Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Authors: Manuel Faysse
Gautier Viaud
CELINE HUDELOT
Pierre Colombo
Venue: EMNLP 2023 Main (2023)
Keywords: Instruction Finetuning
Evaluation Metrics
Large Language Models
TL;DR: We show LLM-based metrics to better fit evaluation requirements introduced by IFT models, and quantify the trade-offs that emerge in industrial practical settings.

Abstract: Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the trade-offs that emerge in practical industrial settings. Our findings offer practitioners actionable insights for real-world IFT model deployment.

Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial
Applications
Manuel Faysse
Gautier Viaud
Céline Hudelot Pierre Colombo
IMluin Technology, Paris, France ²Equall, Paris, France
s MICS, CentraleSupélec, Université Paris-Saclay, France
manuel. faysse@cemttraeesseeee...ff
Abstract
employee use, and decide extend an openly avail- able Chat model by training write memos with a specific templating scheme, to respond to inter- nal FAQs, and to use internal coding tools, all the while retaining the original chat assistant's general purpose abilities. In S2, that same company is only interested in a given specific task; extracting spe- cific information from business documents, and specializes an IFT model for that purpose, aiming to leverage prompting and the generalization ca- pabilities of the model for a more data-efficient training.
Instruction Fine-Tuning (IFT) is powerful paradigm that strengthens the zero-shot capa- bilities of Large Language Models (LLMs), but in doing so induces new evaluation metric re- quirements. We show LLM-based metrics to be well adapted to these requirements, and lever- them to conduct an investigation of task- specialization strategies, quantifying the trade- offs that emerge in practical industrial settings. Our findings offer practitioners actionable in- sights for real-world IFT model deployment.
In this paper, we thoroughly examine S₁ and S2 by investigating the learning dynamies of specializ- ing IFT models through practical lens. To ensure the reliability of our tooling and the rigor of our conclusions, we first undertake a critical assess- ment of the current evaluation practices employed for IFT models. Formally, our contributions are: Contribution 1. IFT models are designed to han- dle tasks of diverse natures and varying difficulties. However, current metrics used to measure their performance are often task-specific (Zellers et al., 2019; Gao et al., 2021), rely automatic met- rics designed for other intended purposes (Papineni et al., 2002; Lin, 2004). To address this limitation, we introduce two new requirements for metries used to evaluate IFT models: Comparability Across Task (CAT) and Task and Format Agnostism (TFA), CAT imposes for metric scores to exhibit consis- tency across diverse set of generative tasks, in contrast to the sole traditional focus of consistency within specific task. TFA defines the need for met- rics demonstrate robustness to variations in the output formats. By highlighting the shortcomings of existing metrics in meeting CAT and TFA, we cluded in the original instruction training set. The second scenario, Sz, centers around converting IFT models into specialized models proficient exclu- sively on specific tasks. In Sj for instance, a large company may want an LLM assistant for internal
1 Introduction
Adapting pre-trained language models (LMs) for specific applications is central in industrial NLP to unlock task-specific performance gains and strengthen model aligmment with industry require- ments. A paradigm gaining traction is the use of instruction fine-tuned (IFT) models, LMs capable of following arbitrary instructions expressed in nat- ural language (Wei et al., 2022a; Sanh al., 2022; Ouyang al., 2022).
Researchers primarily concentrate on improving general-purpose IFT models to be used as versatile agents capable of executing instructions expressed in natural language (Li et al., 2023: Zhou et al., 2023; Xu al., 2023a). In industrial setting, prompting ChatGPT to improve the wording of an email, or to assist with a code snippet would be instances of this zero-shot utilization scenario, which we define as Sp. Critical industrial LLM applications may however not always align with So, and often prioritize two other settings. The first scenario, SJ, requires extending generalist IFT model's capabilitics to new specific tasks not in-
present compelling evidence that using LLMs as scoring agents is a viable evaluation alternative of IFT models.
Contribution 2. We approach our examination of S, and S. from a practical perspective and focus on

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the trade-off between data availability and overall performance. Our analysis uncovers two distinct phases of learning during IFT model specializa- tion: learning to format, and learning to solve tasks. Subsequently, we showcase how practitioners can (i) leverage synthetic data to facilitate learning the desired formatting aspects and (ii) use IFT models to reduce the need of expert data in industrial sce- narios. Our study provides practical insights and actionable recommendations to practitioners look- ing to deploy IFT models in production settings.
and models that mastered the task format (trained on 1000 target task samples). Comparing each metric's TFA to human-reported performance im- provements allows to grasp the extent to which mastery of the task format influences the metric performance, independently of intrinsic task per- formance. In industrial scenarios, this requirement is essential as it ensures minimal bias in the eval- uation due to training data formatting artifacts. In practice, many datasets that industrial actors may add to the training instruction set (S1), or fully train a model on (S2) have specific response formatting that differs from what a zero-shot model will an- swer, leading to a potentially large formatting bias, Comparability intra-task (CIT). While in no way novel requirement, it is essential for metrics measure performance consistently within a given task. We verify this by computing the Spearman correlation coefficient between samples of spe- cific task and human judgments.
2 Re-evaluating IFT Model Evaluation
2.1 What Should Good Scorers Measure?
In scenarios So, S1, and S2, IFT models are trained to perform generative tasks. Unlike models de- signed for single tasks with known output formats, IFT models have the capacity to generate diverse valid responses across different tasks and formats (Ouyang et al., 2022). The novel capabilities of IFT models impose new considerations when selecting an automatic evaluation metric.
In all industrial scenarios for IFT LLMs, rigor- ous model evaluation is necessarily linked evalu- ation metrics that comply with both CAT and TFA. as well as the standard CIT measures.
Comparability across tasks (CAT), Standard eval- uation metrics aim to fulfill one key requirement: coherence within each task with respect to human judgment (Specia et al., 2010). However, due to the multi-task nature of IFT models, the scores should also be comparable across different tasks (Colombo et al., 2022a; Himmi al., 2023). In other words, the scoring scale should be absolute and coherent with human preferences on all tasks, To measure the CAT we will mix samples of differ- ent tasks and compute the Spearman correlation (p) of their score with human judgment². This re- quirement is essential in scenarios Sn and S to measure model performance across different tasks, and make informed decisions regarding the trade- offs between model variants.
2.2 Existing Metrics
Current Evaluation. Currently, two dominant paradigms emerge for assessing the performance of IFT models: (i) relying on reference- scoring metries such as ROUGE-L (Lin, 2004), or normalized log- pogobbbabilities of class labels in few- shot classification benchmarks (Hendrycks et al., 2021; Gao et al., 2021; Zellers et al., 2019), and (ii) model ranking frameworks, based on pairwise preference comparisons of response quality judged by humans or LLM evaluators (Chiang et al., 2023; Dubois al., 2023; Gudibande al., 2023). Lan- guage Model based scoring has been shown be a promising alternative on specific tasks, such as sum- marization (Liu et al., 2023: Colombo et al., 2022b) or translation (Kocmi and Federmann, 2023; Xu et al., 2023b). Our work extends these findings to showcase the multi-task scoring capabilities of LLMs with respect to CAT and TFA.
Task and AgnosttAgnostism (TFA). Evaluation metries should be robust to artifacts associated with the output format and to the nature of the evaluated task (Liang et al., 2022), Implementing task-specific scoring metrics is not scalable so- lution for generalist IFT models. To measure TFA. we compute the relative target task improvement between models prompted in a zero-shot manner
LMs as Viable Scoring Mechanisms. Given the inherently open nature of IFT model generation, we adopt a reference- free setting to ensure unbiased evaluation. We present an input prompt and the corresponding generated response to the LLM
'Code and evaluation datasets are available on hptps:// gi Lttuu..oo . com//anuelFay//an FaaFFa/EaIIIaa 'Common when benchmarking metrics (Bhandari et al., 2020; Colombo et al., 2021; Chhun et al., 2022; Staerman et al., 2021; Fabhri et al., 2021; ; Colombo et al., 2021), we extend the tool to inter-task settings.
*We rely on LMS available through the OpenAl API (i.e., GPT4 and GPT3.5). See Sec. A.3 for details.

