Blog(8)

• RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

  The rapid advancement of Large Language Model (LLM) capabilities has been accompanied by a significant increase in model parameter scale.

• A Class-Balanced Soft-Voting System for Detecting Multi-Generator Machine-Generated Text

  SemEval-2024 Task 8 provides a challenge to detect human-written and machine-generated text. There are 3 subtasks for different detection scenarios. This paper proposes a system that mainly deals with Subtask B.

• Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text

  Large Language Models (LLMs), particularly the debut of ChatGPT, have made significant advancement and demonstrated the ability to produce coherent and natural-sounding text across a wide range of applications.

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Publications(35)

• WhiFlash-Token-Level-Cross-Paradigm-Routing-for-Speculative-Decoding

  AuthorYoung D. Kwon, Miles Williams, Rui Li, Alexandros Kouris, Stylianos I. Venieris

  PublishedEMNLP 2026 Empirical Methods in Natural Language Processing

  Date2026-10-24

• Better-Later-Than-Sooner-Neuro-Symbolic-Knowledge-Graph-Construction-via-Ontology-grounded-Post-extraction-Correction

  AuthorTimothy Hospedales, Cristina Cornelio

  PublishedEMNLP 2026 Empirical Methods in Natural Language Processing

  Date2026-10-24

• Learning to Wait: Real Streaming Speech-to-Text Translation with an LLM

  AuthorTitouan Parcollet, Rogier C. van Dalen

  PublishedInterspeech 2026

  Date2026-09-28

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News(16)

• [Blog] CG-TTRL: Context-Guided Test-Time Reinforcement Learning for On-Device Large Language Models

  Large Language Models (LLMs) are increasingly often expected to handle complex multi-step reasoning directly on the user's device.

• [Blog] LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment

  Large Language Models (LLMs) continue to deliver remarkable capabilities, but their memory footprint remains a rigid barrier to deployment.

• [Blog] NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

  Weight-only quantization of Large Language Models (LLMs) aims to make LLM serving efficient by reducing parameter precision.

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