LLMs VS Human Judgement
LLMs VS Human Judgement
5 papers - 5 perspectives on AI
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Welcome to another edition of AI Fridays, where we bring you a curated selection of the 5 most important papers in the world of AI to delve into over the weekend. Prepared by our CTO and AI Researcher, Vishwas Mruthyunjaya, this edition promises to deliver groundbreaking insights, innovative techniques, and transformative ideas that will enrich your understanding of AI.
Can Large Language Models Be an Alternative to Human Evaluations? (🔗 Read the Paper)
The paper explores the quest to enhance text quality assessment by investigating the potential of large language models (LLMs) as substitutes for human evaluators, shedding light on their alignment with human evaluation outcomes and navigating the complexities and ethical considerations of this innovative approach.
Challenging Reproducibility in Human Evaluation: Human evaluation, crucial for assessing text quality, often poses difficulties in terms of reproducibility and comparability.
LLMs as Human Evaluation Substitutes: This paper investigates the potential of large language models (LLMs) to replace human evaluators by exposing them to identical instructions and questions, showcasing alignment between LLM and human evaluation outcomes.
Exploring LLM Evaluation Implications: While demonstrating the feasibility of LLM evaluation, the authors delve into its limitations and ethical considerations, shedding light on its broader implications.
Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact (🔗 Read the Paper)
The paper delves into the dynamic evolution of knowledge graphs across three generations, from entity-based to dual neural KGs, as this paper elucidates their distinct characteristics, construction processes, and industry-oriented techniques, offering a comprehensive journey through the realms of research and application.
Evolution of Knowledge Graphs: This paper explores the progression of knowledge graphs through three generations: entity-based KGs, text-rich KGs, and dual neural KGs, highlighting their distinct characteristics, construction methods, and industry-oriented techniques.
Bridging Research and Application: Each generation of knowledge graphs serves as a testament to the evolution of research concepts and their practical application, with knowledge graphs serving as illustrative examples in advancing both scientific and business domains.
Prompt2Model: Generating Deployable Models from Natural Language Instructions (🔗 Read the Paper)
Uncover the innovative approach of Prompt2Model, a method that empowers NLP tasks by training specialized models from task descriptions, effectively surpassing the performance of gpt-3.5-turbo while addressing LLM computational and API constraints.
Enhancing NLP with LLMs: Large language models (LLMs) are instrumental in NLP systems but face challenges due to computational demands and API limitations.
Introducing Prompt2Model: This paper presents Prompt2Model, a novel method for training specialized models from task descriptions by leveraging existing and newly generated datasets through LLMs, showcasing its superior performance compared to gpt-3.5-turbo.
PMET: Precise Model Editing in a Transformer (🔗 Read the Paper)
Dive into the realm of knowledge modification in Large Language Models (LLMs) through editing techniques, as this paper unveils PMET—a groundbreaking approach that optimizes MHSA and FFN hidden states, showcasing remarkable performance enhancements on COUNTERFACT and zsRE datasets.
Efficiency of Editing Techniques in LLMs: Small knowledge modifications in Large Language Models (LLMs) are achieved at a low cost through editing techniques.
Optimizing Transformer Layer Hidden States: Existing methods focus on optimizing Transformer Layer (TL) hidden states for target knowledge retention but face challenges due to the presence of unnecessary information.
Introducing PMET: This paper introduces PMET, a novel approach that optimizes both MHSA and FFN hidden states, utilizing the optimized FFN hidden states to update FFN weights, resulting in state-of-the-art performance on COUNTERFACT and zsRE datasets.
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI (🔗 Read the Paper)
Explore the innovative FARM framework, designed to combat covertly unsafe text in intelligent systems by leveraging external knowledge to generate trustworthy rationales, enhancing safety management and protection for consumers, while achieving remarkable performance on the SafeText dataset.
Preserving User Safety in Intelligent Systems: The safety of users is increasingly jeopardized by intelligent systems suggesting potentially harmful actions.
Tackling Covertly Unsafe Text with FARM: The FARM framework offers a solution by harnessing external knowledge to create reliable rationales, particularly focusing on missing information retrieved from trustworthy sources.
Advancing Safety Management: FARM's capability to classify safety and offer interpretable rationales empowers stakeholders and policymakers to mitigate system risks and safeguard consumers effectively, all while achieving state-of-the-art safety classification results with a notable 5.9% improvement on the SafeText dataset.
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