arXiv AI
AnyBottle: A Recipe to Only Keep the Concepts You Really Need
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arXiv AI
Oct 9
Towards Reasonable Concept Bottleneck Models
The paper introduces CREAMs, a flexible framework for Concept Bottleneck Models that allows explicit encoding of concept‑concept and concept‑task relationships such as mutual exclusivity, hierarchy, and correlations. CREAMs can optionally use a regularized side‑channel to compensate for incomplete concept sets, maintaining competitive performance while encouraging concept‑grounded predictions. The authors also propose a new metric to assess interpretability when predictions partially rely on the side‑channel, and demonstrate that CREAMs support efficient interventions, avoid concept leakage, and match black‑box performance even with missing concepts.
arXiv Machine Learning
Jun 16
Assessing Reliability of Symbol Detection in Concept Bottleneck Models
arXiv:2606. 16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols.
arXiv AI
2d ago
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification
arXiv:2610.16182v1 Announce Type: new Abstract: Flagship large language models (LLMs) offer rich general knowledge and strong reasoning capabilities, but their high inference cost and latency limit t...
arXiv AI
Jun 2
Uncovering Competency Gaps in Large Language Models and Their Benchmarks
arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.
arXiv Computation and Language
Sep 11
NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction
NCP-ArchPreview is a latent‑space language model that extends standard next‑token prediction (NTP) with a Next Concept Prediction (NCP) objective, allowing the model to predict discrete concepts spanning multiple tokens. The architecture builds a product‑quantized concept vocabulary from hidden states, uses a dedicated Concept Module to forecast future concepts, and feeds these predictions back to guide token‑level generation, all trained jointly end‑to‑end. Trained on 5.73 T tokens with 8.9 B parameters, it achieves the final pretraining loss of OLMo‑3‑7B using only 51.3 % of the tokens, outperforms OLMo‑3‑7B on downstream tasks (including a 5.99‑point GSM8K gain), and demonstrates that the learned latent space enables lightweight domain adaptation and improved drafting performance.
arXiv AI
Jun 9
A Geometric Unification of Concept Learning with Concept Cones
arXiv:2512. 07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge.