KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning
Authors: Peng Yu, Cheng Deng, beiya dai, Xinbing Wang, Ying Wen


Reviewer paper summaries: The paper proposes a novel method for knowledge graph completion using language models. Specifically, it involves training language models to predict the tails of tuples and introduces contrastive learning to explicitly enhance representation learning.
Summary of strengths: The paper is good writing and easy to follow.
Summary of weaknesses: My main concern is the performance. In Table 1, the proposed method does not outperform some baselines, such as simKGC and C-LMKE, in most settings. This raises questions about the effectiveness of using a language model for this task, considering that simKGC, which relies solely on contrastive learning, achieves better performance compared to the proposed method.
Comments, suggestions and typos: - What are the low-direction and column-direction losses in Equation 1?
- The training objective comprises two different losses: contrastive loss and implicit knowledge alignment loss. Could you train your model without the implicit knowledge alignment loss? This would help determine whether language modelling is necessary.
Overall assessment scores: 2.5