---
license: apache-2.0
task_categories:
- text-generation
---
Official implementation of the paper ["Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following"](https://huggingface.co/papers/2502.17204).

Code: https://github.com/meowpass/PBIF

We systematically study **the position bias problem in multi-constraint
instruction following**. Through our experiments, we have the following findings:

- **LLMs prefer to "hard-to-easy" constraint order**
  - existing LLMs can achieve a better following accuracy in multi-constraint instructions when presented with constraints in “hard-to-easy” orders. 
  - This finding can be generalized in both single-round and multi-round scenarios, regardless of the architecture of LLM, the size of LLM’s parameters and the number of constraints.
- **Constraints order affect how the LLMs handle a specific constraint**
  - The "Hard-to-easy" constraint order induces the LLM to pay more attention to the constraint part in the multi-constraint instructions.
  - The LLM’s performance on various constraints is strongly correlated with its attention patterns.

# PBIF Dataset
The dataset consists of single_round inference data and multi_round inference data.
For each of the data, there are 5 fields:
- `prompt`: Synthesized multi-constraint instructions.
- `constraint`: The constraints contained in the instructions.
- `instruction_id_list`: The id of the constraints in the instructions.
- `kwargs`: Corresponding parameters for the constraints, which are only used for evaluation.
- `ranking`: The constraint order of the instruction. (0 indicates the hardest constraint)
It is worth noting that, in multi_round inference data, the `prompt` is the initial instruction, which is more convenient for the user to construct the multi-round dialog data for themselves.