GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.
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TLDR9+: A large scale resource for extreme summarization of social media posts
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A fitted iso-depth scaling law measures that one recurrence in looped transformers is worth r^0.46 unique blocks in validation loss.
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
MultiMat shows multimodal large models plus constrained search produce higher-quality procedural material graphs than text-only baselines on a new production dataset.
ProMQA-Assembly is a new multimodal procedural QA dataset with 646 pairs on assembly activities, built via LLM-generated candidates verified by humans plus 81 task graphs, and used to benchmark multimodal models.
LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
Controlled experiments on MNIST show human soft-labels act as a regularizer that improves calibration on hard samples and aligns model uncertainty with humans, beyond accuracy gains from correcting mislabels.
DPUA is a two-phase framework that aligns LLM uncertainty expressions with human disagreement distributions in subjectivity analysis while preserving task performance.
LLMs exhibit pseudo-deliberation, with consistent value-action misalignment in generated dialogues despite reasoning, as measured by the new VALDI framework across 4941 scenarios.
Latent-GRPO stabilizes reinforcement learning in latent space, delivering 7.86 Pass@1 gains on low-difficulty tasks over latent baselines and 4.27 points over explicit GRPO on high-difficulty tasks with 3-4x shorter reasoning chains.
LCF detects multiple LLM runtime threats by computing aggregated diagonal Mahalanobis distances on layer-wise hidden-state differences, calibrated on clean examples, achieving high detection rates with low overhead across several model architectures.
A context-aware Sentinel-Strategist system for RAG selectively applies defenses to block membership inference and data poisoning while recovering most retrieval utility compared to always-on defense stacks.
Token-level contrastive attribution yields informative signals for some LLM benchmark failures but is not universally applicable across datasets and models.
Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.
HyEm maps radius-controlled hyperbolic ontology embeddings to Euclidean space for ANN indexing and applies query-adaptive hyperbolic reranking to improve hierarchy-aware retrieval while preserving most Euclidean performance on flat queries.
CodeT5+ is a flexible encoder-decoder LLM family for code pretrained with diverse objectives on multilingual corpora and initialized from existing LLMs, achieving state-of-the-art results on code generation, completion, math programming, and retrieval tasks including new SoTA on HumanEval with the 1
Sparrow uses targeted rule-based human feedback and evidence provision to outperform baselines in preference while violating rules only 8% of the time under adversarial probing.
Re-evaluating controlled text generation systems under standardized conditions reveals that many published performance claims do not hold, highlighting the need for consistent evaluation practices.
CEZSAR uses contrastive learning to align video and sentence embeddings with automatic negative sampling, claiming state-of-the-art zero-shot action recognition on UCF-101 and Kinetics-400.
Latent reasoning models often ignore their latent tokens for predictions and their correct outputs can be decoded into natural language reasoning traces more reliably than incorrect outputs.
AI benchmark evaluations require standardized item-level data releases as core infrastructure to support validity assessment, demonstrated via the OpenEval archive of 10M responses across 155k items.
ACSESS automatically combines 23 sample selection strategies to outperform individual strategies in few-shot learning on text and image datasets.
AgriIR is a configurable RAG framework using modular stages and 1B-parameter models to deliver grounded, citable answers for Indian agricultural information access.
citing papers explorer
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Pseudo-Deliberation in Language Models: When Reasoning Fails to Align Values and Actions
LLMs exhibit pseudo-deliberation, with consistent value-action misalignment in generated dialogues despite reasoning, as measured by the new VALDI framework across 4941 scenarios.