilingual pre-trained models: (i) XLM-RoBERTa (XLM-R) (Conneau et al., 2020);and (ii) language-agnostic BERT sentence embedding (LaBSE) (Feng et al., 2022). Model trainingexperiments were conducted using one NVIDIA... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 16 | 3.1 TRAINING | Hyperparameter Tuning for Hybrid Batch Sampling. To determine the optimal values for thehyperparameters α and β in our hybrid batch sampling approach, we conducted a comprehensive gridsearch. We evaluated α values ranging from 0 to 1, with β always set to 1 − α. Each configurationwas tested on a held-out validation set... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 17 | 3.2 EVALUATION | Datasets. We evaluate the retrieval effectiveness of different models on three distinct datasets:XQuAD-R (Roy et al., 2020) and MLQA-R (Roy et al., 2020).2 XQuAD-R and MLQA-R are questionanswering datasets with parallel questions and passages in 11 languages and 7 languages, respectively.Thus, these two datasets can be... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 18 | 3.2 EVALUATION | 2The evaluation of the models is conducted on datasets that are completely separate and distinct from theones used for training. More specifically, the models have not encountered any data samples, whether fromthe training or testing splits, of the evaluation datasets during their training process. This ensures an unbi... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 19 | 3.2 EVALUATION | XQuAD-R (↑) |
ICLR.cc/2025/Conference | zkNCWtw2fd | 20 | 3.2 EVALUATION | Table 1: Main experiments on XQuAD-R and MLQA-R. mAP (marco averaged across all languages)numbers are reported. Mo., CR., and Mul. denote monolingual, cross-lingual, and multilingualretrieval settings. respectively. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 21 | 3.2 EVALUATION | MLQA-R (↑) XLM-R LaBSE Sampling Mo..792.755.798.808.801.817 X-XX-YHybridX-XX-YHybrid language bias (↓) Model XLM-R LaBSE Sampling XQuAD-R MLQA-R410295287262225221 X-XX-YHybridX-XX-YHybrid Metrics and Settings. We report the mean average precision (mAP) for XQuAD-R and MLQA-Rsince the metric considers the retrieval qual... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 22 | 3.2 EVALUATION | Model |
ICLR.cc/2025/Conference | zkNCWtw2fd | 23 | 4.1 SUMMARY OF MAIN RESULTS | Zero-shot Retrieval Evaluation. We report the effectiveness of different batch sampling strategiesin Table 1. We observe that X-X and X-Y sampling only perform well in monolingual and crosslingual retrieval settings, respectively. These results indicate that optimization for either monolingualor cross-lingual retrieval... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 24 | 4.1 SUMMARY OF MAIN RESULTS | 3The results for the Recall metric are in Section 4.2.1.4The performance of the models is evaluated on certain languages, such as Greek (el) and Vietnamese (vi),which were not included in the training data. This aspect of the evaluation process aims to assess the ability ofthe models to handle languages they have not b... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 25 | 4.1 SUMMARY OF MAIN RESULTS | Table 2: Language bias in multilingual retrieval. |
ICLR.cc/2025/Conference | 9DrPvYCETp | 22 | 3 SHARED RECURRENT MEMORY TRANSFORMER | R(s, u, a(U )) : S × U × An → R, O(s, a) : S × A → O. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 27 | 4.1 SUMMARY OF MAIN RESULTS | Language Bias Evaluation. To gain insight into why hybrid batch sampling achieves strongperformance in multilingual retrieval settings, we investigate the language bias exhibited by modelsfine-tuned using different batch sampling strategies. Following Huang et al. (2023b), we measure thelanguage bias using the maximum ... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 28 | 4.2 | IN-DEPTH ANALYSIS 4.2.1 ZERO-SHOT RETRIEVAL EVALUATION ON XQUAD-R AND MLQA-R We present the experimental results of our proposed hybrid batching approach for improving theretrieval performance of fine-tuned multilingual language models across various tasks and datasets.We compare our method with two baseline training b... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 29 | 4.2 | Consistent improvement across languages and tasks: Tables 3 through 6 demonstrate the performance of the proposed hybrid batching approach when applied to the XLM-R and LaBSE models onthe XQuAD-R and MLQA-R datasets. Our method consistently achieves the highest mean MAP andmean R@1 scores across monolingual and cross-l... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 30 | 4.2 | Balanced performance across evaluation metrics: The proposed approach strikes a balance between the X-X-mono (optimized for monolingual retrieval setting) and X-Y (crosslingual/multilingual retrieval settings) baselines. This compromise is evident when analyzing theperformance of individual languages across different r... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 31 | 4.2 | 5Note that in XQuAD-R and MLQA-R, each query only has one relevant passage in each language. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 32 | 4.2 | Evaluation of Fine-tuned XLM-R Model on XQuAD-R Dataset |
