Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04053 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918193104158720 |
|---|---|
| author | Takagi, Hirohane Minegishi, Gouki Kizawa, Shota Sukeda, Issey Yanaka, Hitomi |
| author_facet | Takagi, Hirohane Minegishi, Gouki Kizawa, Shota Sukeda, Issey Yanaka, Hitomi |
| contents | Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions:(1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2)How does irrelevant numerical context perturb these representations and their downstream outputs? To address these questions, we combine linear probing with partial correlation analysis and prompt-based vulnerability tests across models of varying sizes. Our results show that LLMs encode real-world numerical correlations but tend to systematically amplify them. Moreover, irrelevant context induces consistent shifts in magnitude representations, with downstream effects that vary by model size. These findings reveal a vulnerability in LLM decision-making and lay the groundwork for fairer, representation-aware control under multi-attribute entanglement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpreting Multi-Attribute Confounding through Numerical Attributes in Large Language Models Takagi, Hirohane Minegishi, Gouki Kizawa, Shota Sukeda, Issey Yanaka, Hitomi Artificial Intelligence Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions:(1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2)How does irrelevant numerical context perturb these representations and their downstream outputs? To address these questions, we combine linear probing with partial correlation analysis and prompt-based vulnerability tests across models of varying sizes. Our results show that LLMs encode real-world numerical correlations but tend to systematically amplify them. Moreover, irrelevant context induces consistent shifts in magnitude representations, with downstream effects that vary by model size. These findings reveal a vulnerability in LLM decision-making and lay the groundwork for fairer, representation-aware control under multi-attribute entanglement. |
| title | Interpreting Multi-Attribute Confounding through Numerical Attributes in Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.04053 |