Explanation Regeneration via Information Bottleneck (2023)

Abstract: Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Thanks to the superior generative capacity of large pretrained language models (PLM), recent work built on prompt engineering enables explanations generated without specific training. However, explanations generated through single-pass prompting often lack sufficiency and conciseness, due to the prompt complexity and hallucination issues. To discard the dross and take the essence of current PLM’s results, we propose to produce sufficient and concise explanations via the information bottleneck (EIB) theory. EIB regenerates explanations by polishing the single-pass output of PLM but retaining the information that supports the contents being explained by balancing two information bottleneck objectives. Experiments on two different tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.

## introduction natural language explanations have attracted a lot of attention as a way to uncover the rationales behind black-box predictions. thanks to the power of large pretrained language models (plm) @xcite , prompting methods proposed in recent studies achieve impressive results in generating free-text explanations @xcite . a clear advantage of such methods is that they involve no additional training from task-specific datasets. in this paper, we regard a free-text explanation as a description of the relationship between an input context and a hypothesis, e.g., a question and an answer. although it is difficult to state that one th r o w keep 𝑥 # giving ... teachers. ## method prompting recently, writing explanations through prompting large plms has become a competitive approach. given an nlp task sample 𝒛 including input 𝒛 𝑐 and output 𝒛 𝑜 , we could infer its explanation 𝒙 via prompting a plm: 𝒙 = plm(𝑆(𝒛 𝑐 , 𝒛 𝑜 )), where function 𝑆(•, •) transforms 𝒛 to prompt formats through predefined templates. for example, if we have a qa sample, question 𝒛 𝑐 : can elephants be put in the fridge? and answer 𝒛 𝑜 : no, the prompt will be "the question is can elephants be put in the fridge? the answer is no because.". although prompting has achieved remarkable success, machine-generated explanations still have room for improvement as discussed in the introduction. therefore, we seek to step further under the current achievement, exploring an effective way to improve explanation quality in terms of meaningfulness, sufficiency, and conciseness. formulation suppose we have a sample 𝒛 ∈ z and its explanation hypothesis 𝒙 ∈ x. 2 we aim to refine 𝒙 into a better 𝒙 ′ which can: (1) reduce irrelevant information in 𝒙 (conciseness), ( 2 ) preserve and supplement useful information to infer 𝒛 (meaningfulness, sufficiency). we divide the explanation regeneration task into two problems: refinement and generation. first, we model the refinement problem from an information-theoretic view, i.e., learn the internal representation t of the initial explanation x, defined as 𝑝 𝜃 (𝒕 | 𝒙), such that t is maximally compressive about the (noisy) x while being maximally expressive about z: the above process can be formulated as the information bottleneck principle (ib) @xcite . ib defines the characteristics of an optimal representation, in terms of the fundamental tradeoff between having a concise representation and one with good predictive power, which is equivalent to minimizing the following objective function: given a bottleneck representation 𝒕, our second goal is to generate a free-text explanation 𝒙 ′ based on 𝒙. therefore, we pack a log-likelihood objective for language modeling with l ib as the objective function of the whole model, and train it on an automatically constructed synthetic dataset: the overall proposed eib is illustrated in figure 2 . in the following, we will present the optimization and training with respect to (i) explanation compression for distilling a bottleneck representation from the initial explanation, (ii) information preservation for ensuring the distilled bottleneck representation expressive about the explained sample, and (iii) explanation regeneration from the distilled bottleneck representation for producing a better explanation than the initial input one. ## related work textual explanations in free-text forms are more expressive and generally more readable @xcite . recent methods in free-text explanation generation could be divided into two types: supervised learning on labeled datasets @xcite @xcite and unsupervised learning with large-scale pre-trained language models (plm) @xcite @xcite @xcite . the success of zero-shot models @xcite drives research in a more reference-free way and saves annotation costs. a common strategy to encourage a plm to produce explanations is to directly describe the input sample as context to the plm, which has no guarantee for being supportive and organized explanations at one time @xcite @xcite . by contrast, eib learns to distil task-relevance information from the initial explanations of plm and regenerates sufficient and concise explanations with distant supervision from an automatically-constructed dataset. information bottleneck (ib) provides an information perspective to explain the performance of neural networks @xcite . ib measures the mutual information between random variables and is powerful, especially for unsupervised learning @xcite , which has been adapted in various nlp downstream applications @xcite @xcite @xcite , balancing a tradeoff between task irrelevance and task objectives. we are interested in refining the unqualified explanation candidates into sufficient and concise ones with the guidance of the explained tasks by managing two ib objectives. to the best of our knowledge, we are the first to apply the information bottleneck principle to generate explanations that adhere to explanatory criteria. ## conclusion natural language explanations have attracted a lot of attention because free-text explanations are more expressive and generally more readable. however, the quality of machine-generated explanations still face challenges, e.g., inadequate evidences or redundancy expressions, even with large plms. in this work, we propose to produce sufficient and concise explanations via the information bottleneck theory (ib), where explanations are regenerated by refining the single-pass outputs from plm but keeping the information that supports the explained samples under a tradeoff between ib objectives. we automatically construct pseudo-parallel data for training eib to autoregressively generate new explanations. experiments on two tasks show that eib is effective for generating sufficient and concise explanations. besides, our extensive analysis shows that the current automatic evaluation for free-text explanation is extremely difficult, and persuasive evaluation frameworks are encouraged to compensate for conventional automatic metrics.