Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation

• URL: http://arxiv.org/abs/2506.13608v1
• Date: Mon, 16 Jun 2025 15:35:41 GMT
• Title: Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation
• Authors: Debanjan Dutta, Faizanuddin Ansari, Swagatam Das,
• Abstract summary: In this article, we present the performance of ICL on partially ordered relation by introducing the notion of inductively increasing complexity in prompts.<n>In most cases, the saturated performance of the chosen metric indicates that while ICL offers some benefits, its effectiveness remains constrained as we increase the complexity in the prompts even in presence of sufficient demonstrative examples.
• Score: 14.904264782690639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating rational and generally accurate responses to tasks, often accompanied by example demonstrations, highlights Large Language Model's (LLM's) remarkable In-Context Learning (ICL) capabilities without requiring updates to the model's parameter space. Despite having an ongoing exploration focused on the inference from a document-level concept, its behavior in learning well-defined functions or relations in context needs a careful investigation. In this article, we present the performance of ICL on partially ordered relation by introducing the notion of inductively increasing complexity in prompts. In most cases, the saturated performance of the chosen metric indicates that while ICL offers some benefits, its effectiveness remains constrained as we increase the complexity in the prompts even in presence of sufficient demonstrative examples. The behavior is evident from our empirical findings and has further been theoretically justified in term of its implicit optimization process. The code is available \href{https://anonymous.4open.science/r/ICLonPartiallyOrderSet}{here}.

Related papers

• Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization [26.9153121765435]
  Large language models (LLMs) have demonstrated remarkable in-context learning abilities.<n>This paper investigates how ICL emerges and the impact of pre-training phase on ICL.<n>Our theory is supported by experiments on numerical linear dynamic systems, synthetic GINC and real-world language datasets.
  arXiv  Detail & Related papers  (2025-02-24T10:26:29Z)
• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning.<n>We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.<n>We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Eliciting Causal Abilities in Large Language Models for Reasoning Tasks [14.512834333917414]
  We introduce the Self-Causal Instruction Enhancement (SCIE) method, which enables LLMs to generate high-quality, low-quantity observational data.<n>In SCIE, the instructions are treated as the treatment, and textual features are used to process natural language.<n>Our method effectively generates instructions that enhance reasoning performance with reduced training cost of prompts.
  arXiv  Detail & Related papers  (2024-12-19T17:03:02Z)
• On the Loss of Context-awareness in General Instruction Fine-tuning [101.03941308894191]
  We investigate the loss of context awareness after supervised fine-tuning.<n>We find that the performance decline is associated with a bias toward different roles learned during conversational instruction fine-tuning.<n>We propose a metric to identify context-dependent examples from general instruction fine-tuning datasets.
  arXiv  Detail & Related papers  (2024-11-05T00:16:01Z)
• Implicit In-context Learning [37.0562059811099]
  We introduce Implicit In-context Learning (I2CL), an innovative paradigm that reduces the inference cost of ICL to that of zero-shot learning with minimal information loss.<n>I2CL achieves few-shot level performance at zero-shot inference cost, and it exhibits robustness against variations in demonstration examples.
  arXiv  Detail & Related papers  (2024-05-23T14:57:52Z)
• ParaICL: Towards Parallel In-Context Learning [74.38022919598443]
  Large language models (LLMs) have become the norm in natural language processing.<n>Few-shot in-context learning (ICL) relies on the choice of few-shot demonstration examples.<n>We propose a novel method named parallel in-context learning (ParaICL)
  arXiv  Detail & Related papers  (2024-03-31T05:56:15Z)
• TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning [37.09785060896196]
  We propose textbfTEGEE (Task Definition Guided Expert Ensembling), a method that explicitly extracts task definitions.<n>Our framework employs a dual 3B model approach, with each model assigned a distinct role.<n> Empirical evaluations show that TEGEE performs comparably to the larger LLaMA2-13B model.
  arXiv  Detail & Related papers  (2024-03-07T05:26:41Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction [12.673710691468264]
  We introduce the Heuristic-Driven Link-of- Analogy (HD-LoA) prompting to address the challenge of example selection. Inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations. Experiments show that our method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets.
  arXiv  Detail & Related papers  (2023-11-11T12:05:01Z)
• Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition [4.7938839332508945]
  We propose a Prompt-based Logical Semantics Enhancement (PLSE) method for Implicit Discourse Relation Recognition (IDRR) Our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-01T08:38:08Z)
• Explaining Emergent In-Context Learning as Kernel Regression [61.57151500616111]
  Large language models (LLMs) have initiated a paradigm shift in transfer learning. In this paper, we investigate the reason why a transformer-based language model can accomplish in-context learning after pre-training. We find that during ICL, the attention and hidden features in LLMs match the behaviors of a kernel regression.
  arXiv  Detail & Related papers  (2023-05-22T06:45:02Z)
• 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)
• Learning Implicitly with Noisy Data in Linear Arithmetic [94.66549436482306]
  We extend implicit learning in PAC-Semantics to handle intervals and threshold uncertainty in the language of linear arithmetic. We show that our implicit approach to learning optimal linear programming objective constraints significantly outperforms an explicit approach in practice.
  arXiv  Detail & Related papers  (2020-10-23T19:08:46Z)

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.