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prompting assign score on scale of 0 10. subsequently scaling it between and to facilitate comparisons with other evaluation metrics.
than with traditionally used metrics. Intra-task cor- relations on all other human data tasks, averaged in CIT lead to similar conclusions.
Baseline Metries. We assess the fulfillment of both CAT and TFA by comparing the proposed metrics against well-established reference-based metrics, including ROUGE", BScore (Zhang et al., 2020), and SBERT (Reimers and Gurevych, 2019), as well as a machine learned metric, the OpenAssistant Reward Model (RM) (Köpf et al., 2023) trained on human preferences.
CAT Analysis. Tab. ((eft) shows that all metrics, with the exception of the GPT4-based metric, ex- hibit weak or no correlation in the context of inter- task consistency. While it is true that existing met- ries demonstrate the ability to diffcrentiate between good and bad samples within a single task (CIT), their performance falls short when confronted with the open setting imposed by IFT models.
TFA Analysis. On non-LLM-based metrics, perfor- mance gains reported between zero-shot models, and models trained on 1000 target-task samples (Tab. (leftt, TFA) largely exceed the 12.0 % rela- tive improvement of human scores, and demon- strate how format, once learned, unrcalistically boosts reference-based metrics which are heavily impacted by format.
2.3 Experimental setup
Training an IFT model. IFT models are trained by fine-tuning a base model on a large instruction corpus, collected either through human annotations (Ouyang et al., 2022: Köpf et al., 2023) or con- catenating task-specific datasets (Sanh al., 2022; Mishra al., 2022). In line with recent work (Chi- ang et al., 2023; Wang et al., 2023; Peng et al., 2023), we leverage synthetic data as the base in- struction set in our IFT models (Taori et al., 2023). Benchmarking automatic metrics. To benchmark the metrics, we rely on a combination of synthetic and real data. For synthetic data, we use the Al- paca GPT4 dataset (Taori et al., 2023), and tag the data in 13 task categories (see Sec. A.1) (e.g.. logic, code, rewrite). For human data, we focus on tasks with industrial interests. Specifically, we include Natural Language Inference (Williams et al., 8;; Wang et al., 2019), Question Answering (Rajpurkar et al., 2016), NER (Tjong Kim Sang and De Meul- der, 2003), and Sentiment Classification (Socher al., 2013; Agirre et al., 2013)). To build our met- ric evaluation dataset, we train and run LLaMA-7B models (Touvron et al., 2023a) on varied data mix- tures and target tasks. For rigor, we also report scores on the summarization with human feedback dataset from (Stiennon et al., 2022) (SUM).
Metric Similarity Analysis. Fig. displays met- ric correlation at the sample level on the synthetic dataset. The results align with (Chen et 2022), indicating moderate to high correlation between BERT-based metries and ROUGE-L. However, all metrics exhibit a low correlation with GPT4, indi- cating different response features are scored.
Zoom GPT4. Tab. 1 (rightt shows strong cor- relation between the results of GPT4-based met- rics and the corresponding LLM task abilities re- ported in Wei et al. (2022b) (Logic and Coding are non- trivial for LLMs, Writing tasks are rela- tively easier). However, reference-based metrics such as ROUGE suggest the opposite, as they are biased by the syntactic overlap between model outputs and reference answers in these categories. The GPT3.5 scorer also highly rates results the Logical Reasoning tasks, contrarily to GPT4 This is due to its lesser ability to spot logical inconsis- tencies in the evaluated responses (Bubeck et al., 2023), hinting that evaluator models must be ca- pable at the evaluated task themselves in order to produce meaningful scores.
2.4 Experimental results
To better understand the limitation of existing met- ries we conduct both single-task analysis to ensure that metrics are able to score tasks reliably well multi-task analysis, which is the standard setting for IFT models. Results are reported in Tab. 1. CIT Analysis. From Tab. (left, SUM), we observe that the average correlation with human scores for evaluated summaries are higher for LLM models
Our findings highlight the inadequacy of exist- ing metrics in quantifying the performance of IFT models, while emphasizing GPT4 as promising candidate. This performance gap in evaluation ca- pabilities is primarily explained by GPT4's reduced dependence to reference answers, leading to a more coherent and absolute evaluation scale CAT, and an improved robustness to variations in output format- ting TFA. The GPT4 scorer's 's powerful capabilitiess
"ROUGE-1 is used here, it is best on one-word long labels "More details in See. B.

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Table (Left) correlation between human scores and metries on the summa-
rization task (SUM), the human data tasks individually then averaged in (CIT),
GPT4
100
and on the concatenated human tasks (CAT) to form inter-task settings. (TFA)
GPT3.5
17
100
80
denotes relative metric improvement after 1000 target task samples are added the training set. (Right) Metric scores averaged per symthetic data category
ROUGE
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BScore
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SBERT
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TEA
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Logic
Code
Memory
Write
20
RM
19
14
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100
ROUGE
0.28
0.22
0.57
+5139%
ROUGE
0.52
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GPT3.5
ROUGE
BScare
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RM
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+49.0% 5
BScone
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SBERT
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0.43
+86.3 R
SBERT
0.50
0.84
0.74
0.68
0.37
RM
0.49
0.4
0.28
0.33
Figure 1: Spearman p between
Human
GPT
0.42
0.54
0.19
0.48
+1205 R
+9.5% 5
GPT4
GPT-3.5
0.71
0.88
0.79
0.82
0.93
0.87
0.97
0.86
metrics on synthetic data.
unlock the study of novel settings traditional met- rics would struggle with (Schaeffer et al., 2023).
Mastering Format to Foster Understanding. Fig. 2 shows target task performance as the number of target task samples introduced within the base training set increases. Across all tasks and models, specialization is biphasic: first, task output format is learned while overall performance remains stant, even slightly decreases. Only once the format has been mastered, as noted by the spike of the Exact Match, does the model improve upon its underlying task performance (Human and GPT4 scores increase), eventually surpassing the origi- nal zero-shot performance. It is worth noting that this analysis is made possible by format-an aggmat-agnostic scorers (TFA) that can accurately decouple output format from underlying model performance.
3 IFT Models for Industrial Applications
3.1 Sj: Improving Specific Tasks
In this section, we delve into S₁, which specifically aims to extend an IFT model's capabilities to better perform on specific instructions.
Setting. We fine-tune a base 7B-LLM model (Pythia Biderman et al. (2023), Bloom Scao et al. (2022), Falcon Penedo et al. (2023), or LLaMA) using synthetic instructions. In each training iter- ation, we introduce selected number of N real target task samples into the synthetic dataset. We evaluate the model performance on an independent test subset of the target task. 6
Measuring Model Forgetting. Performance on a test split of the Alpaca data shows little to no perfor- mance degradation (<1 (<1%) caused by the inclusion of new tasks to the training mix (Sec. C.1.2).
Exact Match conll
Human Score mnli
GPT4 Score sst2
Pythia
0.5
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Leveraging Synthetic Data to Learn to Format. Our findings suggest a straightforward approach to optimizing the use of real examples: employ synthetic examples to assist the model in master- ing the desired format before relying real sam- ples enhance overall model performance. We repeat the previous experiment, replacing the N human- annotated target task training samples (H), by GPT4 yynthetialll-ggneeaaae synthetic samples with random labels (R) (Fig. 3) Exact Match shows synthetic or randomly labeled data can indeed be used to learn the desired for- mat, although the better quality human data even- tually yields better results with more samples. In (S+H), we train 100 synthetic samples, then on N enables the model to master the format before be- ing trained on high-quality data, largely improving human annotated data sample efficiency.
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samples. This technique
Figure 2: Incorporating 0 < N < 1000 real task sam-
ples into IFT model training
'More experimental details are given in Sec. C.I.I.

