CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917673983541248 |
|---|---|
| author | Ghaddar, Abbas Alfonso-Hermelo, David Langlais, Philippe Rezagholizadeh, Mehdi Chen, Boxing Parthasarathi, Prasanna |
| author_facet | Ghaddar, Abbas Alfonso-Hermelo, David Langlais, Philippe Rezagholizadeh, Mehdi Chen, Boxing Parthasarathi, Prasanna |
| contents | In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHARP, a diagnostic test set, designed for an improved evaluation of hallucinations in conversational model. CHARP not only measures hallucination but also the compliance of the models to the conversation task. Our extensive analysis reveals that models primarily exhibit poor performance on CHARP due to their inability to effectively attend to and reason over the conversation history. Furthermore, the evaluation methods of FaithDial fail to capture these shortcomings, neglecting the conversational history. Our findings indicate that there is substantial room for contribution in both dataset creation and hallucination evaluation for knowledge-grounded dialogue, and that CHARP can serve as a tool for monitoring the progress in this particular research area. CHARP is publicly available at https://huggingface.co/datasets/huawei-noah/CHARP |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15110 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems Ghaddar, Abbas Alfonso-Hermelo, David Langlais, Philippe Rezagholizadeh, Mehdi Chen, Boxing Parthasarathi, Prasanna Computation and Language In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHARP, a diagnostic test set, designed for an improved evaluation of hallucinations in conversational model. CHARP not only measures hallucination but also the compliance of the models to the conversation task. Our extensive analysis reveals that models primarily exhibit poor performance on CHARP due to their inability to effectively attend to and reason over the conversation history. Furthermore, the evaluation methods of FaithDial fail to capture these shortcomings, neglecting the conversational history. Our findings indicate that there is substantial room for contribution in both dataset creation and hallucination evaluation for knowledge-grounded dialogue, and that CHARP can serve as a tool for monitoring the progress in this particular research area. CHARP is publicly available at https://huggingface.co/datasets/huawei-noah/CHARP |
| title | CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.15110 |