in-context learning

topic11 events

papersTODAY 04:00 UTC

arXiv paper reports in-context learning emerges similarly across modalities

A new arXiv preprint examines few-shot in-context learning, the ability of a model to pick up abstract patterns from examples in its prompt and apply them to new inputs. The authors note this behavior has been studied mainly in large language models trained on next-token prediction, and report that it arises in a convergent way across different modalities. The announcement provides only the abstract, so methodological details are not yet available.

in-context-learning/multimodal/research/arxiv

papersTODAY 04:00 UTC

arXiv Paper Proposes Training-Free Lexical Prompt Compression for LLMs

A new arXiv preprint describes a deterministic, training-free pipeline for shortening the prompts given to large language models by compressing their lexical content. The authors report a Pareto analysis of the trade-offs between compression and task performance across eleven task categories. The work targets the growing cost and context limits caused by long prompts in techniques like chain-of-thought and in-context learning.

prompt-compression/llm/efficiency/arxiv

papersTODAY 04:00 UTC

Data-Efficient Sample Selection for In-Context Learning

A new arXiv paper tackles the problem of choosing which demonstration examples to include in a prompt when using in-context learning with large language models. Because the space of possible example subsets is combinatorially large, the authors propose a data-efficient approach to selecting good combinations without exhaustive search. The work aims to improve how LLMs adapt to new tasks without fine-tuning.

in-context-learning/llm/research/sample-selection

papersTODAY 04:00 UTC

Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction

A new arXiv paper examines how tabular foundation models, which perform well on standard classification and regression tasks, can be adapted to time-to-event (survival) prediction. The authors propose adaptation interfaces to address the difficulty that censored outcomes pose for in-context learning. The work is listed under both machine learning and AI categories on arXiv.

tabular-data/survival-analysis/foundation-models/in-context-learning

papersTODAY 04:00 UTC

arXiv Paper Examines Whether Tabular Foundation Models Still Require Feature Engineering

A new arXiv preprint in machine learning asks whether manual feature engineering remains necessary now that tabular foundation models exist. These models are pretrained across many tabular datasets and applied through in-context learning, which may reduce reliance on hand-crafted features. The abstract frames the question as an open issue for tabular machine learning research.

tabular-data/foundation-models/feature-engineering/in-context-learning

papersTODAY 04:00 UTC

Study Finds Tabular Foundation Models Can Generalize From a Single Table

A new arXiv paper argues that deep tabular models using in-context learning can achieve broader generalization even when trained or conditioned on just one table. This challenges the common assumption that such models need many diverse tables to generalize well. The work focuses on inference-time context rather than weight updates.

tabular-data/in-context-learning/generalization/research

papersSEP 11 04:00 UTC

Activation-Based Active Learning Tested for In-Context Example Selection

A new arXiv paper examines whether internal transformer activations can guide the selection of in-context examples for large language models. The authors note that earlier work applied active learning to in-context sample selection, but without drawing on recent findings about how activations encode information. The study reports mixed results and lessons from testing this activation-based approach.

active-learning/in-context-learning/llm/research

papersSEP 11 04:00 UTC

Test-time training shown to boost in-context learning of nonlinear functions

A new arXiv paper examines test-time training (TTT), a method where selected model parameters are updated before each prediction so the model can adapt to test data. The authors note that while TTT has had empirical success, its theoretical basis is not well understood, and their analysis focuses on how it affects in-context learning of nonlinear functions.

research/test-time-training/in-context-learning/theory

papersSEP 10 04:00 UTC

Can foundation models moderate online content? Comparing instruction- and example-driven policies

A new arXiv paper investigates whether foundation models can apply complex content moderation policies reliably and consistently. The study compares two ways of translating moderation rules into model behavior: conveying them through explicit instructions versus through illustrative examples. The findings are relevant to platforms seeking scalable, automated moderation of online content.

content-moderation/evaluation/foundation-models

papersSEP 10 04:00 UTC

Retrieval-Based In-Context Learning for Detecting Defamatory Posts in GermEval 2026 Shared Task

A team competing in the GermEval 2026 shared task explored retrieval-based in-context learning for identifying social media posts involving criminally relevant defamation. Their paper details the strategies tested and examines the cases where the method falls short of reliable detection.

nlp/hate-speech/benchmark/in-context-learning

papersSEP 10 04:00 UTC

Paper proposes contrastive modeling to align reasoning paths in multimodal in-context learning

A newly released arXiv paper examines a weakness in how multimodal large language models use in-context learning, noting that current methods tend to copy superficial patterns from examples rather than the underlying reasoning process. The authors present a contrastive modeling technique designed to align a model's reasoning path with the logic demonstrated in-context. The approach is intended to improve performance across a range of multimodal tasks by fostering genuine reasoning instead of imitation.

multimodal/in-context-learning/reasoning/llms