Related papers: In-context Example Selection with Influences

Related papers

Order Matters: Rethinking Prompt Construction in In-Context Learning [52.19217980839306]
In-context learning (ICL) enables large language models to perform new tasks by conditioning on a sequence of examples.<n>Most prior work assumes that which examples are chosen has a far greater effect on performance than how those examples are ordered.<n>We revisit this assumption and conduct a systematic comparison between the effect of selection and ordering.
arXiv Detail & Related papers (2025-11-12T19:57:55Z)
When Many-Shot Prompting Fails: An Empirical Study of LLM Code Translation [0.23332469289621782]
Large Language Models (LLMs) with vast context windows offer new avenues for in-context learning (ICL)<n>We systematically evaluate the impact of scaling in-context examples from zero-shot to many-shot configurations of up to 625 examples.<n>Our findings reveal a "many-shot paradox": while static similarity metrics may modestly improve with more examples, functional correctness consistently peaks with few-shot prompting.
arXiv Detail & Related papers (2025-10-19T12:29:13Z)
Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples [3.4511221986774516]
Large Language Models exhibit powerful few-shot in-context learning (ICL) capabilities, but the performance is highly sensitive to provided examples.<n>Recent research has focused on retrieving corresponding examples for each input query.<n>We propose a novel method that utilizes Negative samples to better select Positive sample examples.
arXiv Detail & Related papers (2025-07-31T03:06:27Z)
Bridging the Gap Between Preference Alignment and Machine Unlearning [16.24082027914431]
We propose a framework to explore the relationship between Preference Alignment for Large Language Models and Reinforcement Learning with Human Feedback. Our analysis reveals that not all negative examples contribute equally to alignment improvement when unlearned, and the effect varies significantly across examples. We propose a framework called Unlearning to Align, which leverages bi-level optimization to efficiently select and unlearn examples for optimal PA performance.
arXiv Detail & Related papers (2025-04-09T07:49:08Z)
In-Context Learning with Long-Context Models: An In-Depth Exploration [96.1389740719691]
We show that, for many datasets with large label spaces, performance continues to increase with hundreds or thousands of demonstrations. We show that although long-context ICL can be surprisingly effective, most of this gain comes from attending back to similar examples.
arXiv Detail & Related papers (2024-04-30T21:06:52Z)
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities [15.931776592470895]
Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) This paper proposes a novel Bayesian in-Context example Selection method (ByCS) for ICL.
arXiv Detail & Related papers (2024-04-23T03:42:48Z)
ParaICL: Towards Robust Parallel In-Context Learning [74.38022919598443]
Large language models (LLMs) have become the norm in natural language processing. Few-shot in-context learning (ICL) relies on the choice of few-shot demonstration examples. We propose a novel method named parallel in-context learning (ParaICL)
arXiv Detail & Related papers (2024-03-31T05:56:15Z)
$Se^2$: Sequential Example Selection for In-Context Learning [83.17038582333716]
Large language models (LLMs) for in-context learning (ICL) need to be activated by demonstration examples. Prior work has extensively explored the selection of examples for ICL, predominantly following the "select then organize" paradigm. In this paper, we formulate the problem as a $Se$quential $Se$lection problem and introduce $Se2$, a sequential-aware method.
arXiv Detail & Related papers (2024-02-21T15:35:04Z)
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning [32.29118942982609]
Large Language Models (LLMs) have recently gained the In-Context Learning (ICL) ability with the models scaling up. This paper investigates how to determine approximately optimal weights for demonstration examples and how to apply them during ICL. Experimental results on 8 text classification tasks show that our approach outperforms conventional ICL by a large margin.
arXiv Detail & Related papers (2023-10-12T13:15:11Z)
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning [53.52699766206808]
We propose Retrieval for In-Context Learning (RetICL), a learnable method for modeling and optimally selecting examples sequentially for in-context learning. We evaluate RetICL on math word problem solving and scientific question answering tasks and show that it consistently outperforms or matches and learnable baselines.
arXiv Detail & Related papers (2023-05-23T20:15:56Z)
Active Learning Principles for In-Context Learning with Large Language Models [65.09970281795769]
This paper investigates how Active Learning algorithms can serve as effective demonstration selection methods for in-context learning. We show that in-context example selection through AL prioritizes high-quality examples that exhibit low uncertainty and bear similarity to the test examples.
arXiv Detail & Related papers (2023-05-23T17:16:04Z)
Compositional Exemplars for In-context Learning [21.961094715261133]
Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability. We propose CEIL (Compositional Exemplars for In-context Learning) to model the interaction between the given input and in-context examples. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, paraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation, and semantic parsing.
arXiv Detail & Related papers (2023-02-11T14:02:08Z)
Investigating the Role of Negatives in Contrastive Representation Learning [59.30700308648194]
Noise contrastive learning is a popular technique for unsupervised representation learning. We focus on disambiguating the role of one of these parameters: the number of negative examples. We find that the results broadly agree with our theory, while our vision experiments are murkier with performance sometimes even being insensitive to the number of negatives.
arXiv Detail & Related papers (2021-06-18T06:44:16Z)
RelatIF: Identifying Explanatory Training Examples via Relative Influence [13.87851325824883]
We use influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine learning model. We introduce RelatIF, a new class of criteria for choosing relevant training examples by way of an optimization objective that places a constraint on global influence. In empirical evaluations, we find that the examples returned by RelatIF are more intuitive when compared to those found using influence functions.
arXiv Detail & Related papers (2020-03-25T20:59:54Z)

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