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This finding aligns with the results presented in Sec. 3.1, emphasizing the potential of synthetic datasets to enhance data efficiency industrial scenarios.
mnii
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4 Key Takeaways for Practitioners
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Leveraging LLM for evaluation. Evaluating IFT models is challenging, as it mandates comparability across tasks and format-agnostism, which standard metrics struggle with. While LLM scoring is not ideal (Limitations in Sec. 4), it is strong option practitioners should add their arsenal.
Lol
N
N
Figure 3: Incorporating S N < 1000 (H)uman, (Slynthetic and (R)andomly labeled synthetic data sam- ples in IFT training set. (S+H) is trained on 100 syn- thetic samples, then N human data samples.
3.2
Sy: IFT models as Task-Specific Solvers
Leveraging Synthetic Data for Efficient Learn- ing. LLM-bbsed evaluation uncovers the fact that leveraging synthetic data provides a quick and cost- effective approach to mastering format in low data regimes, with no performance degradation. This methodology proves viable across various scenar- ios, presenting an opportunity to more efficiently leverage potentially limited amounts of expert an- notated data available.
Setting. We use four model architectures and, for each architecture, we employ the base model to train an IFT model variant using the synthetic Al- paca dataset. We then fine-tune both the base mod- els and their IFT variants on subset of N samples drawn from the target task. This setup simulates an industrial scenario in which limited data is avail- able to specialize a model on a unique task, and assesses whether there are benefits to instruction- tuning a base model before fine-tuning it on the target task.
Limitations
While this paper has argued in favor of using LLM as scorers, important drawbacks remain. The best- performing scorer at the moment, GPT4 a propri- ctary, black-box access model, and no guarantecs exist that it will remain accessible unchanged over time, leading to reproducibility issues and data pri- vacy concerns. Since model and training data inter- nals are not pponeenooeedge, analysis of scoring errors and internal model biases is also limited.
Results. Fig. 4 demonstrates that IFT models ex- hibit enhanced performance in low-data scenarios (when 10 S N < 200). Intuitively, IFT models are better able to leverage the task description given in the prompts, thus enabling boosted zero-shot performance (Scao and Rush, 2021). This comple- ments and steers model training when finetuned with small numbers of samples. When more sam- ples are available (N 2 200), the task pattern is sufficiently clear and the benefits of prompting and of the IFT training phase disappear.
Promising openly available alternative models are being developed, either general purpose LLMs aiming to disrupt the hegemony of GPT4 (Tou- vron et al., 2023b; Bai et al., 2023), or smaller models specialized for automatic evaluation, often attempting to distillate GPT4's scoring abilities by training on GPT4 generated scores or scoring ex- planations (Xu et al., 2023b; Liu et al., 2023). the latter category, the Prometheus scoring model (Kim et al., 2023), based on Llama2, claims scor- ing performances on par with GPT4 in terms of human score correlations over a variety of tasks and benchmarks. Eventually, strong Open-Source LLMs should alleviate most of the concerns raised by relying on proprietary black-box models and we hope this work, by shedding light on the impor- tance of LLM scoring, motivates these efforts to build open models with strong scoring abilities.
Alpaca
Base Model Falcon
Bloom
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10"1100
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Figure 4: GPT4 score on SST-2 test set after finetuning with 0 < N < 1000 samples on (base) LM an IFT model. Further experiments can be found in Sec. C.2.2.

