An Evaluation of Classifiers for Mapping Generative LLM Responses to Answer Options of Multiple-choice Questionnaires (2026.eacl-srw)
Copied to clipboard
| Challenge: | Large language models are used to generate multiple-choice style questionnaires that were originally intended for humans in persona simulations. |
| Approach: | They investigate the performance of smaller LLMs for mapping LLM outputs into the available answer options of multiple-choice questionnaires. |
| Outcome: | The proposed model underperforms on three datasets with differing answer option complexity. |
Similar Papers
Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs? (2024.lrec-main)
Copied to clipboard
| Challenge: | Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) however, there are concerns about whether MCQ can truly measure LLM’s capabilities. |
| Approach: | They propose to use multiple choice questions to evaluate large language models (LLMs) to assess their capabilities. |
| Outcome: | The proposed methods show that MCQs are less reliable than LFGQs in terms of expected calibration error. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. |
| Approach: | They use a multilingual LLM to generate synthetic datasets covering 11 languages and 4 classification tasks and use them to train smaller models. |
| Outcome: | The proposed model outperforms the large generator in low-resource languages and tasks. |
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation (2023.findings-acl)
Copied to clipboard
Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Hélène Sauzéon, Pierre-Yves Oudeyer
| Challenge: | Large Language Models (LLMs) have demonstrated impressive prowess in natural language generation. |
| Approach: | They propose a method to select high-quality questions from LLM-generated candidates using round-trip and prompt-based scoring. |
| Outcome: | The proposed approach can select high-quality questions from a set of LLM-generated candidates without modification of the underlying model nor rely on human annotations. |
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026.findings-acl)
Copied to clipboard
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, Longxu Dou, Xianzhen Luo, Yingwei MA, Qingfu Zhu, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Prior work showed that multiple reasoning formats outperform a single format when generating multiple answers. |
| Approach: | They propose a method to measure reasoning error when generating multiple answers . they propose 'formatadapter' which generates and selects suitable reasoning formats . |
| Outcome: | The proposed method achieves a 4.3% performance improvement over previous works on math and commonsense reasoning tasks. |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
Copied to clipboard
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |
LLMs May Perform MCQA by Selecting the Least Incorrect Option (2025.coling-main)
Copied to clipboard
| Challenge: | Multiple Choice Question Answering (MCQA) is a fundamental format for various tasks in NLP, such as commonsense reasoning. |
| Approach: | They propose a method to increase the number of correct options in a dataset. |
| Outcome: | The proposed method improves the performance of multiple choice question answering (MCQA) and improves its accuracy. |
Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions (2026.eacl-long)
Copied to clipboard
| Challenge: | Multiple-choice questions (MCQs) are used to assess knowledge, reasoning abilities, and even values encoded in large language models. |
| Approach: | They propose to test whether multilingual LLMs are consistent in their responses across languages . they also use human-translated questions aligned in 8 European languages to test their robustness . |
| Outcome: | The proposed corpus of questions is aligned in 8 European languages and compared with previous studies. |
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
Copied to clipboard
Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
| Approach: | They propose to use Large Language Models as evaluators to rank or score other models’ outputs by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |
| Outcome: | The proposed evaluation methods can be used to improve multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |