From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning
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_ | 1866911108668850176 |
|---|---|
| author | Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David |
| author_facet | Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David |
| contents | Instruction-tuned large language models (LLMs) have shown strong performance on a variety of tasks; however, generalizing from synthetic to human-authored instructions in grounded environments remains a challenge for them. In this work, we study generalization challenges in spatial grounding tasks where models interpret and translate instructions for building object arrangements on a $2.5$D grid. We fine-tune LLMs using only synthetic instructions and evaluate their performance on a benchmark dataset containing both synthetic and human-written instructions. Our results reveal that while models generalize well on simple tasks, their performance degrades significantly on more complex tasks. We present a detailed error analysis of the gaps in instruction generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David Computation and Language Instruction-tuned large language models (LLMs) have shown strong performance on a variety of tasks; however, generalizing from synthetic to human-authored instructions in grounded environments remains a challenge for them. In this work, we study generalization challenges in spatial grounding tasks where models interpret and translate instructions for building object arrangements on a $2.5$D grid. We fine-tune LLMs using only synthetic instructions and evaluate their performance on a benchmark dataset containing both synthetic and human-written instructions. Our results reveal that while models generalize well on simple tasks, their performance degrades significantly on more complex tasks. We present a detailed error analysis of the gaps in instruction generalization. |
| title | From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.14425 |