Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have aimed to refine their capacity to accurately follow human instructions and navigate intricate scenarios. |
| Approach: | They propose a method that uses a set of instructions to translate English into Japanese and then generates Japanese instruction data using GPT-4. |
| Outcome: | The proposed method outperforms Japanese-Alpaca models in the evaluation benchmarks without human references. |
Similar Papers
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
Evaluating Large Language Models on Wikipedia-Style Survey Generation (2024.findings-acl)
Copied to clipboard
Fan Gao, Hang Jiang, Rui Yang, Qingcheng Zeng, Jinghui Lu, Moritz Blum, Tianwei She, Yuang Jiang, Irene Li
| Challenge: | Recent studies have shown that large language models can perform well in general tasks, but their effectiveness and limitations in domainspecific tasks remain unclear. |
| Approach: | They examine the proficiency of Large Language Models (LLMs) in generating succinct survey articles specific to the niche field of NLP in computer science. |
| Outcome: | The LLMs perform better in generating succinct survey articles specific to the niche field of NLP in computer science, compared to human-authored surveys, but they exhibit bias in evaluation. |
Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
Copied to clipboard
Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
| Challenge: | Existing surveys focus on LLMs' specific utility for data annotation and synthesis. |
| Approach: | They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations . |
| Outcome: | The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information. |
Evaluating Large Language Models with Enterprise Benchmarks (2025.naacl-industry)
Copied to clipboard
Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Guang-Jie Ren, Kate Soule, Yifan Mai, Yada Zhu
| Challenge: | Existing benchmarks lack domain-specific datasets for evaluating large language models . existing benchmarks often lack domain specific datasets, which can be difficult to convert to standardized metrics or regulatory issues. |
| Approach: | They propose to use 25 publicly available domain-specific English benchmarks from diverse domains . they propose to combine a wide range of natural language processing tasks for holistic evaluation . |
| Outcome: | The proposed framework includes 25 publicly available domain-specific English benchmarks from diverse enterprise domains like financial services, legal, climate, cyber security, and 2 public Japanese finance benchmarks. |
LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)
Copied to clipboard
Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Using generic and efficient benchmark generators, human annotators are limited by inefficiency . current benchmark generator methods rely on seed signals, leading to long cycles and high costs . |
| Approach: | They propose a framework to evaluate LLMs as generic benchmark generators and integrate them as BenchMaker. |
| Outcome: | The proposed framework achieves comparable performance to human-annotated benchmarks on most metrics. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback (2023.emnlp-demo)
Copied to clipboard
| Challenge: | Existing instruction-tuned open-source LLMs have only been instruction- tuned for English and a few popular languages, thus hindering their accessibility to many other languages in the world. |
| Approach: | They propose a framework that uses supervised fine-tuning and reinforcement learning from human feedback to improve the accessibility of large language models. |
| Outcome: | The proposed framework enables the evaluation of generative LLMs in multiple languages. |
InstructEval: Instruction-Tuned Text Evaluator from Human Preference (2024.findings-acl)
Copied to clipboard
| Challenge: | InstructEval is a general text evaluator based on open-source Large Language Models (LLMs). |
| Approach: | They propose to build a general multi-aspect text evaluator based on open-source Large Language Models (LLMs) they use extensive open Human Preference Modeling datasets and a small set of multi-spect annotated data to overcome the shortage of annotation resources for multi-task evaluations. |
| Outcome: | The proposed model performs comparable or superior to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation tasks. |
Self-Instruct: Aligning Language Models with Self-Generated Instructions (2023.acl-long)
Copied to clipboard
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
| Challenge: | Large “instruction-tuned” language models depend heavily on human-written instruction data . this limited quantity, diversity, and creativity hinders the generality of the tuned model . |
| Approach: | They propose a framework for improving instruction-following capabilities of pretrained language models by bootstrapping off their own generations. |
| Outcome: | The proposed framework outperforms existing public instruction datasets by 5% . it generates instructions, input, and output samples, then filters invalid or similar ones . |
CultureInstruct: Curating Multi-Cultural Instructions at Scale (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models exhibit severe cultural bias, despite their success in recent years . a critical challenge of LLMs is integration of cultural knowledge into these models . |
| Approach: | They propose a large-scale instruction-tuning dataset to reduce cultural bias in large language models. |
| Outcome: | The proposed model outperforms GPT-4o Mini and GPT-42 with 18.47% and 13.07% relative improvements on cultural benchmarks. |