8 papers
Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models
Zili Zhang, Yilin Wang, Heng Wang +2
Large language models (LLMs) can solve complex multi-hop problems yet exhibit puzzling failures on simple two-hop queries: although a model may correctly store each individual hop,…
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts
Zinan Zeng, Sen Ye, Zijian Cai +4
Online movie review websites are valuable for information and discussion about movies. However, the massive spoiler reviews detract from the movie-watching experience, making spoil…
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment
Yizhuo Zhang, Heng Wang, Shangbin Feng +3
Previous research has sought to enhance the graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. While these led to specialized LLMs better at so…
Kimi-VL Technical Report
Kimi Team, Angang Du, Bohong Yin +92
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong…
Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection
Haokai Zhang, Shengtao Zhang, Zijian Cai +4
Spoilers in movie reviews are important on platforms like IMDb and Rotten Tomatoes, offering benefits and drawbacks. They can guide some viewers' choices but also affect those who…
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
Ruiqi Zhong, Heng Wang, Dan Klein +1
To make sense of massive data, we often fit simplified models and then interpret the parameters; for example, we cluster the text embeddings and then interpret the mean parameters…