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Ethics Statement
ity, pages 32-43, Atlanta, Georgia, USA. Association for Computational Linguistics.
While this work intends to evaluate scorers across many different tasks and settings, it is essentially English-centrie, and no conclusions are drawn about the robustness of LLM scorers in other lan- guages. L.M scoring may also be affected by internal model bias acquired through pretraining or subsequent finetuning, and while efforts are made by OpenAl to mitigate bias, critical applications of LLM evaluation should consider that truly objec- tive evaluation is not attainable.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge. Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma. Rui Men, Xingzhang Ren. Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang. Sheng- guang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang. Shusheng Yang. Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingx- uan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu, 2023. Qwen technical report.
All data and base models used in this work orig- inate from publicly available sources. The GPT4 Alpaca dataset is a variant of (Taori et al., 2023) built from synthetic data only, collected through the OpenAI API. The non-synthetic data are sourced from manually annotated, widely used datasets for NLP benchmarking. This work does not transgress any usage restrictions associated with these data sources. Base models used are either available through fully open-source licenses (Falcon, Pythia), or licenses with no restrictions for research pur- poses (LLaMA, Bloom).
Manik Bhandari, Pranav Narayan Gour, Atabak Ash- faq. Pengfei Liu, and Graham Neubig. 2020. Re- evaluating evaluation in text summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9347-9359, Online, Association for Computa- tional Linguistics.
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O' Brien, Eric Hallahan, Mo- hammad Aflah Khan. Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling.
We estimate our experiments consumed 5500 GPU V100 hours, using a low-carbon compute cluster, amounting to about 950 kg of CO2 over the course of the project. To reduce the impact to maximum, all runs are done through the effi- cient Low-Rank Adaptation training strategy (Hu et al., 2021), and only trained adapter weights are stored minimize bandwidth and memory usage. API calls external APIs cached to minimize redundancies.
Sébastien Bubeck, Varun Chandrasekaran, Ronen EI- dan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Pe- ter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023. Sparks of artificial general in- telligence: Early experiments with gpt-4.
Yanran Chen, Jonas Belouadi, and Steffen Eger. 2022. Reproducibility issues for bert-based evaluation met- rics. arXiv preprint arXiv:2204 00004.
Acknowledgements
Cyril Chhun, Pierre Colombo. Chloé Clavel, and Fabian Suchanek. 2022. Of human criteria and automatic metrics: A benchmark of the evaluation of story generation. arXiv preprint arXiv:2208. 11646.
This work partially supported by Illuin Tech- nology, and by a grant from ANRT France. This work was performed using HPC resources from GENCI-IDRIS (Grant 2023-AD01 1014185). Sec- ondary compute contributions were made on HPC resources (Grant 2023-AD103256) and (Grant 2023-AD101838).
Wei-L.in Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang. Yonghao Zhuang. Joseph E. Gonzalez, lon Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chatbot impressing gpt-4 with 90%* * chatgpt quality.
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A Ressources
PER (persons), and ORG (organizations). Each key should have a value that list of strings. If the text contains no entities of given type, the corresponding list should be empty.
A.1 Data
The GPT4 Alpaca dataset is collected from https://hugging: costtatsses//
vicgalle/allaca--rn
(Rajpurkar et al.. 2016) Answer the question depending on the context. You must only answer with one excerpt from the text.
SQUADV2
We define a taxonomy of 13 subtasks that the in- structions can fall into: Classify, Code, Answer from Context (Question Answering based on given passage), Create (Artistically oriented Nat- ural Langiage Generation), Extract, Logic (Rea- soning tasks), Answer from Memory (Question Answering from internal model knowledge), Sum- marize from Memory (Summarriaaiio//Exlaaaaaooo from internal model knowledge), Rewrite (Refor- mulation tasks), Write (Natural Language Gener- ation tasks with a non-artistic goal), Summarize (Summarization tasks given a passage), Translate, and Other. We then tag each of the 52000 instruc- tions with the subtask it falls into using GPT4 as an oracle by prompting it with the classification task. To verify the quality of the tagging, we manually verify a random sample of 100 instructions and find a 93% agreement rate.
(Narayan et al., 2018) Summarize the following article few words.
XSUM
Finally split each data task category into 3 sets: training set, validation set, and set, with 80%, 10%, and 10% of the data respectively.
A.2 Models
Models are publicly available on the HuggingFace Hub, and are fairly similar decoder GPT architec- tures. We select the 7 billion parameter version compare models with similar scales: LLaMA (Tou- vron et al., 2023a), Falcon (Penedo et al., 2023), Pythia (Biderman et al., 2023), Bloom (Scao et al., 2022)).
The other datasets also originate from the Hug- ginglFace Hub 789 We format all tasks as instruc- tions using the following prompts and concatenat- ing the input at the end.
A.3 Metrics
Model performance is measured using two fam- ily of methods: reference-based metrics and LM metrics.
MNLI (Williams et al., 2018) Classify the fol- lowing relationship between the Hypothesis sen- tence and the Premise sentence, as either Entail- ment, Contradiction or Neutral.
Reference- metries are the common met- rics used in the litterature evaluate the perfor- mance of generative model on a given task, and offer a measure of the distance between a gen- eration and a reference output. In this category are included the Exact Match and F-measures, but also more task-specific metrics based on co- occurence of words betwwen the output and the reference, such as ROUGE scores (Lin, 2004) for summarization, BLEU scores for translation (Pap- ineni al., 2002). To go beyond word matching heuristics, we also baseline neural network scor- ers. SentenceBert models (Reimers and Gurevych, 2019) enables to calculate the cosine similarity between sentence embeddings; in this work a general purpose embedding model (all-mini-Im- 16-v2) is used. BertScore computes a Fl score leveraging pairwise cosine similarity between kens, using strong encoder model finetuned on an NLI task as shown to be best, here we use tetrossttt//bbtttaaaeaaaaa
QNLI (Wang al., 2019; Rajpurkar al., 2016) Classify whether the given context contains enough information to answer the question (answerable) or not (unanswerable).
STSB (Agirre et al., 2013) Give an integer score between and 5, describing how similar sentencel and sentence2 are. 5 means they are very similar, means they are nothing alike.
SST2 (Socher et al., 2013) Classify the following sentence negative or positive.
CONLL (Tjong Kim Sang and De Meulder, 2003) Extract locations, persons, and organizations from the text. The output should be formatted as JSON object with three keys: LOC (locations),
chttps://hugginge: https//nnnna.cooottttttts//ggue "https://huggiingffce httpe//aacaeecccae/eeeeeeeeeeeee co/ddatasetss/quad.v "https://huggingt"
from the Hug- gingFace Hub (He et al., 2023).

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LM metries are metrics that are computed by directly scoring the output using a language model. this work, we experiment with GPT4 and GPT3.5 as scorers, by providing the evaluated model output and tasking the scorer to grade the quality of the output fom O to 10, based on rele- vance, fluency, factuality, coherence. The scoring prompts are task agnostic and allow to compare the performance of model different tasks, open-ended tasks where there is no ground truth and traditional literature metrics cannot be used. They also have the advantage of being continuous, which allows to study transitive regime without the "emergence" effect (Schaeffer et al.. 2023). Finally, we baseline Reward Model (RM) (Köpf et al., 2023), model trained on outputting score de- signed to reflect human's s appreciation of a judge- ment. While this can be used reference- free scorer, we find increased robustness when report- ing the score softmaxed with the RM score of the reference.
Output:
Response 1 corresponds to the model prediction, and reference 2 to the "gold" label. The scores obtained are then checked for conformity (correct output template, scores between 0 and 10). Finally, they are scaled between 0 and 1.
A.4 Framework
Code is written using PyTorch and the Transform- library, well as the PEFT library to train models using low-rank adaptation. Training runs are done on compute clusters with NVIDIA V100 32GB GPUs.
A.5 Default Model Training
We train models across wide range of models, training data source and dataset sizes. To stay con- sistent between runs, we train models for 400 steps with a 128 batch size, achieved through gradient ac- cumulation, a 5e-4 learning rate with linear decay, warmup of 100 steps, or one epoch (whichever is lowest). We frequently log validation split CE loss and use early stopping prevent overfitting. Code is fully released at https: //gi thub. com/ Manuel Fanueelay/IFFEvaa Training runs to completion take about hours on VI00 GPU and 2h on a A A100 GPU. This default setup is used to train all models in this work.
GPT Scoring prompt To obtain GPT4 and GPT3.5 scores for model responses to given in- struction, prompt the OpenAl API such:
You are a helpful assistant that helps eval- uate the quality of two responses to a prompt.
Answer by awarding score between 0 and 10 to each response, where 0 means the response is completely inappropri- ate and 10 means the response very good. A response that is acceptable should never be awarded less than 6 out of 10.
The only exception are models trained on datasets with less than 128 samples (less than the batch size). In these cases, select batch size of 8.
B Re-evaluating evaluation
Answer base on the following criteria:
B.1 Experimental setup
1. Is the response grammatically correct?
Summarization dataset In this dataset (Stiennon et al., 2022), summaries are already generated and associated with human scores. We select 4 summa- rization models from the dataset, and 200 inputs the models have been evaluated in common on, and that have at least different human annotators. This enables computing Spearman Rank Correla- tion both between metrics and the mean human annotation, but also between human annotators by averaging the pairwise human correlation scores over all pairs of annotators.
2. Is the response semantically correct?
3. the response coherent?
4. Is the response relevant to the prompt?
Output format (csv):
<scorel from 0 to
from 0 to
10>
Rate the responses to the following in- struction.
Synthetic Alpaca dataset Models are trained in the default conditions explicited in A.5. To con- struct Spearman rank correlations between evalua- tors, we train a suite over 52 models in which the
[prompt]
Response 1: [responsel)
Response (response2)

