UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions

• URL: http://arxiv.org/abs/2404.13343v1
• Date: Sat, 20 Apr 2024 10:41:02 GMT
• Title: UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions
• Authors: Ana-Cristina Rogoz, Radu Tudor Ionescu,
• Abstract summary: This work explores a novel data augmentation method based on Large Language Models (LLMs) for predicting item difficulty and response time of retired USMLE Multiple-Choice Questions (MCQs) in the BEA 2024 Shared Task. Our approach is based on augmenting the dataset with answers from zero-shot LLMs and employing transformer-based models based on six alternative feature combinations.
• Score: 25.877058354902953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores a novel data augmentation method based on Large Language Models (LLMs) for predicting item difficulty and response time of retired USMLE Multiple-Choice Questions (MCQs) in the BEA 2024 Shared Task. Our approach is based on augmenting the dataset with answers from zero-shot LLMs (Falcon, Meditron, Mistral) and employing transformer-based models based on six alternative feature combinations. The results suggest that predicting the difficulty of questions is more challenging. Notably, our top performing methods consistently include the question text, and benefit from the variability of LLM answers, highlighting the potential of LLMs for improving automated assessment in medical licensing exams. We make our code available https://github.com/ana-rogoz/BEA-2024.

Related papers

• AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs [53.6200736559742]
  AGENT-CQ consists of two stages: a generation stage and an evaluation stage. CrowdLLM simulates human crowdsourcing judgments to assess generated questions and answers. Experiments on the ClariQ dataset demonstrate CrowdLLM's effectiveness in evaluating question and answer quality.
  arXiv  Detail & Related papers  (2024-10-25T17:06:27Z)
• Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions [45.04582353648683]
  We propose to assign preference labels by simulating expected outcomes in the future turns. This allows LLMs to learn to ask clarifying questions when it can generate responses tailored to each user interpretation in future turns. We evaluate systems based on their ability to ask clarifying questions that can recover each user's interpretation and expected answer.
  arXiv  Detail & Related papers  (2024-10-17T17:29:04Z)
• Are LLMs Aware that Some Questions are not Open-ended? [58.93124686141781]
  We study whether Large Language Models are aware that some questions have limited answers and need to respond more deterministically. The lack of question awareness in LLMs leads to two phenomena: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions.
  arXiv  Detail & Related papers  (2024-10-01T06:07:00Z)
• FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering [26.398873686905063]
  Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks. We propose a prompting method, Finite State Machine (FSM) to enhance the reasoning capabilities of LLM for complex tasks.
  arXiv  Detail & Related papers  (2024-07-03T10:01:01Z)
• Multi-LLM QA with Embodied Exploration [55.581423861790945]
  We investigate the use of Multi-Embodied LLM Explorers (MELE) for question-answering in an unknown environment. Multiple LLM-based agents independently explore and then answer queries about a household environment. We analyze different aggregation methods to generate a single, final answer for each query.
  arXiv  Detail & Related papers  (2024-06-16T12:46:40Z)
• Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena [23.264049073539663]
  Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs) LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent biases of priori unbalanced probabilities. This work aims to tackle these significant difficulties, and establish a new LLM evaluation benchmark through entirely open-style questions.
  arXiv  Detail & Related papers  (2024-06-11T17:59:47Z)
• Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models [0.9065034043031668]
  We first propose the Question-Answer cross attention networks (QAN) with pre-trained models for answer selection. We then utilize large language model (LLM) to perform answer selection with knowledge augmentation. Experiments show that the QAN model state-of-the-art performance on two datasets, SemEval2015 and SemEval 2017.
  arXiv  Detail & Related papers  (2023-11-29T10:24:50Z)
• You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments [37.03210795084276]
  We examine whether the current format of prompting Large Language Models elicits responses in a consistent and robust manner. Our experiments on 17 different LLMs reveal that even simple perturbations significantly downgrade a model's question-answering ability. Our results suggest that the currently widespread practice of prompting is insufficient to accurately and reliably capture model perceptions.
  arXiv  Detail & Related papers  (2023-11-16T09:50:53Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)
• Question Answering as Programming for Solving Time-Sensitive Questions [84.07553016489769]
  Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. Recently, Large Language Models (LLMs) have shown remarkable intelligence in question answering. This can be attributed to the LLMs' inability to perform rigorous reasoning based on surface-level text semantics. We propose a novel approach where we reframe the $textbfQ$uestion $textbfA$rogrogering task.
  arXiv  Detail & Related papers  (2023-05-23T16:35:16Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.