JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911179405787136 |
|---|---|
| author | Yao, Louie Hong Jarvis, Nicholas Zhan, Tiffany Ghosh, Saptarshi Liu, Linfeng Jiang, Tianyu |
| author_facet | Yao, Louie Hong Jarvis, Nicholas Zhan, Tiffany Ghosh, Saptarshi Liu, Linfeng Jiang, Tianyu |
| contents | Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response framework that embeds both LLMs and questions in a shared space. For question embeddings, the direction encodes semantics and the norm encodes difficulty, while correctness on each question is determined by the geometric interaction between the model and question embeddings. This geometry replaces a global ranking of LLMs with topical specialization and enables smooth variation across related questions. Building on this framework, our experimental results reveal that out-of-distribution behavior can be explained through directional alignment, and that larger norms consistently indicate harder questions. Moreover, JE-IRT naturally supports generalization: once the space is learned, new LLMs are added by fitting a single embedding. The learned space further reveals an LLM-internal taxonomy that only partially aligns with human-defined subject categories. JE-IRT thus establishes a unified and interpretable geometric lens that connects LLM abilities with the structure of questions, offering a distinctive perspective on model evaluation and generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory Yao, Louie Hong Jarvis, Nicholas Zhan, Tiffany Ghosh, Saptarshi Liu, Linfeng Jiang, Tianyu Artificial Intelligence Computation and Language Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature. We present JE-IRT, a geometric item-response framework that embeds both LLMs and questions in a shared space. For question embeddings, the direction encodes semantics and the norm encodes difficulty, while correctness on each question is determined by the geometric interaction between the model and question embeddings. This geometry replaces a global ranking of LLMs with topical specialization and enables smooth variation across related questions. Building on this framework, our experimental results reveal that out-of-distribution behavior can be explained through directional alignment, and that larger norms consistently indicate harder questions. Moreover, JE-IRT naturally supports generalization: once the space is learned, new LLMs are added by fitting a single embedding. The learned space further reveals an LLM-internal taxonomy that only partially aligns with human-defined subject categories. JE-IRT thus establishes a unified and interpretable geometric lens that connects LLM abilities with the structure of questions, offering a distinctive perspective on model evaluation and generalization. |
| title | JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.22888 |