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training data mixture slightly differs (exclusion or
els finetuned on the Alpaca dataset, and for each task, 5 models trained on both Alpaca and 1000 samples of the target task. Previous experiments (Fig. 2) have shown that on these datasets, format is generally learned after a few hundred samples. This enables to compare models that Iearned target task output format with models that have not, using the various metrics at our disposal. We com- pute the relative performance improvement of all metrics, and average them in Tab. 1 (TSA). While human evaluation shows model performance has progressed with the introduction of target task sam- ples (12%), non-LLM metrics largely overestimate the boost in response performance, because of their strong bias towards outputs matching the "gold" reference. In low-shot settings when no formatting is learned, it is particularly interesting to use for- mat agnostic scorers like GPT4 to truly analyze a model's comprchension of task.
partial exclusion of target category). To do so, we (i) select one of the 13 task categories identified in A.I. (ii) We then build a base training data mixtures by concatenating the training splits from all other categories, respectively the validation splits. (iii) Finally, we randomly sample respectively O. 10. 100 and 1000 samples from the held-out category's s training split and add them to the base mixture. (v) We train one model per data mixture according to the training guidelines in A.5. (v) We evaluate per- formance with all metries on the held-out task test split. (vi) The process is repeated for all categories.
Human-annotated datasets Models are trained in the default conditions explicited in A.5. To eval- uate Spearman correlation between rankers (CAT and CIT scenarios), we train a suite of over 300 models in which the training data mixture slightly differs (exclusion partial exclusion of a target category). To do so, we (i) select all 13 task cate- gories identified in A. (ii) We then build a base training data mixtures by concatenating the training splits from all categories, respectively the valida- tion splits. (iii) We then select a category from the benchmark tasks (MNLI, QNLI, STSB, SST2, Squad, XSum, CONLL). (iv) We randomly sample respectively 0, 10, 25, 50, 100, 200, 500 and 1000 samples from the selected category's 's training split and add them to the base mixture, and repeat the sampling process 4 times, to obtain 7*8*4 504 data mixtures.
B.2 Obtaining Human Scores on the benchmark data
To facilitate the collection of human annotated model outputs on the non-synthetic data, we sim- plify the problem by reducing it to a binary choice (Correct / Incorrect) on classífication-like datasets (SST2, MNLI, QNLI). We manually observe out- puts for each trained model and output task cate- gory, and craft pre-tagging heuristics to make the annotation process quicker. These heuristics are empirically built with knowledge of model outputs; for example on zero-shot sentiment classification tasks, Llama models will often answer "The sen- tence is Positive", instead of "positive", ", but trivial heuristic functions can assist in tagging these as correct answers. This is facilitated by the fact out- put structure for given model/task combination is often very similar (especially at low decoding temperatures). and we can iterate on these heuristic functions until no error detected all. For non- classification tasks, we adopt a strict rating scheme as well. NER responses through the CONLL 2003 task are considered correct only when the dictio- nary contains all correct key/value pairs with re- spect to the ground truth, but disregarding format- ting artefacts. For SquadV2, extractive task, we report the F1 intersection between the predicted answer span, and the correct answer, similarly as what is done in (Rajpurkar al., 2016). Finally, for summarization, we report ROUGE scores for XSUM as a reference point, but do not include
(iv) We train one model per data mixture according to the training guidelines in A.5.
(v) We evaluate performance with GPT4 on the target task test split. (vi) To compute the Spear- man correlations, we aim to reduce dataset specific formatting artefacts and thus only select models trained with less than 100 target task samples in the training mixture. Correlations are computed two ways, either by computing Spearman correlations per task category and averaging the correlation val- ues with human judgement (CIT), or by comput- ing correlations globally without considering task category information (CAT) enabling the study of cross-task scoring comparability.
TFA Finally, to compute the TFA, that is the rel- ative performance improvements after format is learned, we select 3 target tasks, MNLI, QNLI, SST2. These datasets are the three datasets we have perfect sample-wise binary human evaluation for from the set of human data benchmarks. From the suite of trained models, we select base mod-

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these results with the other tasks during correlation computations, and rather compute human corre- lations using the human annotation dataset from (Stiennon et al., 2022), for which we report results on Table (SUM).
Sentence: in the media Model prediction: The sentence "in the media" is positive statement. Ground truth: negative
Sentence: profanities Model prediction: The sentence "profanities" is a positive statement. Ground truth: negative
Code is released at https://githhb com/ ManuelFay/IFTEval
"The model seems to struggle with understand- ing the context and sarcasm in the sentences. It often misinterprets negative statements as positíve, possibly due to the presence of words that might be associated with positive sentiment other contexts. The model needs to improve in understanding the overall context of the sentences and detecting sar- casm or irony. Additionally, it seems to struggle with shorter sentences or single-word inputs, where might not have enough information accurately determine the sentiment."
B.3 Results on Alpaca test set
The full results per category are reported in Table 2.
B.4 Automatic Error Analysis
To go further in the analysis and uncover novel in- sights at large scale, LLM models can for example enable automatic error analysis. We select few low scoring outputs and prompt the scoring models to detect mistake patterns, or give potential explana- tions for the generative model's shortcomings On the sentiment classification task, automatic error analysis correctly detects that most errors are false positives, triggered by sarcasm, or "the presence of words that might be associated with positive sentiment in other contexts". For NLI tasks, detects the tested model has troubles with with "un- derstanding negations, contradictions, and subtle differences in meaning between the premise and hypothesis".
NLI The following is series of predictions a NLI ML model got wrong. Can you spot any pat- terns in the types of mistakes the model made and explain in what areas the model needs improve?
He pulled his cloak tighter and wished for moment that he had not shaved his head. Hypothesis: The man pulled his super hero cape around him- self show off. Model prediction: En- tailment. Ground truth: neutral
Premise:
Sentiment Analysis The following is series of predictions sentiment classification ML model got wrong. Can you spot any patterns in the types of mistakes the model made and explain what areas the model needs improve?
Premise: because always had do it and just pay someone else do it and they do the they do the cutting they fertilize they um edge and um think this year i I'm going to have some landscaping put in Hypothesis: I still do all the gar- dening and landscaping myself. Model prediction: Entailment. Ground truth: contradiction
Sentence: the château would have been benefited from sharper, cleaner script before went in front of the camera Model prediction: The sentence a pos- itive statement. Ground truth: negative
Sentence: but there 's plenty to offend everyone Model prediction: The sen- tence is positive. Ground truth: negative
Premise: He's chosen Meg Ryan. Hy- pothesis: Jon Doe was chosen. Model prediction: Entailment. Ground truth: contradiction
Sentence: outtakes in which most of the characters forget their lines and just utter "uhhh, which is better than most of the writing in the movie Model prediction: The sentence is positive. Ground truth: negative
Premise: yep that's what he's worried about the trees or bush because lilac bushes they they grow fast some people uh would really like have them and then the people that do have them they spread and they sprout all over their their lawn Hypothesis: He's not worried about the trees. Lilac bushes take a long time
Sentence: the filmmakers' paws, sad say Model prediction: The sentence positive. Ground truth: negative

