SlowGuess/NAACL_2022_OCR huggingface dataset:
(b) BERTscore.
Table 3: A comparison of the basic infilling prompt (B; "<extra_id_0> because <extra_id_1>") with its more natural sounding version (N; see §3.2).
Task	Accuracy	BERTscore
INFILLING	E-SNLI	75.10.4	67.50.4
ECQA	27.60.4	24.50.3
ComVE	49.00.3	44.30.3
SBIC	64.70.5	62.00.5
Average	54.1	49.6
T5	E-SNLI	79.20.3	71.30.3
ECQA	38.30.3	33.90.3
ComVE	55.90.3	50.40.3
SBIC	65.10.6	62.80.6
Average	59.6	54.6
QASIMPLE	E-SNLI	75.00.3	67.50.3
ECQA	41.40.3	36.70.3
ComVE	67.30.7	61.00.6
SBIC	67.50.4	65.30.4
Average	62.8	57.6
Table 4: A comparison between three prompt types: INFILLING, ≈T5, and QASIMPLE prompts. See §3 for descriptions of these prompts.
cept E-SNLI for which unsurprisingly  $\approx$ T5 is the best. Finally, this brings us to the end of our extensive exploration of natural language prompts for a prompt-based finetuning approach to few-shot self-rationalization. We identify the QASIMPLE prompt as the most effective and we use it to study how few-shot self-rationalization performance scales with the size of the UNIFIEDQA model.
4 Improving Self-Rationalization with Increasing Model Size
In §3, we discovered that a QA prompt combined with the base UNIFIEDQA model version is as an effective combination for few-shot self-rationalization through prompt-based finetuning. In this section, we provide two additional evaluations to establish the first approach to few-shot self-rationalization.
First, we assess how plausible the generated explanations are when evaluated by annotators on Amazon MTurk. Details of how we conduct human evaluation of plausibility are given in §2. One HIT contains 10 instances and we pay $1 per HIT.
Next, we investigate how self-rationalization performance changes with the model size since larger pretrained language models typically give better few-shot performance (Brown et al., 2020). We wonder whether the same trend will hold for a complex generation task of self-rationalization where
Model	Accuracy	BERTscore	All	Plausibility
Score	κ	Score	κ	Score	κ	Score	κ
E-SNLI	BASE	79.20.3	71.30.3	16.71.5	0.73	15.62.3	0.67	17.52.9	0.79	17.12.7	0.72
LARGE	84.80.3	76.60.3	32.71.9	0.57	27.32.9	0.43	33.93.4	0.64	36.83.6	0.64
3B	87.40.2	79.10.2	41.62.1	0.62	27.12.8	0.52	46.83.8	0.70	50.93.6	0.64
GPT-3	65.40.5	59.80.5	42.42.2	0.54	27.32.9	0.48	66.04.4	0.71	43.83.5	0.51
GOLD	77.41.6	0.63	63.53.0	0.44	87.91.8	0.74	82.52.4	0.72
RAND	33.3
ECQA	BASE	41.40.3	36.70.3	25.51.2	0.32
LARGE	57.20.4	51.00.3	30.31.5	0.38
3B	65.90.4	59.00.3	34.21.6	0.35
GPT-3	60.61.5	54.41.3	45.11.4	0.12
GOLD	70.91.5	0.45
RAND	20.00
COMVE	BASE	67.30.7	61.00.6	13.81.3	0.45
LARGE	81.30.4	73.90.4	25.61.7	0.52
3B	89.00.4	81.00.3	33.41.7	0.63
GPT-3	74.01.4	67.61.3	42.21.8	0.73
GOLD	77.21.3	0.55
RAND	50.0
SBIC	BASE	67.50.4	65.30.4	58.02.2	0.68	21.42.1	0.54	94.61.1	0.82
LARGE	71.10.4	68.50.4	61.82.2	0.66	27.22.2	0.43	96.50.9	0.89
3B	71.70.5	68.90.5	64.22.1	0.68	33.82.6	0.55	94.61.0	0.81
GPT-3	74.21.4	71.51.4	72.71.7	0.53	52.62.5	0.34	92.71.0	0.72
GOLD	79.81.6	0.67	64.92.7	0.52	94.71.0	0.81
RAND	50.0
Table 5: The first results on the FEB benchmark using T5/UNIFIEDQA (BASE, LARGE, 3B) and GPT-3. T5 with ≈T5 prompt is used only for E-SNLI, and UNIFIEDQA + QASIMPLE prompt is used for other datasets. The descriptions of these prompts are given in §3 and details of how evaluation metrics are calculated in §2. RAND stands for a random baseline and GOLD for human-authored explanations. Label1/Label2/Label3 are entailment/neutral/contradiction in E-SNLI and offensive/not offensive in SBIC. The number of parameters is: 200M (BASE), 770M (LARGE), 2.8B (3B), and 175B (GPT-3).
