TaxoSAE: Rethinking Sparse Autoencoders through Taxonomic Logic
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...
arXiv:2606. 24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge.
The paper introduces SAE‑IDF, a data‑driven approach that uses sparse autoencoders to encode symbols into interpretable sparse codes, re‑weights dimensions by rarity, and matches units across heterogeneous symbolic systems. On 12 tasks spanning six symbolic system types, SAE‑IDF outperforms dense LLM baselines (Gemma‑2‑9B and Llama‑3.1‑8B) by up to 45 points, consistently across layers and configurations. Each sparse dimension carries a human‑readable meaning, allowing matches to decompose into named concepts, thereby enabling a scalable, universal symbolic backbone.
AlignSAE is a method that aligns Sparse Autoencoder (SAE) features with a predefined ontology using a two‑stage curriculum: an initial unsupervised pre‑train followed by supervised post‑train that binds specific concepts to dedicated latent slots. This approach preserves general reconstruction capacity while creating an interpretable interface for inspecting and controlling individual concepts. Experiments on GPT‑2 show that AlignSAE enables precise causal interventions—such as reliable concept swaps with 94.8% success in a 1‑hop QA task—and achieves 9–∞× better disentanglement than standard SAEs, as measured by Mutual Information Gap, while supporting multi‑hop reasoning and probing generalization dynamics.
arXiv:2609.01564v1 Announce Type: cross Abstract: Large language models (LLMs) struggle to classify text into taxonomies with many semantically similar labels, as the distinctions are domain-specific...
The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.