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GPT4
GPT3.5
ROUGE-1
BERTScore
SBERT
RM
Soft RM
Write
0.97
0.86
0.41
0.72
0.74
3.21
0.33
Answer from Context
0.94
0.86
0.55
0.76
0.80
1.29
0.45
Answer from Memory
0.93
0.87
0.47
0.73
0.84
2.78
0.28
Extract
0.92
0.88
0.54
0.77
0.83
1.61
0.52
Summarize from Memory
0.92
0.85
0.45
0.73
0.84
3.21
0.43
Summarize from Context
0.89
0.86
0.54
0.79
0.84
1.47
0.42
Rewrite
0.89
0.87
0.68
0.86
0.82
-0.20
0.47
Translate
0.88
0.94
0.58
0.81
0.71
1.78
0.44
Create
0.85
0.78
0.34
0.67
0.62
-0.20
0.27
Classify
0.85
0.76
0.50
0.73
0.72
0.74
0.43
Code
0.79
0.82
0.46
0.71
0.74
0.73
0.43
Logic Reasoning
0.71
0.88
0.52
0.73
0.80
0.33
0.49
Table 2: Averaged score over samples and models of each metric on each category. Category Other is not listed as it consists synthetic instructions often not answerable by LLMs without external tooling.
to grow. Model prediction: Entailment.
it. Model prediction: The relationship between the Premise sentence and the Hypothesis sentence is Neutral. Both sentences are independent of each other and do not entail or contradict each other. Ground truth: entailment
Ground truth: contradiction
Premise: Trying Your Luck Hypothesis: This is not game of luck, but one of pure skill. Model prediction: Entailment.
Ground truth: contradiction
Premise: well that's pretty typical though uh don't uh don't guess it's going to be any much different uh than than has been in the past so expect uh July and August we'll see our or uh share of hundred degree days Hypothesis: We don't ever see the temperature get into the hundreds. Model prediction: Entail- ment. Ground truth: contradiction
Premise: He bent down to study the tiny little jeweled gears. Hypothesis: The jewels the gears he examined. Model prediction: Entailment. Ground truth: contradiction
Premise: As basic guide, the sym- bols below have been used indicate high-season rates in Hong Kong dollars, based on double occupancy, with bath or shower. Hypothesis: This page in- tentionally left blank. Model prediction: Entailment. Ground truth: contradiction
They would burn to the ground by morning. Hypothesis: They would not burn to the ground by morning, Model prediction: Entailment. Ground truth: contradiction
Premise:
Premise: fact, European nations need to do some serious fiscal housecleaning. Hypothesis: There needs to be some se- rious fiscal housecleaning by European nations. Model prediction: The relation- ship between the Premise sentence and the Hypothesis sentence is Neutral. The two sentences are contradictory or entailing. Ground truth: entailment
Premise: A rusty iron gate swinging dis- mally on its hinges! Hypothesis: The iron gate did not move at all. Model prediction: Entailment. Ground truth: contradiction
The model seems to have pattern of incorrectly predicting entailment when the ground truth is ei- ther neutral or contradiction. It appears struggle with understanding negations, contradictions. and subtle differences in meaning between the premise and hypothesis. The model also seems to have difficulty with understanding the context and re-
Premise: It means that they gather and interpret their material fairly and argue about its interpretations rationally. Hy- pothesis: They gather and interpret the material fairly and argue rationally about

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lationships between entities mentioned in the sen-
0.8
tences. To improve, the model needs focus on better understanding negations, identifying con-
0.6
0.6
0.4
0.4
tradictions, and recognizing subtle differences in meaning and context between sentences. Addition- ally, it should work on better identifying the rela- tionships between entities and the overall context of the sentences.
0.2
0.2
0.0
0.0
0.0
10a
10
10
10-
16
contl
1.0
mili
1.0
enii
Exnct Match
0.8
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Custom Score
0.6
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0.6
CP 1-4 Searn
B.5 Correlations on the non-synthetic human tasks
0.4
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0.2
0.2
Table 3 and Table 4 display the scorer correlations between themselves on the non-synthetic datasets in both the CAT setting (Table 3), and the CIT set- ting (Table 4). Results are consistent with Fig. 1, with BertScore, SentenceBert, Rouge all displaying greater correlations between themselves than with reference-free metrics, and GPT4 standing out both on scoring within each tasks (CIT) and between tasks (CAT).
0.0
01o"
10t
10"
10"
0 0.30
10t
10
10'
0
1000
10-
10
10"
Figure 5: LLaMA performance after incorporating 0 < N S 1000 real task samples from respectively CONLL. MNLI, QNLI, SquadV2, SST2 and XSUM into the base training mixture
C.2 Sz: IFT models as Task-Specife Solvers
C.2.1 Experimental setup
C IFT Models For Industrial Applications
Models are trained in the default conditions ex- plicited in A.5. Starting from either a base model, or an instruction fine-tuned model, we evaluate tar- get task performance as the model is fine-tuned solely on varying number of target task training samples. We (i) select target task from the real data benchmark tasks. (ii) We then sample set of N E {0, 10, 25, 50, 100, 200, 500, 1000} samples from the training set, and pick 100 samples to serve as a validation split. (iii) We repeat the sampling process 4 times to mitigate variations between runs. (iv) We train one model per data mixture according to the training guidelines in A.5. (v) We evaluate performance with all metries listed in A.3 on the target task test split.
C.1 Sy: Improving Specific Tasks
C.1.1 Experimental setup
Models are trained in the default conditions ex- plicited in Sec. A.5. To evaluate target task per- formance as target task samples are progressively introduced in the generalist synthetic training set, we train suite, we (i) select all 13 task categories identified in Sec. A.1. (ii) We then build base training data mixtures by concatenating the train- ing splits from all categories, respectively the vali- dation splits. (iii) We then select a category from the benchmark tasks (MNLI, QNLI, STSB, SST2, SquadV2, Xsum, CONLL). (iv) We randomly sam- ple respectively 0, 10, 25, 50, 100, 200, 500 and 1000 samples from the selected category's 's training split and add them to the base mixture, and repeat the sampling process 4 times; to obtain 7*8*4 504 data mixtures. (iv) We train one model per data mixture according to the training guidelines in Sec. A.5. (v) We evaluate performance with all metries listed in Sec. A.3 on the target task test split.
C.2.2 Extra models
To evaluate the impact of IFT quality of the findings of the S₂ experiment, we repeat the experiment using an "off-the-shelf" strong IFT model from the Hugging Face Hub. Notably, we select the 7B variant of (Chiang et al., 2023) for the Llama vari- ant, BloomZ for Bloom (Muennighoff et al., 2022), Malcon-Instrud for Falcon (Penedo al., 2023) and an IFT version of Pythia trained by (Köpf et al., 2023). Results reported in Figure 6 show similar dynamics between the off-the-shelf models and the variants Instruction Fine-tuned this work for one epoch on the Alpaca dataset (Taori et al., 2023).
C.1.2 Performance degradation
Full performance degradation results after 1000 samples of task speciffc data are integrated within the synthetic test set are shown in Tab. 5.