it is conceivable that an enormous model could overfit on a few examples. To this end, we evaluate three versions of UNIFIEDQA (BASE, LARGE, 3B) and GPT-3 (Brown et al., 2020). We use davinci-instruct-beta which is a beta version of the INSTRUCTGPT model (Ouyang et al., 2022).
We evaluate GPT-3 using its API and "in-context demonstrations" (Brown et al., 2020). We pack as many training examples (demonstrations) as we can fit in the input, followed by the input of the test example, then run GPT-3 to generate its output. The number of demonstrations we are able to fit ranges from [28,45] which are randomly
selected from the 48 used for UNIFIEDQA. Since evaluation using a single prompt costs us $1,050, we do not do prompt search for GPT-3. We use the prompts shown in Fig. 1 in Appendix.
A detailed description of evaluation metrics is given in §2. The dev set size (of each out of 60 dev sets) for GPT-3 is 18 instead of 350 (because of the API cost). Ground-truth explanations are evaluated together with explanations generated by 4 models. Therefore, for GOLD explanations, we report the average of 4 plausibility scores, std. errors, and  $\kappa$  values calculated with 4 Mturk batches (corresponding to 4 models).
Results are shown in Table 5. Note that we use T5 with the  $\approx$ T5 prompt for E-SNLI, and UNIFIEDQA with QA SIMPLE (§3) for other datasets to establish the best possible performance for each dataset. The exact prompts for each task are given in Appendix A.2. We observe that all metrics—accuracy, BERTscore, and plausibility—monotonically increase with the model size for all datasets. That is, larger models learn to predict task labels and generate explanations from a few examples better. UNIFIEDQA-3B has a higher accuracy/BERTscore than GPT-3 for all datasets except SBIC, but GPT-3 generates explanations that are notably more plausible.
The following observations suggest that few-shot self-rationalization is a promising research direction. The difference in plausibility scores between the BASE and 3B model versions ranges from [6.2, 24.8] (on average 14.8). In other words, since it is possible to generate more plausible explanations by only increasing the model size, it is conceivable that further progress could be made with more creative approaches. Next, the plausibility score of the best model (GPT-3) ranges from [42.2, 72.7] ([42.2, 52.6] if we consider only SBIC "offensive"  $(Label_{1})$  subset. This shows that a moderate plausibility can already be achieved with current models without any task-specific enhancements.
Despite that, the gap between our best models and human-authored explanations remains large. The average plausibility score across datasets is 43.4 (UNIFIEDQA-3B), 50.6 (GPT-3), and 76.3 (GOLD). In other words, the difference in plausibility scores between UNIFIEDQA-3B's and human explanations is 33.0, and between GPT-3's and human explanations is 25.7. We expect that the FEB benchmark, our UNIFIEDQA approach, and first results, present a good starting point to tackle this challenge.
