Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performance fluctuation when disturbing the order of the incorporated constraints. Yet, none of the existing works has systematically investigated this position bias problem in the field of multi-constraint instruction following. To bridge this gap, we design a probing task where we quantitatively measure the difficulty distribution of the constraints by a novel Difficulty Distribution Index (CDDI). Through the experimental results, we find that LLMs are more performant when presented with the constraints in a ``hard-to-easy'' order. This preference can be generalized to LLMs with different architecture or different sizes of parameters. Additionally, we conduct an explanation study, providing an intuitive insight into the correlation between the LLM's attention and constraint orders. Our code and dataset are publicly available at https://github.com/meowpass/PBIF.
Code (1)
Tasks
Instruction FollowingPositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Local Structure Matters Most: Perturbation Study in NLU
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the order of words. In this paper, we investigat…
Natural Language UnderstandingPositionLocal Structure Matters Most: Perturbation Study in NLU
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the order of words. In this paper, we investigat…
Natural Language UnderstandingPositionTranslating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this work, we extend a pointer-generator and …
Question AnsweringSemantic ParsingTable-based Question AnsweringAttention Basin: Why Contextual Position Matters in Large Language Models
The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this positional bias, our extensive experiments revea…
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) utilize multimodal contexts consisting of text, images, or videos to solve various multimodal tasks. However, we find that changing the order of multimodal input can cause the mod…
In-Context LearningQuestion AnsweringSensitivityVisual Question Answering