arXiv Machine Learning
Jun 5
Self-Augmenting Retrieval for Diffusion Language Models
arXiv:2606. 06474v1 Announce Type: cross Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel.
A Diagnostic Study of Faithfulness in Diffusion Language Models for Table-to-Text Generation
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arXiv Computation and Language
2d ago
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs New Metrics
The paper critiques generative perplexity (gen‑PPL) as an unreliable metric for evaluating unconditional text generation, showing that it can be minimized by trivial, non‑diverse outputs. The authors demonstrate this by constructing simple samplers that achieve low gen‑PPL yet produce incoherent text, and they propose alternative evaluation tools—including distributional discrepancies, diversity diagnostics, and a compression‑based KL divergence measure—to better assess generative language models. They argue that future progress in diffusion language models should rely on a suite of metrics that capture diverse aspects of generated text quality.
arXiv AI
Jul 14
Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA
arXiv:2605. 17932v2 Announce Type: replace-cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus mainly on autoregressive architectures.
arXiv AI
Jun 9
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
arXiv:2606. 08417v1 Announce Type: cross Abstract: Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling.
arXiv Computation and Language
4d ago
The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering
arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunk...
Hugging Face Trending Papers
Oct 6
Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with diffusion, but changes identifiers, training recipe...
arXiv Computation and Language
Oct 7
Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
arXiv:2610.08716v1 Announce Type: cross Abstract: Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with...
arXiv AI
Oct 5
On the Tip of the Tongue: Why LLMs Hallucinate Answers They Can Decode
arXiv:2603.13911v2 Announce Type: replace Abstract: A language model can give the wrong answer even when the correct answer is decodable from its intermediate states. To study this gap between decoda...
arXiv Machine Learning
Jun 25
Improved Large Language Diffusion Models
arXiv:2606. 25331v1 Announce Type: cross Abstract: Modern large language models are predominantly trained with autoregressive factorization and causal attention.
arXiv AI
Jul 7
Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
arXiv AI
Sep 30
The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models
arXiv:2609.35860v1 Announce Type: cross Abstract: Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors a...
arXiv AI
Oct 15
RL Post-Training Diffusion Language Models Along Their Own Generation Paths
arXiv:2610.17266v1 Announce Type: new Abstract: Diffusion language models are a promising and more efficient alternative to autoregressive models. However, most RL post-training techniques are not ea...