ICLR.cc/2025/Conference | zkNCWtw2fd | 33 | 4.2 | Table 3: Performance comparison of MAP and Recall scores across zero-shot monolingual, crosslingual, and multilingual retrieval tasks on the XQuAD-R dataset for a fine-tuned XLM-R model anddifferent training batch types. The best result is highlighted in bold, and the second-best result isunderlined. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 34 | 4.2 | Monolingual Cross-lingual Multilingual Source Language X-X-mono X-Y Proposed X-X-mono X-Y Proposed X-X-mono X-Y Proposed ardeeleneshiruthtrvizh Mean R@1 Monolingual Cross-lingual R@10 Multilingual Source Language X-X-mono X-Y Proposed X-X-mono X-Y Proposed X-X-mono X-Y Proposed ardeeleneshiruthtrvizh Mean Evaluation of... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 35 | 4.2 | MAP |
ICLR.cc/2025/Conference | zkNCWtw2fd | 36 | 4.2 | Competitive reduction in average rank distance compared to cross-lingual batching. Theproposed approach exhibits competitive performance in reducing the average rank distance comparedto the strong X-Y baseline. In Table 7 (XQuAD-R), the proposed method achieves the best mean rankdistance of 286.6 using XLM-R, outperfor... |
ICLR.cc/2025/Conference | gtVo4xcpFI | 31 | 3.3 BENCHMARK DATASET CONSTRUCTION | Amount Description |
ICLR.cc/2025/Conference | gtVo4xcpFI | 32 | 57 60 | Focus on evaluating the grasp of the LLM on fundamental hardware concepts and principles. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 38 | 4.2 | Table 5: Performance comparison of MAP and Recall scores across zero-shot monolingual, crosslingual, and multilingual retrieval tasks on the XQuAD-R dataset for a fine-tuned LaBSE model anddifferent training batch types. The best result is highlighted in bold, and the second-best result isunderlined. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 39 | 4.2 | Table 6: Performance comparison of MAP and Recall scores across zero-shot monolingual, crosslingual, and multilingual retrieval tasks on the MLQA-R dataset for a fine-tuned LaBSE model anddifferent training batch types. The best result is highlighted in bold, and the second-best result isunderlined. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 40 | 4.2 | Tables 7 and 8 present a comprehensive comparison of the average rank distance metric6 (Huang et al.,2023a) across different multilingual retrieval tasks using fine-tuned XLM-R and LaBSE models. Theproposed approach is evaluated against two baseline methods: X-X-mono and X-Y, on two datasets:XQuAD-R (Table 7) and MLQA-... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 41 | 5 CONCLUSION | Developing IR models that can handle queries and documents across many languages is increasinglycritical. In this work, we introduced a hybrid batch training strategy to optimize IR systems formonolingual, cross-lingual, and multilingual performance simultaneously. By fine-tuning multilinguallanguage models on a mix of... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 42 | 5 CONCLUSION | 6Rank distance is the average, over all queries and their relevant documents, of the difference between themaximum and minimum ranks assigned by an MLIR model to parallel (semantically similar) relevant documentsacross different languages. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 43 | 6 LIMITATIONS | This work focuses on optimizing retrieval performance but does not address issues related to resultdiversity, fairness, or transparency in multilingual settings. For example, it may reflect societalbiases present in the training data. Addressing these concerns is important for building equitablemultilingual retrieval s... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 44 | 6 LIMITATIONS | Furthermore, the experiments focus only on the XQuAD-R, MLQA-R, and MIRACL benchmarkdatasets. While these cover a range of languages, they may not be fully representative of real-worldmultilingual information retrieval needs. The robustness of the results to other domains, questiontypes, and retrieval scenarios is an e... |
ICLR.cc/2025/Conference | zkNCWtw2fd | 45 | 6 LIMITATIONS | Average Rank Distance over XQuAD-R Dataset |
ICLR.cc/2025/Conference | zkNCWtw2fd | 46 | 6 LIMITATIONS | Table 7: Comparison of the rank distances among relevant documents of the XQuAD-R dataset acrossrank lists generated by fine-tuned XLM-R and LaBSE models for zero-shot multilingual retrievaltasks under different training batch types. The best result is highlighted in bold, and the second-bestresult is underlined. |
ICLR.cc/2025/Conference | zkNCWtw2fd | 47 | 6 LIMITATIONS | XLM-R Source Language X-X-mono X-Y Proposed X-X-mono X-Y Proposed ardeeleneshiruthtrvizh Mean Average Rank Distance over MLQA-R Dataset XLM-R LaBSE Source Language X-X-mono X-Y Proposed X-X-mono X-Y Proposed ardeeneshivizh Mean |
