Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
Authors: Yoonna Jang, Jungwoo Lim, Yuna Hur, Dongsuk Oh, Suhyune Son, Yeonsoo Lee, Donghoon Shin, Seungryong Kim, Heuiseok Lim10803-10812
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the abilities to make informative and customized utterances of pre-trained language models, we utilize BART and GPT-2 as well as transformer-based models. We assess their generation abilities with automatic scores and conduct human evaluations for qualitative results. |
| Researcher Affiliation | Collaboration | Yoonna Jang1 , Jungwoo Lim1 , Yuna Hur1 , Dongsuk Oh1, Suhyune Son1, Yeonsoo Lee2, Donghoon Shin2, Seungryong Kim1 , and Heuiseok Lim1 1Department of Computer Science and Engineering, Korea University 2Language AI Lab, NCSOFT |
| Pseudocode | No | The paper describes the model architecture and objective functions but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | In this work, we introduce a new dataset, call For Customized conversation dataset1 (called Fo Cus), that supports knowledge-grounded answers that reflect user s persona. 1http://github.com/pkchat-focus/Fo Cus |
| Open Datasets | Yes | In this work, we introduce a new dataset, call For Customized conversation dataset1 (called Fo Cus), that supports knowledge-grounded answers that reflect user s persona. 1http://github.com/pkchat-focus/Fo Cus |
| Dataset Splits | Yes | We split the collected data into train, valid and test sets. The detailed statistics of our dataset are summarized in Table 2. ... Table 2: Statistics of Fo Cus dataset. # Dialogs Train 11,562 Valid 1,445 Test 1,445 |
| Hardware Specification | Yes | Fine-tuning them on the entire data with 2 epochs takes approximately 10 hours with one RTX-8000 GPU. |
| Software Dependencies | No | The paper states, "We implement the models based on the source code of Hugging Face s transformers (Wolf et al. 2020, 2019)," but does not provide specific version numbers for software dependencies like Hugging Face Transformers, PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | We use a batch size of 4 with a gradient accumulation of 32. Adam optimizer is used, and the learning rate is set as 6.25e-5, where 훽1 = 0.9, 훽2 = 0.999 with linear decay. ... For the utterance generation, we use the nucleus sampling with top-p = 0.9 and sampling temperature with 0.7. The maximum sequence length is set to 20. |