Performance w.r.t. Labels For E-SNLI and SBIC, we can inspect the metrics with respect to labels. In E-SNLI part of the Table 5,  $Label_1$  marks "entailment",  $Label_2$  "neutral", and  $Label_3$  "contradiction". There are notable differences between the plausibility scores for each label. The plausibility score for "entailment" does not scale with the model size and it is much lower than scores for other labels (the best score is 27.3 vs. 66.0/50.9). This issue stems from the difficulty of explaining the entailment label (Camburu et al., 2018). Even
people struggle with explaining "entailment" as evident by the lower GOLD score for "entailment" compared to the other two labels. An interesting observation from the other two labels is that UNIFIEDQA-3B is explains "contradiction" instances best and GPT-3 "neutral" instances.
In SBIC part of the Table 5,  $Label_{1}$  marks "offensive" and  $Label_{2}$  "not offensive" instances. The latter achieve almost perfect plausibility since the models learn to generate "This post does not imply anything offensive". Thus, main plausibility scores for SBIC are those of offensive instances. We can observe that the relative differences between models for offensive instances are much larger than the relative differences when examples of both labels are accounted for (column "All / Score"). If we had only looked into a single plausibility score we would not notice these differences. This result is in line with Carton et al. (2020) who also recommend breaking down the evaluation of explanations w.r.t. labels whenever possible.
Annotator Agreement Finally, we observe challenges in collecting human judgments of plausibility. For all datasets except ECQA, Fleiss'  $\kappa$  is either moderate (between 0.41-0.6) or substantial (between 0.61-0.8). One exception is GPT-3 on SBIC (Label<sub>1</sub>; offensive) where  $\kappa$  is only 0.34. We also observe that  $\kappa$  for GPT-3's explanations is lower than  $\kappa$  for UNIFIEDQA's or GOLD explanations, with the exception of COMVE. The most concerning is ECQA where  $\kappa$  is on average 0.35 for UNIFIEDQA's explanations, 0.34 for GOLD explanations, and only 0.12 for GPT-3's. Future work should investigate the reasons behind these differences more carefully.
Few-Shot Self-Rationalization A standard approach to creating explanations in the form of highlights is the select-then-predict method (Lei et al., 2016) that does not use any human-author input highlights. On the other hand, a standard method for generating free-text explanations is to use human-written explanations (Liu et al., 2019; Wu and Mooney, 2019; Narang et al., 2020, among others). To the best of our knowledge, prior to submitting our work only two prior works have generated free-text explanations in a weakly-supervised way from the task prediction loss. Latcinnik and Berant (2020) approach commonsense QA in that fashion. Brahman et al. (2021) propose a distant
supervision approach to explaining a defeasible inference task. In this paper, we introduce the FEB benchmark to unify the evaluation of few-shot self-rationalization and present the first approach and results on FEB.
Concurrent to our work, Yordanov et al. (2021) study self-rationalization transfer from a highresource task to a task with only a few humanauthored explanations. Wiegreffe et al. (2022) analyze explanations obtained by prompting GPT-3 multiple times to get multiple explanation candidates, and then filter these candidates using a model trained to predict acceptability of explanations. Their prompt consists of a few examples with high-quality explanations written by the authors and a new instance together with its gold label. Wei et al. (2022) demonstrate end-task performance improvements attained by prompting the PaLM model (Chowdhery et al., 2022) to first generate an explanation behind its reasoning ("chain of thought") and then the task label. Zelikman et al. (2022) extend this approach by using explanations generated in a few-shot manner to refine the same GPT-J (Wang and Komatsuzaki, 2021) model.
Few-Shot Learning We study natural language prompts (Brown et al., 2020; Schick and Schütze, 2021) to establish the first approach to few-shot self-rationalization. Alternatively, few-shot learning researchers are studying prompts in the form of continuous/soft vectors that do not correspond to real tokens (e.g., Qin and Eisner, 2021). Such methods present a promising research direction for few-shot self-rationalization. Namely, we show that larger models generate notably more plausible explanations, and "prefix tuning" (Li and Liang, 2021) has been shown to learn two condition generation tasks using only  $0.1\%$  of the parameters, while maintaining comparable performance. In practice, such approaches still require a notable amount of GPU memory. Thus, any efforts to reduce required memory such as compression (Ganesh et al., 2021) may be valuable for few-shot self-rationalization.
