metadata

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".

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.