---

ROUGE-1
GPT4
GPT3.5
BScore
SBert
RM
Human
ROUGE-1
1.00
-0.16
0,46
0.73
0.85
-0.77
0.22
GPT4
-0.16
1.00
-0.28
0.10
-0.04
0.56
0.68
GPT3.5
0.46
-0.28
1.00
-0.00
0.16
-0.37
-0.19
BScore
0.73
0.10
-0.00
1.00
0.91
-0.61
0.22
SBert
0.85
-0.04
0.16
0.91
1.00
-0.69
0.29
RM
-0,77
0.56
-0.37
-0.61
-0.69
1.00
0.28
Human
0.22
0.68
-0.19
0.22
0.29
0.28
1.00
Table Spearman p between scorers on non-synthetic data in the CAT setting.
ROUGE-1
GPT4
GPT3.5
BScore
SBert
RM
Human
ROUGE-1
1.00
0.50
0.33
0.63
0.90
-0.10
0.57
GPT4
0.50
1.00
0.66
0.07
0.35
0.40
0.77
GPT3.5
0.33
0.66
1.00
-0.10
0.16
0.24
0.48
BScore
0.63
0.07
-0.10
1.00
0.66
-0.43
0.13
SBert
0.90
0.35
0.16
0.66
1.00
-0.18
0.43
RM
-0,10
0,40
0.24
-0.43
-0.18
1.00
0,29
Human
0.57
0.77
0.48
0.13
0.43
0.29
1.00
Table Spearman y between metrics on non-synthetic data in the CIT setting.
Base
sst2
conll
mnli
Average
Bloom
0.75
0.74
0.74
0.73
0.73
Falcon
0.87
0.86
0.87
0.84
0.85
Llama
0.84
0.85
0.86
0.86
0.86
Pythia
0.67
0.66
0.66
0.66
0.66
Table 5: Performance degradation on the Alpaca test set, after introducing 1000 samples specialized to train set. Base denotes the reference scores, computed from the initial zero-shot performance.
0.8
Alpaca
0.8
Base
Shelf
0.7
0.6
0100 10 10 10 10a
010"
10t 10 10
Bloom
Falcon
0.75
0.25
0100 10 102 10 10 Llama
10
0100 10- 102 10 Pythia
Figure 6: GPT4 score SST-2 test set after finetuning with 0 < N < 1000 samples (base) LM, an "off- the-shelf" IFT model, and an IFT model fine-tuned on Alpaca.


