Is In-Context Learning (ICL) Sufficient for Instruction Following in LLMs?

Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion (EPFL)

Paper: https://arxiv.org/abs/2405.19874

TL;DR: We uncover that, unlike for tasks such as classification, translation, or summarization, adding more ICL demonstrations for long-context LLMs does not systematically improve instruction following performance, even with more sophisticated approaches. Moreover, we show that

While ICL alignment with URIAL (ICLR'24: Untuned LLMs with Restyled In-context Alignment) is effective, it still underperformn compared to instruction fine-tuning on established benchmarks such as MT-Bench and AlpacaEval 2.0 (LC), especially with more capable base LMs.

For instruction-following tasks, the demonstrations need to be carefully chosen and of high quality, with correct answers to each question, which departs from findings in prior work (Min et al., 2022).

Installation

conda create -n yourenvname python=3.10
conda activate yourenvname
pip install -r requirements.txt

Compare ICL alignment with URIAL to instruction fine-tuning

One can find a series of bash scripts that run ICL alignment on base LLMs under the run_scripts/mt-bench/ folder. In particular,

version: it refers to the prompt that is used to do ICL. It contains two parts: (a) general rules that the base LLM should follow when generating answers, and (b) a list of instructions with corresponding answers.
model_name: it specifies the base model to use in the experiment.
data_name: it decides the evaluation test data.
repetition_penalty: we use a repetition penalty on base models to prevent degeneration, 1.0 for MoEs and 1.15 for the remaining models.

For example, run URIAL on Mistral-7B-v0.2,

bash run_scripts/mt-bench/mistral-7b-v2-urial.sh

In order to evaluate on MT-Bench, one needs to run the following command to format the generated results

python run_scripts/mt-bench/formatting_results.py Mistral-7B-v0.2.urial-vllm

Next, proceed to the FastChat library to perform the MT-Bench scoring. The AlpacaEval 2.0 benchmark adopts a similar procedure; the only difference is that the generated answers do not need to be formatted.

We improve the alignment performance via a greedy search of proper 4th/5th/6th example and list our searched results in

urial_prompts/urial.plus.best_4.examples.txt
urial_prompts/urial.plus.best_4&5.examples.txt
urial_prompts/urial.plus.best_4&5&6.examples.txt

Scaling experiments

Under the urial_prompts folder, we give prompts with varying numbers of in-context demonstrations, which are employed in the scaling experiments described in our paper.

random: Through randomly sampling from the high-quality dataset multiple times, we generate a series of in-context demonstration sets that each contain $N$ examples, and insert the $N$ demonstrations into the prompt template.
greedy: One could perceive the greedy search results of the proper fourth example as an alternative method of evaluating the data quality. We sort the 100 high-quality examples in descending order based on the score as evaluated by GPT-4-Turbo. Thus it allows us to create another series of in-context demonstration sets by selecting top-$N$ examples.
diversity: We employ stratified sampling over the source annotation of OpenHermes-2.5 to collect $N$ diverse ICL examples. We get rid of the URIAL demonstrations in this experiment since their source is unknown.
unsupervised: We reuse instructions from the experiment of scaling up diverse examples and remove corresponding answers. The prompt template for this scaling experiment consists of: (a) general rules that the model should follow when generating answers, (b) multiple instructions without answers, and (c) a few-shot prompt with ground truth answers for the desired output format and the URIAL prompt is used in our work.

Evaluation

One can find standardized code for evaluation we did in our paper from:

Citation

If you find this useful in your research, please consider citing:

@misc{zhao2024incontext,
      title={Is In-Context Learning Sufficient for Instruction Following in LLMs?}, 
      author={Hao Zhao and Maksym Andriushchenko and Francesco Croce and Nicolas Flammarion},
      year={2024},
      eprint={2405.19874},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Acknowledgements

We thank the following open-sourced repositories. If aspects of these repositories appearing in our work are useful to your research, we ask that you consider citing the accompanying papers.

[1] https://github.com/Re-Align/URIAL
[2] https://github.com/tatsu-lab/alpaca_eval
[3] https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge
[4] https://github.com/vllm-project/vllm
[5] https://github.com/huggingface/transformers
[6] https://github.com/tml-epfl/long-is-more-for-alignment

tml-epfl / icl-alignment