ECon: On the Detection and Resolution of Evidence Conflicts
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_ | 1866929529353666560 |
|---|---|
| author | Jiayang, Cheng Chan, Chunkit Zhuang, Qianqian Qiu, Lin Zhang, Tianhang Liu, Tengxiao Song, Yangqiu Zhang, Yue Liu, Pengfei Zhang, Zheng |
| author_facet | Jiayang, Cheng Chan, Chunkit Zhuang, Qianqian Qiu, Lin Zhang, Tianhang Liu, Tengxiao Song, Yangqiu Zhang, Yue Liu, Pengfei Zhang, Zheng |
| contents | The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or "inter-evidence conflicts." This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. We evaluate conflict detection methods, including Natural Language Inference (NLI) models, factual consistency (FC) models, and LLMs, on these conflicts (RQ1) and analyze LLMs' conflict resolution behaviors (RQ2). Our key findings include: (1) NLI and LLM models exhibit high precision in detecting answer conflicts, though weaker models suffer from low recall; (2) FC models struggle with lexically similar answer conflicts, while NLI and LLM models handle these better; and (3) stronger models like GPT-4 show robust performance, especially with nuanced conflicts. For conflict resolution, LLMs often favor one piece of conflicting evidence without justification and rely on internal knowledge if they have prior beliefs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04068 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ECon: On the Detection and Resolution of Evidence Conflicts Jiayang, Cheng Chan, Chunkit Zhuang, Qianqian Qiu, Lin Zhang, Tianhang Liu, Tengxiao Song, Yangqiu Zhang, Yue Liu, Pengfei Zhang, Zheng Computation and Language Artificial Intelligence The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or "inter-evidence conflicts." This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. We evaluate conflict detection methods, including Natural Language Inference (NLI) models, factual consistency (FC) models, and LLMs, on these conflicts (RQ1) and analyze LLMs' conflict resolution behaviors (RQ2). Our key findings include: (1) NLI and LLM models exhibit high precision in detecting answer conflicts, though weaker models suffer from low recall; (2) FC models struggle with lexically similar answer conflicts, while NLI and LLM models handle these better; and (3) stronger models like GPT-4 show robust performance, especially with nuanced conflicts. For conflict resolution, LLMs often favor one piece of conflicting evidence without justification and rely on internal knowledge if they have prior beliefs. |
| title | ECon: On the Detection and Resolution of Evidence Conflicts |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.04068 |