Decoding Symbolism in Language Models (2023)

Abstract: This work explores the feasibility of eliciting knowledge from language models (LMs) to decode symbolism, recognizing something (e.g.,roses) as a stand-in for another (e.g., love). We present our evaluative framework, Symbolism Analysis (SymbA), which compares LMs (e.g., RoBERTa, GPT-J) on different types of symbolism and analyze the outcomes along multiple metrics. Our findings suggest that conventional symbols are more reliably elicited from LMs while situated symbols are more challenging. Results also reveal the negative impact of the bias in pre-trained corpora. We further demonstrate that a simple re-ranking strategy can mitigate the bias and significantly improve model performances to be on par with human performances in some cases.

## introduction symbolism is an important literary device that helps to persuade ideas concisely @xcite . a system that can decode symbolism should recognize that one item (e.g., a baby) is a stand-in for something else (e.g., innocence). it has applications in understanding persuasive texts as well as the visual media @xcite @xcite . for example, a social media moderator needs to know that certain seemingly benign phrase or object may signal some banned behavior; an intelligent writing tutor should recognize (in)appropriate usages of symbolism in student essays; a persuasive text/image generator may convey its message more effectively by appropriate uses of symbolism. with these potential applications in mind, this work explores whether state-of-theart lms encapsulate enough implicit and abstract knowledge to infer symbolic relationships. specifically, we ask: given some observed physical object or content (referred to as the signifier), can lms predict an appropriate corresponding conceptual symbolic reference (referred to as the signified) 1 ? decoding symbolism is a challenging task (even for humans). first, symbols serve many different purposes, from representing figures of speech and modes of thought to denoting various signs, passwords, and customs @xcite . thus, some types of signifier-signified relationships may be more difficult to decode than others. prior work suggests that lms encapsulate some commonsense knowledge @xcite ; therefore, we anticipate lms may capture the more semantically related symbolic relationships (e.g., a fork as a symbol of food because it is usedfor eating), but what about those involving a longer chain of reasoning? how do additional factors such as the complexity of the lm and the choice of the prompt impact the performances of different lms? second, symbols may be situational: the same signifier may be a stand-in for different references under different scenarios. for example, while a baby often represents innocence, when depicted as being held by a harried parent, that baby comes to symbolize burden and responsibility. it is crucial to examine the extent to which lms can identify the appropriate signified concept based on the situational context. finally, while symbolism is often used to emphasize common human concepts (e.g., love), it is also an apt device to represent rare, difficult concepts. this dichotomy poses a challenge for lms, which are susceptible to biases from their pre-training corpora @xcite @xcite because the bias leads to a strong preference for the more commonly signified concepts (e.g., love) while penalizing symbolic links with rarer words. to assess their capacity to decode symbolism, we have developed an evaluative framework called symba (symbolism analysis) to empirically com-pare three classes of lms: word embedding (word2vec), which serves as a baseline, masked (bert and roberta), and autoregressive (gpt-2 and gpt-j). the evaluative task is: given a prompt containing a signifier, return a ranked list of potential signified concepts. models are also evaluated on a multiple-choice task against a human upperbound. two sets of evaluative data 2 are curated to highlight different aspects of the symbolic relationships. one set consists of conventional symbol pairs that we compiled from commonly used symbols in english literature, which tend to be context invariant. the other is a subset that we sampled from a visual advertisement corpus @xcite that contains situated symbolic pairs; the local context immediately surrounding the signifier and the intended signified are annotated by humans. by modifying the prompt to exclude/include the local description, we observe the impact of the situated context. additional fine-grained categorizations of the evaluative data help to reveal the characteristics of symbolic relationships that pose the greatest challenge to the lms. moreover, we propose ways for quantifying and tempering the bias in lms favoring commonly signified concepts. overall, we find that lms can capture aspects of symbolic knowledge, with the newer, larger models significantly outperform their previous iterations. surprisingly, advanced lms performed better on conventional symbolism (more idiomatic) than symbolism in ads (more semantically related), where they fared significantly worse than word2vec. this reveals the negative impact of the hypothesized bias in pre-training corpora. we demonstrate that the proposed debiasing method improves performance; the increase is the most dramatic for situated ads symbols (e.g., roberta improved by 260%). after reranking, gpt-j and roberta achieve performances comparable to human on the multiple choice task. further analyses suggest lms perform better on explicit relationships such as usedfor than implicit ones, and the debiased models are sufficiently robust with respect to the probing prompts. 