| language: | |
| - en | |
| 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. |