Adjusting Interpretable Dimensions in Embedding Space with Human Judgments (2024)

Abstract: Embedding spaces contain interpretable dimensions indicating gender, formality in style, or even object properties. This has been observed multiple times. Such interpretable dimensions are becoming valuable tools in different areas of study, from social science to neuroscience. The standard way to compute these dimensions uses contrasting seed words and computes difference vectors over them. This is simple but does not always work well. We combine seed-based vectors with guidance from human ratings of where words fall along a specific dimension, and evaluate on predicting both object properties like size and danger, and the stylistic properties of formality and complexity. We obtain interpretable dimensions with markedly better performance especially in cases where seed-based dimensions do not work well.

## introduction properties are commonly used in linguistics @xcite @xcite as well as in psychology @xcite for representing word meanings and concepts. those same properties are discoverable as interpretable dimensions in word embedding space, and can be used to explore the patterns and regularities encoded by large language models (llms) @xcite . because llms are trained on texts from many different authors, we can view them as a compact repository of human utterances. this makes them an interesting resource for studying linguistic phenomena, analyzing social contexts of words, or as a standin for conceptual knowledge for interpreting brain voxels. interpretable dimensions provide an attractive and simple way to access this resource @xcite @xcite . compared to probing @xcite , interpretable dimensions allow for a direct exploration of llm embedding space, without external classifiers. the most common way to obtain interpretable dimensions is to specify some seed pairs of antonyms, and take the average over their vector differences. but it is unclear what makes good seed pairs, or even how to test whether a particular property corresponds to a discernible dimension in embedding space. @xcite and @xcite express concerns about the quality of commonly used hand-curated seed lexicons and propose metrics for evaluating seeds. in this paper, we take a different approach to addressing the problem of "bad seeds" @xcite : we propose a method to augment seed-based interpretable dimensions with additional guidance from human ratings, and we show that this augmentation is particularly impactful when the original seed-based dimensions are problematic. 1 figure 1 shows words for clothes with dimensions for wealth, and animal names with dimensions for size, blue for seed-based dimensions and red for our new fitted dimensions. the fitted dimensions correct overly high wealth estimates for tights and stockings, and exaggerated size estimates for bee and ant. while interpretable dimensions are useful both to social science and to cognitive science, there is an important difference between the fields: in social science, crowd-sourced datasets cannot be trusted in absolute terms, because annotators may have social biases of their own; this is the scenario that @xcite address. in cognitive science however, experimental data from human participants is central (though the method used to solicit data can have an influence on the outcome). we work with data from cognitive science here, so we can use human ratings to improve seed dimensions. our new method draws inspiration from a completely separate strand of research on interpretable dimensions, in the context of knowledge graph embeddings @xcite @xcite . there, interpretable dimensions are learned using labeled training data. in the current paper, we use a similar learning strategy, and apply it to a combination of seed-based dimensions and labeled training data. we apply this technique to predict human ratings on object properties and stylistic aspects of words, and find that it improves performance particularly in cases where seed-based dimensions underperform, and that in contrast to seed-based dimensions it is able to make predictions at the same scale as the original ratings. a larger issue is: when can we trust that an interpretable dimension shows us what the llm truly "knows" about the property in question, that we are not misled by noise in our tool? this same worry also arises for probing classifiers @xcite . 2 we take one step towards addressing this issue: by combining two sources of information, seeds and human annotation, we hope to reduce noise present in either source. ## related work interpretable dimensions in word embedding space have first been observed in nlp (bolukbasi et al., 2 there is a related question of whether information encoded in the model is also relevant for downstream performance @xcite . this question is not so central for linguists or cognitive scientists interested in knowledge reflected in an llm. what is central for them is that the dimension really encodes the property of interest. in all these studies, dimensions are discovered using the seed-based methodology, where a few seed pairs of antonyms are specified and the dimension is computed as the average over vector differences for these pairs. this method is simpler than alternative representation approaches (e.g., the multi-task learning framework of @xcite ). seed pair selection has until now been ad hoc; but some choices, such as the selected word pairs, their number and order, and the way they are combined, have a strong impact on the quality of the derived dimension. @xcite address the "bad seeds" problem by measuring the coherence of each seed set pairing after mapping to the bias subspace: when all words in the vocabulary are projected onto the subspace, the two seed sets must be drawn as far apart as possible. @xcite propose to measure the semantic axis' self-consistency using a leave-one-out approach, where each seed is compared to an axis constructed from the remaining seeds. a good seed, when left out, should be closer to the pole it belongs to. in our approach, we do not test for seed quality. instead, we use human ratings to improve on seed-based dimensions. our approach is inspired by work on knowledge graph embeddings @xcite @xcite . drawing on the conceptual spaces of @xcite for intuition, @xcite learn embeddings of knowledge graph nodes that include interpretable dimensions for properties. like us, they learn interpretable dimensions using labeled training data. our objective function is an adaptation of their objective function, but still different as they also learn the space while we have a fixed space. for constructing interpretable dimensions, most previous work used static embeddings (glove @xcite and word2vec @xcite ). recent work extends the methodology to contextualized representations @xcite . we experiment with both kinds of embeddings. ## results and discussion in this section we evaluate the different interpretable dimension models from section 3.1 on the tasks of predicting human ratings of object properties @xcite , and human ratings of the complexity and formality of words @xcite . ## conclusion in this paper we have proposed a method for constructing high quality interpretable dimensions in embedding spaces. we show that by combining seed-based vectors with guidance from human ratings about properties, it is possible to induce better dimensions than with the seed-based methodology alone. we expect the proposed dimensions to be useful in various areas of study, including linguistics, psychology, and social science. for the moment, the proposed dimensions address one property at a time. in future work, we are planning to explore multifaceted properties which would be better represented through multiple dimensions. aside from a more elaborate description of these properties, a space of multiple interpretable dimensions will offer a rich context of comparison for words that might be similar in some respect and not in others (e.g., tiger and spider with respect to danger and size).