Reviews:
[{"review_id": "FdWNYQWnoV", "reviewer": "Reviewer_9tn7", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer 9tn7 for their time spent on reviewing our paper. We are glad that the reviewer acknowledges the novelty of our introduced evaluation constraints and our efforts in designing solid, replicable experiments. We answer below the concerns of the reviewer:\n\n### On the connection between the evaluation requirements and the identified scenario. \n\n\nAs stated in paragraph 1 of our introduction, our paper studies LLM performance in various realistic industrial training scenarios, and in order to draw rigorous conclusions, reexamines how to accurately evaluate them by imposing novel constraints. \n\nComparability Across Task (CAT) is essential for evaluating IFT models in scenarios S_0 and S_1. Given the multi-task nature of IFT models, it is crucial for evaluation measures to score using coherent evaluation scales across tasks, in order to enable comparing model performance as a whole. This necessity is introduced in paragraph l82, and  illustrated in Table 1  (right) and l202 (as well as Appendix Table 2).  Typically, Table 1 shows GPT-4 evaluation estimates the evaluated models are better at redaction tasks (“Write”), than at logical reasoning tasks (“Logic”).  Furthermore, performance trade-offs occur and training to improve performance on a specific task may lead to degradation on other tasks (l247, detailed in Supplementary Material C.1.2). For instance, we observe from Figure 2 and Table 3, that a Bloom model trained on 1000 samples of CONLL improves on the CONLL task by over 30 points, but drops 1 point of general performance drop as measured by the Alpaca test set. To clarify this, we will extend l82 to include more details and add a clarification line in the introduction and examples in the appendix.\n\nThe Task and Format Agnostism requirement (TFA) ensures minimal bias in the evaluation due to training data formatting artifacts (l93). In industrial scenarios, this is essential since many datasets that industrial actors could add to the training data (S_1), or fully train a model on (S_2) have specific response formatting that is not always fully similar to what a zero-shot model will answer. For instance, the label from a sentiment analysis dataset sample  (SST2) may simply state a movie review is “negative”, while a general-purpose model may output that the review “[...] appears rather positive from the adjectives used”. In this case, although the output is the same, most reference-based metrics will heavily penalize the extra details from the model output. To grasp model understanding capabilities of a target task independently of formatting artifacts, this requirement is crucial as can be seen in Figures 2, 3 and 4 which display experiments that simply cannot be run with metrics biased by output format. To clarify the need of TFA, we will detail the paragraph line 95 with examples, and add a clarification line in the introduction.\nIn all industrial scenarios for IFT LLMs, rigorous model evaluation is necessarily linked to evaluation metrics that comply with both CAT and TFA which is one of the point this paper makes.\n\n\n###  Clarifications on the industrial need to repurpose IFT models\n\nRepurposing an Instruction-following LLM for specific tasks  is common in the industry, either by keeping the LLM general-purpose but adding new capabilities (S1), or by fully specializing an LLM for a set of tasks (S2).  Examples of (S1) could be companies aiming to extend open-source LLMs with company specific needs (coding, internal FAQ answering capabilities) while retaining the benefits of a general purpose instruction models. This paper shows in Section 3.1 that this strategy is interesting because new capabilities can be introduced with minimal performance degradation on the other tasks. (S2) represents use cases in which a model is used for a specific purpose only (summarization for example). In this case, as shown experimentally in section 3.2, it is interesting to start off from an Instruction Following model rather than its base counterpart in low-data regimes where more performance can be extracted. \n\nOur results are especially exciting because we show repurposing IFT models is a strong industrial strategy, especially in terms of data efficiency, and uncover that the use of synthetic data can help optimize efficiency even more (Key Takeaways) .\n\nHopefully we were able to clarify the reviewer’s concerns  and we will strengthen the paper by expanding on these points in the extra space available upon acceptance. We hope our explanations will convince the reviewer of the well foundedness of our work, and shed more light on our experimental choices and claims, hopefully augmenting their soundness and excitement scores.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper proposes novel measures to investigate performances of instruction-following LLMs . \n- Pilot experiments are solid and show that the method is applicable.", "reasons_to_reject": "- The paper fails to link how the novel measures should be linked to scenarios that are described in the introduction: S_0, S_1, and S_2. \n- The paper suggests that there is a need to repurpose an Instruction-following LLM to a specific task. The reason is not sufficiently explained. Saying that this is an industrial need is not sufficient without examples.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes two novel measures to investigate instruction-following LLMs. The idea is to be able to investigate performance across different instruction following tasks in a coherent way.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "wOmnpLJeYk", "reviewer": "Reviewer_NrxC", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the extensive reading of our work and constructive review. We are glad that reviewer NrxC  finds the concepts behind this research project to be of interest; providing new angles to examine the evaluation of instruction-tuned models and empirically showing the LLM-based metrics can meet the new requirements and would benefit industrial applications. \n\n\n### On the necessity for more detailed explanations of Section 1 results\n\n\nCurrently, insights, notably about GPT4 superiority, are briefly given in Section 2.4 and more details are present in the supplementary material (B.1, l839). We explain metrics based on a distance metric to a gold reference are heavily biased by the syntactic overlap between the prediction and the reference (Zoom on GPT4 subsection, B.1). On tasks where a good response may largely differ from the gold reference (eg. creative writing), the response is penalized by reference-based scorers, and the inverse also holds true (Table 1, Table 2). Additionally, response format heavily biases reference-based metrics (TFA analysis, l194). We show LLM based metrics rely on different response features (“Metric Similarity Analysis” subsection l210, and appendix B.5) and are less sensitive to the gold answer and the output format. Extra results are also given in the Appendix, typically metric explanations (A.3), detailed results per evaluation category (Table 2, B.3), and insights about the criteria (TFA in B.1, l839).  It is thus for their capacity to be less sensitive to bias-introduced by the reference, that LLM metrics are more robust to format (TFA) and coherent between tasks (CAT). We will further detail this point in the main paper upon acceptance.\n\n\nAnother insight we did not expand on in the paper by lack of space, that also explains the dominance of GPT-4, can be seen when observing GPT3.5 logic reasoning scores which are very good when compared to the scores GPT-4 attributed. This is simply due to the fact GPT3.5 is not as capable as GPT-4 on such tasks, leading to it not being able to spot logical inconsistencies in the model response as well as GPT-4 does. This hints that evaluator models must be capable at the evaluated task themselves in order to accurately score other models. We will expand on this point in appendix B.3, supported by evidence from Table 1 (main paper) and Table 2 (appendix).\n\n\nWe thank the reviewer for highlighting the need for clarifications we will add to the final manuscript, and hope these explanations will make for a more coherent and understandable work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper provides two interesting angles to examine the evaluation of instruction-tuned models. It empirically show the LLM-based metrics can meet the new requirements and would suit industrial applications. ", "reasons_to_reject": "More ablation on why conventional metrics are inferior to GPT4 is preferred, especially for the proposed requirements analysis (CIT, CAT, TFA). Current evaluation only presents results instead of explaining them.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper investigates the evaluation metrics of instruction-tuned models. It first presents two new requirements for industrial deployment settings of instruction-tuned models: comparability across task and task format agnostism. Through experimental evaluation of instruction-tuned models, the authors compare LLM-based scorer metrics with conventional automatic metrics (like ROUGE, BERT-Score, etc) and find LLM-based scorers like GPT4 have better correlations with human scores. The paper advocates for LLM-based metric evaluation for instruction-tuned models. For industrial applications using instruction-tuned models, the paper also presents two scenarios: improving specific tasks and using instruction models as task-specific solvers. \n\n\nThe main contribution comes from examining the specific evaluation requirements of instruction-tuned models and empirically showing conventional evaluation metrics do not meet the needs and instead one should use LLM-based scorers. ", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "vIazjpxMpU", "reviewer": "Reviewer_52Xn", "summary": "", "questions": "How will the model update and iterative development affect the fairness of the model's scoring? For example, when the scores of the old and new versions are inconsistent.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the extensive review and in-depth look at our work. The paper contributions are found to be interesting and clearly stated, and the review highlights our strong experimental results, as well as our findings on data-efficient training strategies.\n\n### Typos Grammar Style And Presentation Improvements\n\nWe thank the reviewer for taking the time to look through the supplementary material, and will make sure to update it to make it more readable (notably examples in B4).\n\n### Need for experimental clarifications (appendix C.2) \n\nThe Appendix C.2 experiment, referred to in main paper Section 3.2  assesses whether fine-tuning an instruction fine-tuned model for a target task, rather than starting from a base model, helps in terms of data efficiency. We find it is the case in low data regimes (typically with less than 200 target task samples, l279), and this result holds across various model architectures. With an extra page in the main paper, we will be able to move experimental details from the appendix to clarify the motivations and setup of this experiment. We will also justify why it is the case; namely that prompting instruction following models unlocks increased zero-shot performance by leveraging the task description to assist the model’s pattern understanding capabilities (Scao, 2021).\n\nAdditionally, we will make sure to move some details from the appendix and expand on and justify the rest of the experimental results in both Section 1 and 2 within the newly available page.\n\n### Interrogations on the consistency of LLM-based rating between model versions\n\nWe acknowledge the point raised by the reviewer in the “Limitations” section of our work. Along with other drawbacks, no guarantees are given that black box proprietary models like GPT-4 will remain available and stable over time and this is a real drawback if we want to standardize LLM evaluation across time. OpenAI is aware of the issues caused by inconsistent behavior of its models over time and offers guaranteed long term support for some model checkpoints, which may help, but more realistically, the solution should come from the release of capable open-source alternatives, which is a trend we see with the Llama or Falcon model families for instance.\n\nTo expand on this point, we ran extra evaluation experiments using the open source “Llama-2-70B-Chat” model as an evaluator, released after the initial submission date. Our initial results demonstrate this model is capable of outputting evaluation scores that fulfill our CAT and TFA requirements, offering an alternative to reference-based metrics without most of the drawbacks related to using a black-box model (see “Limitations” l304, l312). These interesting results will be expanded and added to the final manuscript.\n\nWe thank Reviewer 52xn once again for the comments and suggestions and hope our clarifications reinforce the soundness of our work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Two new evaluation metrics and a comprehensive method for evaluating the performance of IFT models. In addition, a large number of experiments were conducted to verify the effectiveness of the metrics.\n2. Strong evaluation results with detailed experimental setup to reproduce the results.\n3. The paper proposes using synthetic data to learn the required format information and improve the performance of IFT models in low-data environments, which is reasonable.\n", "reasons_to_reject": "Some experimental results are not well explained and lack justification. For example, C.2.", "questions_to_authors": "How will the model update and iterative development affect the fairness of the model's scoring? For example, when the scores of the old and new versions are inconsistent.", "paper_topic_and_main_contributions": "This paper discusses the evaluation methods and application scenarios of Instruction Fine-tuning models (IFT models) in industrial applications. The paper proposes two new evaluation metrics, CAT and TFA, and verifies the feasibility of these metrics using LLMs as scoring agents. CAT requires that the evaluation metrics be consistent across different tasks, not just within a specific task. TFA requires that the evaluation metrics be robust to changes in the output format. The authors found that using LLMs as scoring agents is a viable alternative method for evaluating IFT models. In addition, the paper presents a method for improving model learning efficiency using synthetic data when data is limited.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "The examples in the appendix (such as B.4) can be presented in a tabular form."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The work introduced Instruction Fine-Tuning (IFT), a paradigm that enhances the zero-shot capabilities of Large Language Models (LLMs) by fine-tuning them on natural language instructions. It proposed two new requirements for metrics used to evaluate IFT models: Comparability Across Task (CAT) and Task and Format Agnostism (TFA). Showed that using LLMs as scoring agents is a good way to meet these requirements, and compares them with existing metrics such as BLEU, ROUGE, and GLUE. The work also demonstrated how synthetic data can be used to improve formatting skills, and how IFT models can reduce the need for expert data in industrial settings.

Pros: 
1: Introduced new evaluation metrics that are more suitable for measuring the performance of IFT models across different tasks and formats, which can help researchers and practitioners compare and benchmark their models more effectively.
2: Provided practical insights and recommendations for fine-tuning IFT models on different tasks, and shows how synthetic data and IFT models can reduce the dependency on expert data in industrial settings, which can lower the cost and complexity of deploying IFT models in production.
Decision: Accept-Main