Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning

• URL: http://arxiv.org/abs/2507.22887v1
• Date: Wed, 30 Jul 2025 17:59:46 GMT
• Title: Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
• Authors: Kwesi Cobbina, Tianyi Zhou,
• Abstract summary: In-context learning (ICL) is a critical emerging capability of large language models (LLMs)<n>This paper investigates an unexplored new positional bias of ICL for the first time.<n>We observe that the predictions and accuracy can drift drastically when the positions of demos, the system prompt, and the user message are varied.
• Score: 19.313795358097483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning (ICL) is a critical emerging capability of large language models (LLMs), enabling few-shot learning during inference by including a few demonstrations (demos) in the prompt. However, it has been found that ICL's performance can be sensitive to the choices of demos and their order. This paper investigates an unexplored new positional bias of ICL for the first time: we observe that the predictions and accuracy can drift drastically when the positions of demos, the system prompt, and the user message in LLM input are varied. We refer to this bias as DEMOS' POSITION IN PROMPT (DPP) bias. We design a systematic evaluation pipeline to study this type of positional bias across classification, question answering, summarization, and reasoning tasks. We introduce two metrics, ACCURACY-CHANGE and PREDICTION-CHANGE, to quantify net gains and output volatility induced by changes in the demos' position. Extensive experiments on ten LLMs from four open-source model families (QWEN, LLAMA3, MISTRAL, COHERE) verify that the bias significantly affects their accuracy and predictions: placing demos at the start of the prompt yields the most stable and accurate outputs with gains of up to +6 points. In contrast, placing demos at the end of the user message flips over 30\% of predictions without improving correctness on QA tasks. Smaller models are most affected by this sensitivity, though even large models remain marginally affected on complex tasks.

Related papers

• Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs [50.29035873837]
  Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training.<n>Long-tail knowledge from specialized domains is often scarce and underrepresented, rarely appearing in the models' memorization.<n>We propose a reinforcement learning-based dynamic uncertainty ranking method for ICL that accounts for the varying impact of each retrieved sample on LLM predictions.
  arXiv  Detail & Related papers  (2024-10-31T03:42:17Z)
• How Susceptible are LLMs to Influence in Prompts? [6.644673474240519]
  Large Language Models (LLMs) are highly sensitive to prompts, including additional context provided therein. We study how an LLM's response to multiple-choice questions changes when the prompt includes a prediction and explanation from another model. Our findings reveal that models are strongly influenced, and when explanations are provided they are swayed irrespective of the quality of the explanation.
  arXiv  Detail & Related papers  (2024-08-17T17:40:52Z)
• LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
  Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science.<n>Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
  arXiv  Detail & Related papers  (2024-07-02T22:23:40Z)
• Eliminating Position Bias of Language Models: A Mechanistic Approach [119.34143323054143]
  Position bias has proven to be a prevalent issue of modern language models (LMs)<n>Our mechanistic analysis attributes the position bias to two components employed in nearly all state-of-the-art LMs: causal attention and relative positional encodings.<n>By eliminating position bias, models achieve better performance and reliability in downstream tasks, including LM-as-a-judge, retrieval-augmented QA, molecule generation, and math reasoning.
  arXiv  Detail & Related papers  (2024-07-01T09:06:57Z)
• Enhancing In-Context Learning via Implicit Demonstration Augmentation [26.78252788538567]
  In-context learning (ICL) enables pre-trained language models to make predictions for unseen inputs without updating parameters. Despite its potential, ICL's effectiveness heavily relies on the quality, quantity, and permutation of demonstrations. In this paper, we tackle this challenge for the first time from the perspective of demonstration augmentation.
  arXiv  Detail & Related papers  (2024-06-27T05:25:46Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs [18.832135309689736]
  Recent advances in large language models (LLMs) have enhanced their ability to process long input contexts. Recent studies show a positional bias in LLMs, demonstrating varying performance depending on the location of useful information. We develop a Position-Aware PAPEFT approach which is composed of a data augmentation technique and an efficient parameter adapter.
  arXiv  Detail & Related papers  (2024-04-01T19:04:17Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction [56.17020601803071]
  Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction. This paper aims to improve the reliability of existing benchmarks by thoroughly investigating and mitigating prompt bias.
  arXiv  Detail & Related papers  (2024-03-15T02:04:35Z)
• Debiasing Multimodal Large Language Models [61.6896704217147]
  Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing. Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image. To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions [5.187383020960245]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in various NLP tasks. Previous works have shown these models are sensitive towards prompt wording, and few-shot demonstrations and their order. This paper investigates sensitivity of LLMs towards the order of options in multiple-choice questions.
  arXiv  Detail & Related papers  (2023-08-22T14:54:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.