arXiv:2606. 19364v1 Announce Type: new Abstract: The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost.
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
arXiv:2609.13154v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et...
arXiv:2606. 24083v1 Announce Type: cross Abstract: "Talk short.
arXiv:2607. 23915v1 Announce Type: cross Abstract: We examine how prompt tone affects both accuracy of the LLM answers and inference cost as reflected in output-token consumption.
ParaBridge is a self‑distillation method that uses a temporary paralinguistic instruction scaffold during training to teach a speech language model when non‑lexical cues should influence dialogue responses. By providing dense, full‑vocabulary next‑token targets from the scaffolded view while the scaffold‑free model generates its own replies, ParaBridge stabilizes inference‑time behavior without requiring curated dialogues or external reward models. Experiments on Qwen3‑Omni show significant gains on safety and empathy benchmarks while preserving general performance across multiple tests.
The paper evaluates three approaches for emotion recognition in conversation— a low‑cost stacked ensemble, an off‑the‑shelf LLM prompt, and a confidence‑gated hybrid that escalates only uncertain ensemble predictions to the LLM. Across three datasets (IEMOCAP, MELD, CMU‑MOSI), the hybrid consistently outperforms each pure system, achieving higher weighted F1 scores while routing most traffic through the inexpensive ensemble. This results in significant cost savings (≈$10‑85 per million utterances) and provides an interpretable escalation signal tied to emotion or sentiment shifts.
arXiv:2609.20824v1 Announce Type: new Abstract: We explore whether entropy-based confidence signals can be leveraged to improve the accuracy of Small Language Models (SLMs) with fewer than 3 billion...
The paper compares the sustainability of native small language models (SLMs) versus large language models (LLMs) compressed via post‑training quantization for edge AI deployment. Using a Holistic Sustainability Score (HSS) that balances capability, efficiency, and safety, the study evaluates 30 configurations across five benchmarks, latency, VRAM, energy, and harmful‑prompt robustness. Results show that optimized quantized LLMs can outperform SLMs overall, while SLMs remain competitive due to lower resource demands, challenging the assumption that native SLMs are always the most sustainable choice.
arXiv:2606. 03618v1 Announce Type: new Abstract: AI-assisted coding agents are bottlenecked by input-token cost.
Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact-center-as-a-service (CCaaS) platforms, where co...
arXiv:2601. 00821v3 Announce Type: replace Abstract: A growing class of conversational-memory systems compresses dialogue history into structured artifacts -- extracted facts, decisions, or events -- on the premise that distilled structure retrieves better than raw text.
The paper presents a survey of 129 public large language model (LLM) prompt datasets, totaling over 1.22 TB and 673 million instances, and introduces a unified taxonomy for them. By analyzing seven datasets in depth, the authors identify lexical, syntactic, and semantic patterns that differentiate prompts from general text, and evaluate these patterns for tasks such as prompt filtering, source domain routing, and response quality assessment. They demonstrate that a 63‑dimensional linguistic feature set extracted on a CPU can match over 91 % of the F1 score of GPU‑based sentence embeddings while halving latency, and that structural features can effectively route prompts across datasets, though they may negatively impact response quality when prompt length is controlled.