UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2867-2889, 2022.

Abstract

An extractive rationale explains a language model’s (LM’s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM’s actual behavior) and plausible (convincing to humans), without compromising the LM’s (i.e., task model’s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (\ie faithfulness and plausibility criteria); and (3) jointly train the task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works’ heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. On five English text classification datasets, our best UNIREX configuration outperforms baselines by an average of 32.9% NRG. Plus, UNIREX rationale extractors’ faithfulness can even generalize to unseen datasets and tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-chan22a, title = {{UNIREX}: A Unified Learning Framework for Language Model Rationale Extraction}, author = {Chan, Aaron and Sanjabi, Maziar and Mathias, Lambert and Tan, Liang and Nie, Shaoliang and Peng, Xiaochang and Ren, Xiang and Firooz, Hamed}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {2867--2889}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/chan22a/chan22a.pdf}, url = {https://proceedings.mlr.press/v162/chan22a.html}, abstract = {An extractive rationale explains a language model’s (LM’s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM’s actual behavior) and plausible (convincing to humans), without compromising the LM’s (i.e., task model’s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (\ie faithfulness and plausibility criteria); and (3) jointly train the task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works’ heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. On five English text classification datasets, our best UNIREX configuration outperforms baselines by an average of 32.9% NRG. Plus, UNIREX rationale extractors’ faithfulness can even generalize to unseen datasets and tasks.} }
Endnote
%0 Conference Paper %T UNIREX: A Unified Learning Framework for Language Model Rationale Extraction %A Aaron Chan %A Maziar Sanjabi %A Lambert Mathias %A Liang Tan %A Shaoliang Nie %A Xiaochang Peng %A Xiang Ren %A Hamed Firooz %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-chan22a %I PMLR %P 2867--2889 %U https://proceedings.mlr.press/v162/chan22a.html %V 162 %X An extractive rationale explains a language model’s (LM’s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM’s actual behavior) and plausible (convincing to humans), without compromising the LM’s (i.e., task model’s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (\ie faithfulness and plausibility criteria); and (3) jointly train the task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works’ heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. On five English text classification datasets, our best UNIREX configuration outperforms baselines by an average of 32.9% NRG. Plus, UNIREX rationale extractors’ faithfulness can even generalize to unseen datasets and tasks.
APA
Chan, A., Sanjabi, M., Mathias, L., Tan, L., Nie, S., Peng, X., Ren, X. & Firooz, H.. (2022). UNIREX: A Unified Learning Framework for Language Model Rationale Extraction. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:2867-2889 Available from https://proceedings.mlr.press/v162/chan22a.html.

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