Papers with ISA
ISA: An Intelligent Shopping Assistant (2020.aacl-demo)
Copied to clipboard
| Challenge: | In-store users only need to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. |
| Approach: | They present a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. |
| Outcome: | The proposed system can improve shopping experience in physical stores by leveraging advanced techniques in computer vision, speech processing, and natural language processing. |
Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures (2024.findings-naacl)
Copied to clipboard
| Challenge: | Applying deep learning to binary code analysis has drawn great attention because of its notable performance. |
| Approach: | They propose to learn cross-architecture instruction embeddings where semantically-similar instructions have close embeddements in a shared space. |
| Outcome: | The proposed approach generates high-quality CAIE with good transferability on four ISAs. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
Copied to clipboard
| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Using deep learning to detect malware has attracted great attention due to its notable performance. |
| Approach: | a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection. |
| Outcome: | The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs. |
Reasoning Requirements for Indirect Speech Act Interpretation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing systems that pretrain word and sentence embeddings to account for nearby linguistic context are unclear how to integrate extra-linguistic context into NLU. |
| Approach: | They perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. |
| Outcome: | The proposed model is based on the domain-general patterns of reasoning involved and implements Answer Set programming. |
Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees (2025.findings-emnlp)
Copied to clipboard
| Challenge: | ISA-centric transpilation pipelines are used to translate low-level programs between ISAs . GG provides high code coverage across unit tests and better energy efficiency . |
| Approach: | They propose a ISA-centric transpilation pipeline that embeds large language models into software testing frameworks to ensure accuracy. |
| Outcome: | The proposed method achieves high code coverage across unit tests and functional/semantic correctness of 99% on HumanEval and 49% on BringupBench programs. |
CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark (2026.acl-long)
Copied to clipboard
Ahmed Heakl, Gustavo Bertolo Stahl, Sarim Hashmi, Seung Hun Eddie Han, Mukul Ranjan, Arina Kharlamova, Salman Khan, Abdulrahman Mahmoud
| Challenge: | Cross-architecture GPU code translation is essential for unlocking low-level hardware portability, yet no scalable solution exists. |
| Approach: | They propose a dataset and model suite for source- and assembly-level GPU code translation that trains domain-specific translation models that achieve 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 . |
| Outcome: | The proposed model achieves 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 outperforming commercial baselines including GPT-5.1, Claude-4.5, and Hipify by wide margins. |