Papers with Blender
FORG3D: Flexible Object Rendering for Generating Vision-Language Spatial Reasoning Data from 3D Scenes (2025.acl-demo)
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| Challenge: | FORG3D synthesizes vision-language data for use in cognitive experiments . it provides precise control over object placement, orientation, and camera configurations . |
| Approach: | They propose a 3D rendering toolkit that synthesizes vision-language data with Blender and Python. |
| Outcome: | The toolkit synthesizes vision-language data for two primary purposes: supporting cognitive experiments and improving visual reasoning capabilities of large vision-linguistic models. |
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
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| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (2022.findings-emnlp)
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| Challenge: | Existing methods to detect toxic generation of pretrained language models rely on templates, data extraction, crowdsourcing workers or automatic generation. |
| Approach: | They propose a method to construct adversarial contexts conditioned on a given response . they augment existing dataset BAD+ and construct a new dataset B AD+ . |
| Outcome: | The proposed method can detect toxic or biased content in large pretrained language models. |
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity (2021.acl-long)
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| Challenge: | We analyze 2,500 phrasings related to the intent of “Are you a robot?” and 2,500 adversarially selected utterances to determine whether systems are non-human. |
| Approach: | They analyze 2,500 phrasings related to the intent of "Are you a robot?" and 2,500 adversarially selected utterances to determine whether systems are non-human. |
| Outcome: | The proposed model and two systems fail to confirm non-human intent, and the proposed model is complex. |