3 2 both datasets are predominantly expressed in english so that our studies focus on the interpretation of symbolism from a western cultural perspective @xcite ; this englishdominant scope matches the pre-training corpora of lms. ## background decoding symbolism the use of symbolism is an important literary device that helps authors to write more persuasively and convey more ideas in fewer words. to gain a deeper understanding of what is communicated, nlp systems need to be able to decode symbolic usages in text. to our knowledge, this is an under-explored problem in nlp, though there has been related work on recognizing metaphoric and idiomatic usages @xcite @xcite @xcite . like symbols, metaphors and idioms also replace some intended target concept with different words; however, a metaphor emphasizes some common property it shares with the target concept. an idiom is an expression that conveys a fixed target meaning that is not composed from the literal meaning of its individual words. in contrast, a symbol serves as a stand-in for a more complex and abstract concept under certain context; it may not share any obvious property with the abstract concept, and it may not be associated with solely one concept @xcite . beyond metaphor recognition, our objectives are also aligned with metaphor interpretation, which aims to connect the surface and target concepts @xcite @xcite . some prior approaches explored connecting them through shared features or logical sequences, but such a path may not exist for symbolism. instead of searching for a path through a discrete space, we elicit the signified associated with the given signifier from the implicit representation of a trained language model. a somewhat related idea was recently investigated by @xcite in which a metaphoric verb is masked so that the language model could predict a more literal verb given the surrounding context. different from our objectives, however, their work does not require the language model to capture the relationship between the metaphoric verb and the literal verb; in contrast, our work explicitly investigates whether a language model will predict the appropriate signified when probed with a signifier. language models since language models serve as the basis of our symbol decoder, we discuss two common approaches. their training regimes lead to different token representation that may impact the ability of each to associate an appropriate signified with the given signifier. autoregressive language models are trained to predict the ground-truth next token given previous ones. pretrained autoregressive language models such as gpt @xcite @xcite are able to generate fluent and coherent human-sounding sentences; however, they can only generate text along one direction and have no access to the context on the other side. masked language models are trained to predict the ground-truth masked token given the right and left context. bert and its variations fall in this group @xcite . bidirectional attention helps the model learn more complete representations of tokens than the unidirectional models. consequently, masked language models usually achieve better performance after fine-tuning on downstream nlp tasks than the autoregressive models. however, they underperform on text generation because of the masking scheme and the independence assumption between masked tokens @xcite . scoring by pmi pmi has been used for scoring candidates in many nlp applications, including zero-shot question answering @xcite , surface form competition @xcite , dialogue generation @xcite as well as knowledge elicitation from language models @xcite . in the context of this work, it serves as a means to re-rank the strength of association between signfier-signified pairs and a method of analysis to identify situations for which re-ranking improves performance. ## symba probe we introduce the symba (symbolism analysis) framework for evaluating language model's ability to decode symbols. symba includes 1066 symbolic pairs from two data sources, a debiasing method and two analytical tools. ## experiments we first evaluate the performance of different language models for decoding the symbolism, with or without the situated context. we then conduct experiments for verifying the biased-prior hypothesis as well as measuring the effectiveness of the debiasing method. we further investigate the finegrained performance with respect to the knowledge difficulty and the relationship types. ## conclusion in this work, we have assessed the feasibility of eliciting symbolic knowledge from different types of language models. by evaluating lms through the symba probe, we find that advanced large language models (e.g. gpt-j and roberta) can achieve human-level performance on a simplified 4-choice task of identifying the intended signified concept from a given signifier. however, there is still ample room for improvement when the model is prompted to select from a large set of candidates. we have also validated that these models are biased in favor of commonly occurring signified concepts. the debiasing method based on re-ranking can significantly improve the performance and increase the robustness with respect to the probing template. our work shows the potential of incorporating language models as a source of knowledge about symbolic relationships for real-world applications that involve understanding and interpreting non-literal expressions.