ICLR.cc/2025/Conference | zkNCWtw2fd | 48 | 6 LIMITATIONS | LaBSE |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 1 | Title | EXECUTION-EVAL: CAN LANGUAGE MODELS EXECUTE REAL-WORLD CODE? |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 2 | Abstract | As language models advance, traditional benchmarks face challenges of datasetsaturation and disconnection from real-world performance, limiting our understanding of true model capabilities. We introduce EXecution-Eval (EXE), abenchmark designed to assess LLMs’ ability to execute code and predict programstates. EXE atte... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 3 | 1 INTRODUCTION | Language model benchmarks are facing challenges of rapid saturation (Ott et al., 2022) and anincreasing disconnect from real-world performance perceived by end-users (Zheng et al., 2023).Due to this, benchmarks are being continually created to address failure modes; e.g. SuperGLUEtargeting GLUE’s low problem difficulty... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 4 | 1 INTRODUCTION | Hence, to maximise an evaluation’s utility we aim to minimise the common failure modes of; a)difficulty, not ensuring an unbound scale of small trivial problems to complex multi-step problems,b) diversity, not ensuring a representative distribution across a large space of problems, c) novelty,not ensuring continually f... |
ICLR.cc/2025/Conference | PwxYoMvmvy | 49 | 5 Conclusions | Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna. On the equivalence between graphisomorphism testing and function approximation with gnns. Advances in neural informationprocessing systems, 32, 2019. |
ICLR.cc/2025/Conference | gtVo4xcpFI | 33 | 57 60 | Apply concepts to new and complex scenarios for generalization. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 5 | 1 INTRODUCTION | Motivated by these challenges we introduce EXecutionEval (EXE), an evaluation replicating oneof the primary tasks humans perform while coding; predicting and comparing a final program statefor a given set of inputs - seen in Figure 1. EXE is designed to avoid the aforementioned failuremodes; emphasising difficulty (smo... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 6 | 1 INTRODUCTION | EXE also holds theoretical inspiration. (Fowler et al., 2022) et al have replicated positive pedagogical correlations found by (Lopez et al., 2008) between the abilities of CS1 students to ”trace”programs (i.e. manually predict outputs and write the internal state out line by line) and their abilities to pass code writ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 7 | 1 INTRODUCTION | Figure 1: An example task from Apache Airflow’s Github repository (code simplified to fit withindiagram). EXE sources tasks from 1,000 Python repositories, generates test cases for them, andcompares the LLM’s ability to execute code against python’s interpreter. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 8 | 2 EVALUATION FRAMEWORK | As seen in Figure 1, an EXE task is to predict a function’s return value or error from: a) a codesnippet and b) a set of input arguments. Code snippets are extracted from PyPi’s most popular 1,000python projects hosted on GitHub, we select our snippets to be pure (i.e. deterministic, no sideeffects), language model gen... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 9 | 2 EVALUATION FRAMEWORK | Through these stages of filtering, the original top 1,000 repositories are filtered down to the 33,875task instances which comprise EXE. A high level breakdown of these task instances across repositories is presented in Figure 3. We note some repositories are overrepresented primarily due to beingmore modern (using typ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 10 | 2 EVALUATION FRAMEWORK | Figure 2: Three stage EXE task generation pipeline. Detailed example tasks and generated inputscan be found in Appendix A.1. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 11 | 2 EVALUATION FRAMEWORK | Figure 3: We observe task counts per repository to have a near logarithmic falloff. Note: Basedon manual observations, several repositories are removed from EXE due to thousands of similarfunctions with only single modifications, for example changing a url address. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 12 | 2.1 TASK FORMATION | Model input. The model is given a complete snippet of code alongside the input state to be executed.The model is then tasked to predict the resulting return value, or in the case that an exception is raisedthe model is instructed to generate an exception type and value. In practice, we prompt modelswith an odata json r... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 13 | 2.1 TASK FORMATION | Evaluation metrics. To evaluate a proposed solution, we use the pass@k metric (Chen et al., 2021),comparing the ground truth and the generated prediction as json objects (set and frozensetare sorted before conversion to json lists). If the original code produced an exception, we comparethe type and message (excluding s... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 14 | 2.2 FEATURES OF EXE | Diversity of inputs and outputs. Unlike many benchmarks focused on a particular subject matterarea, a task in this eval may require a model to perform mathematical reasoning, logical inference,bit manipulation, string operations, loop execution, or to maintain multiple internal variables duringcomputation. Furthermore,... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 15 | 2.2 FEATURES OF EXE | Continually updatable. Both our code collection and task input generation processes can createnew tasks with minimal human oversight. Simply re-running our code collection to pull the latest commits or directing it towards an uncollected Python GitHub repository will create new task instances. Furthermore we can contin... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 16 | 2.2 FEATURES OF EXE | Cost effective scalability. With generation of new tasks requiring an average of 1,112 input tokens(batch of 15) and evaluation of tasks typically requiring 1,123 tokens, ExecEval can be generated,tested and continually updated at a fraction of the cost of human-curated benchmarks. Our initialdataset of 33,875 cases ha... |