We draw attention to the task of few-shot self-rationalization: predicting task labels and generating free-text explanations for the prediction using only a few human-written explanations. We present (i) the FEB benchmark, (ii) the first prompting approach for FEB established through a comprehensive search of natural language prompts, and (iii)
results using models with a number of parameters ranging from 220M to 175B. Our human evaluation results show that progress is possible on this task given that just scaling the model size increases both the plausibility of generated explanations and task accuracy by a very large margin. Despite that, few-shot self-rationalization remains very challenging, with the plausibility of explanations generated by the best model being 27.7 points behind that of human-authored explanations. We hope that work presented in this paper spurs the community to work on this challenging problem to enable more intuitive interaction with NLP systems.
The authors thank members of the AllenNLP team and anonymous reviewers for helpful feedback.
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021. *Explanations for CommonsenseQA: New Dataset and Models*. In *Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics* and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3050–3065, Online. Association for Computational Linguistics.
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021. Flex: Unifying evaluation for few-shot nlp. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).
Faeze Brahman, Vered Shwartz, Rachel Rudinger, and Yejin Choi. 2021. Learning to rationalize for nonmonotonic reasoning with distant supervision. In Proceedings of the AAAI Conference on Artificial Intelligence.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877-1901. Curran Associates, Inc.
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018. e-SNLI: Natural language inference with natural language expla
nations. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).
Samuel Carton, Anirudh Rathore, and Chenhao Tan. 2020. Evaluating and characterizing human rationales. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9294-9307, Online. Association for Computational Linguistics.
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Baindoor Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Oliveira Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. Palm: Scaling language modeling with pathways. arXiv:2204.02311.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, and Marianne Winslett. 2021. Compressing Large-Scale Transformer-Based Models: A Case Study on BERT. Transactions of the Association for Computational Linguistics, 9:1061-1080.
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3816-3830, Online. Association for Computational Linguistics.
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, and Sewon Min. 2019. Question answering is a format; when is it useful? arXiv:1909.11291.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring massive multitask language understanding. In *The International Conference on Learning Representations (ICLR)*.
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423-438.
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv:2001.08361.
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski, Cornelius Emde, Virginie Do, Zeynep Akata, and Thomas Lukasiewicz. 2021. e-vil: A dataset and benchmark for natural language explanations in vision-language tasks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaheh Hajishirzi. 2020. UNIFIEDQA: Crossing format boundaries with a single QA system. In *Findings of the Association for Computational Linguistics: EMNLP* 2020, pages 1896–1907, Online. Association for Computational Linguistics.
Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, and Deepak Ramachandran. 2021. Which linguist invented the lightbulb? presupposition verification for question-answering. arXiv:2101.00391.
Veronica Latcinnik and Jonathan Berant. 2020. Explaining question answering models through text generation. arXiv:2004.05569.
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing neural predictions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 107-117, Austin, Texas. Association for Computational Linguistics.
Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582-4597, Online. Association for Computational Linguistics.
Hui Liu, Qingyu Yin, and William Yang Wang. 2019. Towards explainable NLP: A generative explanation framework for text classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5570-5581, Florence, Italy. Association for Computational Linguistics.
Ana Marasović, Chandra Bhagavatula, Jae sung Park, Ronan Le Bras, Noah A. Smith, and Yejin Choi. 2020. Natural language rationales with full-stack visual reasoning: From pixels to semantic frames to commonsense graphs. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2810-2829, Online. Association for Computational Linguistics.
David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020. WT5?! Training Text-to-Text Models to Explain their Predictions. arXiv:2004.14546.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. 2022. Training language models to follow instructions with human feedback. arXiv:2203.02155.
Guanghui Qin and Jason Eisner. 2021. Learning how to ask: Querying LMs with mixtures of soft prompts. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5203-5212, Online. Association for Computational Linguistics.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67.
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain yourself! leveraging language models for commonsense reasoning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4932-4942, Florence, Italy. Association for Computational Linguistics.
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics.
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon. 2011. Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In AAAI Spring Symposium Series.
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong. 2021.