Papers › Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, Minjoon Seo
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks. Previous work has shown that scaling the number of training tasks is the key component in making stronger MT LMs. In this work, we report an unexpected finding that an expert LM fine-tuned on just a single task can outperform an MT LM trained with 300+ different tasks on 11 different unseen datasets and on 13 datasets of the BIG-bench benchmark by a mean accuracy of 3.20% and 1.29%, respectively. This finding casts doubt on the previously held belief that simply scaling the number of tasks makes stronger MT LMs. Leveraging this finding, we further show that this distributed approach of training a separate expert LM per training task instead of a single MT LM for zero-shot inference possesses many benefits including (1) avoiding negative task transfer that often occurs during instruction tuning, (2) being able to continually learn new tasks without having to re-train on previous tasks to avoid catastrophic forgetting, and (3) showing compositional capabilities when merging individual experts together. The code is available at https://github.com/joeljang/ELM.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | WinoGrande | RoE-3B | Accuracy | 61.60 | #48 of 77 | Archive leaderboard | report |
| Coreference Resolution | Winograd Schema Challenge | RoE-3B | Accuracy | 62.21 | #52 of 82 | Archive leaderboard | report |
| Natural Language Inference | ANLI test | RoE-3B | A1 | 35.49 | #15 of 25 | Archive leaderboard | report |
| Natural Language Inference | ANLI test | RoE-3B | A2 | 34.64 | #15 of 25 | Archive leaderboard | report |
| Natural Language Inference | ANLI test | RoE-3B | A3 | 31.22 | #15 of 25 | Archive leaderboard | report |
| Natural Language Inference | RTE | RoE-3B | Accuracy | 64.01 | #66 of 90 | Archive leaderboard | report |
| Question Answering | COPA | RoE-3B | Accuracy | 79.25 | #37 of 60 | Archive leaderboard | report |
| Question Answering | StoryCloze | RoE-3B | Accuracy | 86.33 | #9 of 23 | Archive leaderboard | report |
| Sentence Completion | HellaSwag | RoE-3B | Accuracy | 34.6 | #81 of 89 | Archive leaderboard | report |
| Word Sense Disambiguation | Words in Context | RoE-3B | Accuracy | 52.97 | #25 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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