Hyundong Cho, Nicolaas Jedema, Leonardo Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti, Ruben Janssen, Jonathan May
| Challenge: | Current instruction tuned language models are trained on textual preference data and therefore not aligned to speech domain. |
| Approach: | They propose to use radio-industry best practices to prompt and learn speech-based preference data to improve speech-suitability of popular instruction tuned language models. |
| Outcome: | The proposed methods achieve the best win rates in head-to-head comparisons, resulting in preferred or tied to the base model in 76.2% of comparisons on average. |
Similar Papers
Advancing Language Models through Instruction Tuning: Recent Progress and Challenges (2025.emnlp-tutorials)
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| Challenge: | tutorial addresses three critical questions within the field of instruction tuning: (1) What are the current focal points in instruction tuning research? (2) What are best practices in training an instruction-following model? (3) What new challenges have emerged? |
| Approach: | This tutorial presents a systematic overview of recent advances in instruction tuning. |
| Outcome: | The tutorial covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. |
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
Multilingual Instruction Tuning With Just a Pinch of Multilinguality (2024.findings-acl)
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| Challenge: | Using multilingual instruction tuning, large language models can be used to follow instructions in multiple languages . a multilingual model can be tuned on a wide range of languages, yet most datasets are limited to English . |
| Approach: | They investigate how multilinguality during instruction tuning affects instruction-following across languages . they find that only 40 multilingual examples improve multilingual instruction- follow . |
| Outcome: | The results show that multilingual models perform better on multilingual mixtures compared to monolingual models . the results suggest that building multilingual instruction-tuned models can be done with only 2-4 languages . |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? (2024.emnlp-main)
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Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
| Challenge: | a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%. |
| Approach: | They conduct an extensive study of the performance of multilingual pre-trained LLMs instruction-tuned on parallel instruction-uning datasets. |
| Outcome: | The proposed model improves cross-lingual instruction following capabilities by 9.9% on a large and mid-sized LLM on parallel instruction-tuning datasets. |
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca (2024.findings-eacl)
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| Challenge: | Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. |
| Approach: | They employ a dataset and machine translations of it to form multilingual data and use it to tune LLMs. |
| Outcome: | The proposed model is on par or better than a model for each language, and multilingual tuning with downsampled data is as powerful and robust. |
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)
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| Challenge: | Large language models respond well in high-resource languages but struggle in low-resourced languages. |
| Approach: | They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. |
| Outcome: | The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models (2024.emnlp-main)
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| Challenge: | Existing approaches to align reasoning abilities between Large Language Models and Smaller Language Model are supervised fine-tuning and preference optimization. |
| Approach: | They propose a method that elicits Smaller Language Models to self-improve their reasoning abilities via preference optimization. |
| Outcome: | The proposed method outperforms Instruction-tuning on commonsense and math reasoning tasks on common and math scenarios. |
Psychometric Predictive Power of Large Language Models (2024.findings-naacl)
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| Challenge: | despite efforts to align large language models with human preferences, instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. |
| Approach: | They find that instruction tuning does not always make large language models human-like from a cognitive perspective. |
| Outcome: | The proposed prompts improve predictive power but are still inferior to small base models. |