AI Insiders

Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-commerce

This paper addresses the challenges associated with adapting Large Language Models (LLMs) for various tasks within the e-commerce domain using prompting techniques. While prompting offers an efficient alternative to fine-tuning, it often requires significant manual effort from domain experts for prompt engineering and frequent updates to align with evolving business needs. Furthermore, crafting truly unbiased natural language prompts and selecting representative in-context examples remain difficult for humans. The authors propose a novel framework called Examples as the Prompt (EaP). This approach leverages labelled data to enhance prompts by automatically selecting the most representative examples to maximise the few-shot learning capabilities of LLMs. EaP is designed to be efficient due to its unsupervised example selection and adaptive to potential data distribution shifts.

From Demonstrations to Rewards: Alignment Without Explicit Human Preference

This paper addresses a core challenge in aligning large language models (LLMs) with human preferences: the substantial data requirements and technical complexity of current state-of-the-art methods, particularly Reinforcement Learning from Human Feedback (RLHF). The authors propose a novel approach based on inverse reinforcement learning (IRL) that can learn alignment directly from demonstration data, eliminating the need for explicit human preference data required by traditional RLHF methods. This research presents a significant step towards simplifying the alignment of large language models by demonstrating that high-quality demonstration data can be effectively leveraged to learn alignment without the need for explicit and costly human preference annotations. The proposed IRL framework offers a promising alternative or complementary approach to existing RLHF methods, potentially reducing the data burden and technical complexities associated with preference collection and reward modelling.

Flaws of Multiple-Choice Questions for Evaluating Generative AI in Medicine

This paper critically examines the use of multiple-choice question (MCQ) benchmarks to assess the medical knowledge and reasoning capabilities of Large Language Models (LLMs). The central argument is that high performance by LLMs on medical MCQs may be an overestimation of their true medical understanding, potentially driven by factors beyond genuine knowledge and reasoning. The authors propose and utilise a novel benchmark of paired free-response and MCQ questions (FreeMedQA) to investigate this hypothesis. This study provides compelling evidence that performance on medical MCQ benchmarks may not be a reliable indicator of the true medical knowledge and reasoning abilities of LLMs. The significant performance drop in free-response questions, coupled with the above-chance MCQ accuracy even with completely masked questions, suggests that LLMs might be exploiting the structure of MCQs rather than demonstrating genuine understanding. The findings underscore the importance of developing and utilizing more rigorous evaluation methods, such as free-response questions, to accurately assess the potential and limitations of LLMs in medical applications.

Generative AI in Education: Impact Across Grade Levels

This paper investigates the impact of Generative Artificial Intelligence (GAI), such as ChatGPT, Kimi, and Doubao, on students' learning across four grade levels (high school sophomores and juniors, university juniors and seniors) in six key areas collectively termed LIPSAL: learning interest, independent learning, problem-solving, self-confidence, appropriate use, and learning enjoyment. The study employed a hybrid-survey method combining questionnaires and group interviews. Key findings indicate that GAI has a generally positive impact on all LIPSAL aspects, with the most significant influence on 'appropriate use' and 'independent learning', and the least on 'learning interest' and 'self-confidence'. University students reported a higher level across all LIPSAL aspects compared to high school students. Students hold a positive attitude towards GAI and are willing to use it, recognising its potential while also acknowledging challenges related to accuracy, over-dependence, and ethical considerations.

NeurIPS 2023 LLM Efficiency Fine-tuning Competition Analysis

This document summarises the key findings and insights from the NeurIPS 2023 Large Language Model (LLM) Efficiency Fine-tuning Competition. The competition aimed to democratise access to state-of-the-art LLMs by challenging participants to fine-tune a pre-trained model within a tight 24-hour timeframe on a single GPU. The analysis of the competition reveals a significant trend towards benchmark overfitting, highlighting the limitations of current evaluation methods. Notably, top-performing submissions prioritised data curation and the use of standard open-source libraries over custom model architectures. The competition also underscored the importance of software quality and reproducibility in the machine learning community. The organisers have released all competition entries and evaluation infrastructure to facilitate further research in this area.