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  • Establish an interdisciplinary programme at Kenyatta University (KU) focused on understanding and addressing climate change with a focus on action research, innovation, sustainability and green-based economy
  • Mainstream green economy principles into KU governance, operations and outreach using whole institution approach for transformation and catalyze behavioral change necessary for a green economy
  • Build capacity of KU to foster the development of green economy curriculum and competencies for youth employability as a pathway to transition to green economy and sustainable societies.
  • Raise awareness of the green economy agenda through new partnerships and collaborations with other learning institutions, industry, government and civil society
  • Collaborate closely with UNESCO and existing UNESCO Chairs on relevant programmes and activities.

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Computer Science > Computation and Language

Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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    Source: Research Data From the analysis in Table 4.7, at KWS, inadequate dialogue with interlinked agencies early in project preparatory stages and inadequate funding of the project are challenges experienced to a large extent each with a mean score of 3.6, equivalent to 3.5 to 5.0 on the Likert scale (3.5 LE<5.0).

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  24. [2403.20329] ReALM: Reference Resolution As Language Modeling

    Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in ...