ICLR.cc/2025/Conference | PwxYoMvmvy | 50 | 5 Conclusions | Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. Adaptive universal generalized pagerank graph neural network. In International Conference on Learning Representations, 2020. |
ICLR.cc/2025/Conference | PwxYoMvmvy | 51 | 5 Conclusions | Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi. On provable benefits of depth in traininggraph convolutional networks. Advances in Neural Information Processing Systems, 34:9936–9949, 2021. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 17 | 2.2 FEATURES OF EXE | Long multi-step problems with smooth difficulty scaling. We provide a continuous spectrumof task difficulties, ranging from 1-step, one-line functions to multi-file, multi-class, multi-100step tasks. Our most complex tasks include function call depths (non-recursive) of up to 13 levels(median: 2), separate identifier c... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 18 | 2.2 FEATURES OF EXE | To address this, we observe a mechanism inspired by the SKILL-MIX evaluation (Yu et al., 2023)that leverages the typed nature of our function selection process. This approach allows us to create even more complex tasks by chaining functions where the output type of one matches the inputtype of another, or by combining ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 19 | 2.2 FEATURES OF EXE | Error prediction. To test the full spectrum of code execution we further generate test cases designedto trigger exceptions. Many of these require in-depth analysis to see ahead of time, for examplepredicting an invalid array index through multiple functions. While debugging exceptions is oneof the more challenging soft... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 20 | 3 RESULTS | We report our evaluation results across different SOTA models alongside our findings across different task statistics below. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 21 | 3 RESULTS | Model |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 22 | 3 RESULTS | Table 1: EXE Pass@1 results GPT-4oGPT-4o-miniLlama3.1-8BLlama3.1-405BClaude3.5-SonnetMistral-Large-2407 LLMs can execute real-world code, achieving results in-line with code generation benchmarks.We find EXE shows similar relative model performance between models as seen in coding benchmarks such as HumanEval (Chen et ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 23 | 3 RESULTS | EXE dataset (Pass@1) Errors (Pass@1) |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 24 | 3 RESULTS | Prior works such as Learning To Execute (Zaremba & Sutskever, 2014) and CRUX-Eval (Gu et al.,2024) have placed justifiable limitations on code complexity; removing mathematical operations,limiting line count, disallowing custom classes and only having one singular function to name a few.We hypothesised that these are n... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 25 | 3 RESULTS | Figure 4: Left - We show the relative accuracy of different models across the top 20 packages by taskcount. Both the relative differences between models and the relative differences between packagesare within expectations from other coding benchmarks (Jimenez et al., 2023). Right - We show themagnitude of diversity acr... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 26 | 3 RESULTS | ExecEval provides a smooth curve of task difficulties. We set out to ensure a) our evaluationdoes not induce saturation from a bounded distribution of task difficulties, b) our evaluation doesnot induce an ”AI overhang” by not having a smooth transition between difficulties and, c) thecorrelated factors affecting diffi... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 27 | 3 RESULTS | As shown in Figure 5 several task statistics such as ”lines of code”, ”processing time” and ”numberof function calls” all correlate log-linearly with a model’s achieved pass@1 score. These correlationsprovide preliminary evidence towards c) as they align with simplistic human intuition, i.e. more linesof code, more com... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 28 | 3 RESULTS | Beyond evaluation-wide difficulty scaling, EXE also demonstrates diversity and varying difficultylevels within individual task sets. Each function has up to 15 generated test cases, allowing us toanalyse variance per task set. To measure execution path diversity, we collect runtime statistics(detailed in Appendix A.6) ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 41 | 4 RELATED WORK | Recent trends in benchmark design have emphasised the importance of diverse, multi-step problemsand agentic capabilities. Works like Jimenez et al. (2023) have introduced benchmarks that requiresolving real world software engineering problems while Zhou et al. (2023) has enabled evaluationof complex instruction followi... |
