Blog(5)

• 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.

• Efficient Compositional Multi-tasking for On-device Large Language Models

  Adapter parameters [2] provide a mechanism to modify the behaviour of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI.

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Publications(15)

• Hierarchical planning for complex tasks with KG-RAG and symbolic verification

  AuthorCristina Cornelio

  PublishedInternational Conference on Machine Learning (ICML)

  Date2025-07-13

• BoA: Attention-aware Post-training Quantization without Backpropagation

  AuthorJunhan Kim, Ho-young Kim, Chungman Lee, Joonyoung Kim, Yongkweon Jeon

  PublishedInternational Conference on Machine Learning (ICML)

  Date2025-07-13

• Progressive Mixed-Precision Decoding for Efficient LLM Inference

  AuthorHao (Mark) Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Stylianos Venieris

  PublishedInternational Conference on Learning Representation (ICLR)

  Date2025-04-25

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News(7)

• [Blog] Efficient Compositional Multi-tasking for On-device Large Language Models

  Adapter parameters [2] provide a mechanism to modify the behaviour of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI.

• [Blog] SemEval-2025 Task 8: LLM Ensemble Methods for QA over Tabular Data

  Question answering using Large Language Models has gained significant popularity in both everyday communication and at the workplace. However|certain tasks|such as querying tables|still pose challenges for commercial and open-source chatbots powered by advanced deep learning models.

• [Blog] Robust neural codec language modeling with phoneme position prediction for zero-shot TTS

  Large language models (LLMs) have exhibited impressive in-context learning abilities [1]. Inspired by these successes, recent studies [2-5] have extended LLM applications to text-to-speech (TTS) systems by representing speech through discrete acoustic codes.

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