Tag
This article describes a digital edition of Roget's Thesaurus from 1911, created by MICRA Inc. in 1988 as part of natural language understanding and semantic interoperability research.
This paper investigates entity tracking in language models and humans using naturalistic narratives, revealing that models with sub-billion parameters already achieve human-level performance and exceed humans, indicating that core language understanding emerges at smaller scales than previously assumed.
Introduces AraSSM, a bidirectional Mamba state-space encoder pretrained via masked language modeling on Arabic corpora, achieving competitive results on Arabic NLU benchmarks while training from scratch on consumer GPUs.
This paper presents a unified semantic modeling framework using a fine-tuned small language model with a multi-adapter architecture for large-scale job understanding at LinkedIn, improving performance and reducing operational complexity.
A discussion about tasks people think AI agents are ready for but aren't, highlighting the challenge of interpreting ambiguous human input like annoyed but unclear messages.
This paper investigates how large language models handle the combination of negation and figurative language, finding that this combination poses a particular challenge and that performance depends heavily on prompt style. The authors develop new annotations for the Fig-QA dataset and analyze embedding spaces to uncover additional linguistic factors like tense and concreteness.
PragReST is a self-supervised framework that improves LLM pragmatic reasoning by generating counterfactual reasoning traces and training models via supervised fine-tuning and reinforcement learning, achieving significant gains on pragmatic benchmarks without human-labeled data.
The article argues that real search queries are chaotic and complex, unlike the clean examples shown in AI demos, and emphasizes the importance of query classification and intent splitting for AI agents and intelligent customer service.
This paper proposes a modular approach for adapting pretrained language models to low-resource languages by freezing embeddings and tuning the rest, showing improvements on NLU tasks for Scottish Gaelic, Irish, and Quechua.
Researchers from Southampton and Manchester propose a hybrid adversarial defence framework for LLMs that combines entropy-based, uncertainty-based, and geometric-based models to simultaneously address hallucination and adversarial vulnerability in NLU tasks, achieving up to 64.92% improvement in adversarial robustness and 62.27% reduction in attack success rate.
Introduces DRInQ, a benchmark for evaluating conversational implicature in question utterances, revealing that LLMs often fail to recover intended implications at inference time despite being able to generate plausible pragmatic scenarios.
This paper proposes a novel preference estimation method that integrates natural language information from LLMs into a structured Bayesian opponent modeling framework for multi-agent negotiation. The approach leverages LLMs to extract qualitative cues from utterances and convert them into probabilistic formats, demonstrating improved agreement rates and preference estimation accuracy on multi-party negotiation benchmarks.
Google DeepMind demonstrated the Gemini Robotics 2 model, enabling robot Apollo to understand natural language instructions, autonomously coordinate movements, and complete multi-step tasks such as packing sports equipment in cluttered environments through full-body control and embodied reasoning, validating the potential of general-purpose robots.