ICLR.cc/2025/Conference | PwxYoMvmvy | 52 | 5 Conclusions | Nima Dehmamy, Albert-L´aszl´o Barab´asi, and Rose Yu. Understanding the representation power ofgraph neural networks in learning graph topology. Advances in Neural Information ProcessingSystems, 32, 2019. |
ICLR.cc/2025/Conference | gtVo4xcpFI | 34 | 57 60 | Divide the difficulty based on the number of lines of code, type,and design time. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 29 | 3 RESULTS | ExecEval’s test case generation scales. While EXE today includes up to 15 test cases per task, ouranalysis demonstrates EXE’s generation pipeline can scale significantly further without plateauing.As shown in Figure 6, generation of novel test case continues well beyond 300 cases per task whilemaintaining all quality c... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 30 | 3 RESULTS | rate. This aligns with intuition - generating novel, base64 images poses significantly more difficultythan generating diverse string or numeric inputs. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 31 | 3 RESULTS | Figure 5: Pass@1 for all tasks across four of our code metrics. The shaded area represents variance,and the opacity is scaled with count of samples. Processing time is measured in microseconds. |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 32 | 3 RESULTS | Importantly, our token efficiency analysis (right plot) reveals that significant scaling is possiblewithout proportional prompt growth. By randomly selecting and injecting just 60 prior cases intothe generation prompt, we can effectively generate over 1,000 novel cases. This sublinear tokengrowth suggests the potential... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 33 | 3 RESULTS | LLMs struggle with certain coding features. As EXE contains a diverse set of tasks, we areable to observe model performance differing greatly based on coding features used in any task.To illustrate: floating point math operations such as multiplications (GPT-4o: 43 mean Pass@1)significantly increase task difficulty, ho... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 34 | 3 RESULTS | Figure 6: Test case generation analysis across eleven diverse Python functions sourced from popular libraries including Azure, PyTorch, Langchain, and NLTK. Functions range from geometriccomputations (torchvision) to SQL regex (snowflake-python-connector). Left: Cumulative uniquevalidated test cases per generation batc... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 35 | 3 RESULTS | With the above metrics, and those seen in Figure 7, their mean Pass@k decreases as their countincreases. To reduce the risk of our metrics being a proxy for longer problems we show the effectscan still be seen below in Figure 8 after normalisation by lines of code (only lines with executablesyntax tokens are counted). |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 36 | 3 RESULTS | Figure 7: Three examples of high pass@1 rate tasks that contain large amounts of function calls.Left - Charset-normaliser performs 300+ function calls to define ranges of unicode characters uponinitialisation; this constant has little effect on task difficulty but is used frequently and hence appearsin many tasks. Midd... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 37 | 4 RELATED WORK | There is a rich history of work on evaluating language models’ abilities in reasoning, execution,and multi-step problem-solving across various domains. These efforts span from natural languageprocessing to mathematical reasoning, and from code generation to program execution. Our work,EXecution-Eval (EXE), builds upon ... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 38 | 4 RELATED WORK | Code generation benchmarks have been the foundation of evaluating the coding abilities of languagemodels. Works like HumanEval (Chen et al., 2021) and MBPP (Austin et al., 2021) establishedstandardised datasets for assessing code synthesis from natural language descriptions. These effortshave expanded to cover multiple... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 39 | 4 RELATED WORK | Figure 8: Pass@1for all tasks across four of our code metrics normalised by line of code count(limited to GPT models for readability). All four of the above metrics previously showed a negativeimpact as they increased, interestingly we now observe branching statements having little to noimpact and return statements sur... |
ICLR.cc/2025/Conference | viQ1bLqKY0 | 40 | 4 RELATED WORK | The concept of ”learning to execute” itself has a long history, Zaremba & Sutskever (2014) exploredneural networks’ ability to learn and execute simple programs. Graves et al. (2014) constructed thefirst Neural Turing Machines with (Kaiser & Sutskever, 2015; Reed & de Freitas, 2015; Dehghaniet al., 2018) all building f... |
ICLR.cc/2025/Conference | PwxYoMvmvy | 53 | 5 Conclusions | Chenhui Deng, Zichao Yue, and Zhiru Zhang. Polynormer: Polynomial-expressive graph trans former in linear time. arXiv preprint arXiv:2403.01232, 2024. |
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