Papers with CAD
How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? (2021.emnlp-main)
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| Challenge: | Recent studies have shown that models trained on CAD can learn cues in the dataset which are spuriously correlated with the construct. |
| Approach: | They focus on sentiment, sexism, and hate speech as social constructs to investigate their effects on model performance. |
| Outcome: | The proposed model generalizes better on out-of-domain datasets while relying less on spurious features. |
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding (2024.naacl-short)
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| Challenge: | Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. |
| Approach: | They propose a context-aware decoding technique that amplifies the difference between the output probabilities when a model is used with and without context. |
| Outcome: | The proposed model significantly improves faithfulness of different LM families including OPT, GPT, LLaMA, and FLAN-T5 for summarization tasks. |
AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)
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| Challenge: | Existing methods for generating counterfactuals rely on human efforts or task-specific designs. |
| Approach: | They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals. |
| Outcome: | The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks. |
An Investigation of the (In)effectiveness of Counterfactually Augmented Data (2022.acl-long)
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| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
| Outcome: | The proposed model can learn robust features that are invariant under distribution shifts, but lacks spurious correlations, and may exacerbate existing correlations. |
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection (2022.naacl-main)
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| Challenge: | sexism and hate speech detection models may be over-relying on core features . construct-driven CAD may induce models to ignore context in which core features are used . |
| Approach: | They propose to use construct-driven and construct-agnostic CAD to reduce model bias . sexism and hate speech detection models are trained on counterfactually augmented data . |
| Outcome: | Using a diverse set of CAD—construct-driven and construct-agnostic—reduces unintended bias. |
CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction (2025.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions. |
| Approach: | They propose a CAD review task to automatically detect and correct potential errors . they propose CAD program repairer framework to provide helpful feedback on error correction . |
| Outcome: | The proposed framework outperforms existing MLLMs in detecting errors and providing feedback on error correction. |
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
Improving Grammatical Error Correction by Correction Acceptability Discrimination (2024.lrec-main)
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| Challenge: | Existing Grammatical Error Correction (GEC) methods overlook the assessment of sentence-level syntax and semantics in the corrected sentence. |
| Approach: | They propose a correction acceptance discrimination task to assess sentence-level syntax and semantics in corrected sentences and a pipeline method to remove invalid corrections. |
| Outcome: | The proposed method improves F0.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set. |
CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards (2026.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping complex 3D objects . designers manually define assembly sequences for individual CAD parts . |
| Approach: | They propose a framework for computer-aided design that predicts actions for CAD parts . they use a reference design image and disassembled parts to generate 6-DoF transformations . |
| Outcome: | The proposed framework outperforms existing MLLMs in the design of CAD assemblies. |
TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable advances in enabling language agents to tackle real-world tasks. |
| Approach: | They propose a tool-using agent-based CAD framework that automates text-to-CAD modeling . they propose an interactive CAD gym to roll out reasoning and tool-augmented interaction trajectories with the CAD engine . |
| Outcome: | The proposed framework can generalize across complex modeling tasks, supporting their open-source counterparts. |
mrCAD: Multimodal Communication to Refine Computer-aided Designs (2025.findings-emnlp)
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William P McCarthy, Saujas Vaduguru, Karl D.d. Willis, Justin Matejka, Judith E Fan, Daniel Fried, Yewen Pu
| Challenge: | generative AI excels at creating artifacts in a single turn but can struggle to make precise refinements that match our design intent. |
| Approach: | They propose to use multi-turn interactions to iterate and refine computer-aided designs (CADs) they use text and drawing to communicate with each other over multiple rounds of interaction . |
| Outcome: | mrCAD consists of 6,082 communication games, 15,163 instruction-execution rounds, played between 1,092 pairs of humans. |