Sentence-Space Metrics ( SSM ) for the Evaluation of Sentence Comprehension (2024)

Abstract: “It is a fundamental challenge to evaluate whether a model can truly capture the meaning ofsentences. Evaluation of whether a model well captures the meaning of individual words, how-ever, can be effectively achieved by analyzing whether the model encodes words in a vectorspace where semantically similar words form clusters. Inspired by this approach, we propose theSentence-Space Metrics (SSM) to evaluate model interpretation of sentences, and the sentencespace is constructed based on the pairwise entailment relationships between all sentence pairswithin a sentence pool. We use three metrics to evaluate a sentence space, i.e., (1) sparsity, (2)clustering of related sentences, and (3) similarity with the sentence space measured from hu-mans. The SSM is applied to evaluate 20 models, including ChatGPT, 18 BERT-family modelsfine-tuned for Natural Language Inference (NLI) task, as well as SimCSE, a sentence representa-tion model. The SSM reveals dramatic differences among models: Although all models achievehigh accuracy on standard NLI datasets such as MNLI, none of them mirrors the human behaviorunder the SSM. These results demonstrate that, compared with traditional accuracy measures,the SSM considers pairwise relationships between hundreds of sentences and therefore providea more fine-grained evaluation of model interpretation of sentences.Introduction”

## introduction how to represent the meaning of a word or a sentence is a classic question in philosophy, linguistics, and psychology @xcite @xcite @xcite . converging evidence from natural language processing (nlp) studies and human neuroimaging studies suggests that the meaning of a word can be well described by its relationship with other words. for example, word embedding algorithms project words into a semantic space in which words with more similar meanings are more closely located @xcite @xcite . similarly, human neuroimaging studies have shown that semantic space for words can be constructed based on human rating of the similarities between words and predicts how similarly the brain responds to different words @xcite . in human language, words are only the basic elements to express meaning, while the unique expressive power relies on the construction of sentences -we use sentences to represent compositional semantic structures, including events and propositions. the complexity and abundancy in sentence meaning renders the evaluation of sentence meaning much more challenging than that of word meaning @xcite . here, inspired by the analysis of word semantic space, we evaluate how well an nlp model captures sentence meaning by constructing a sentence space. each sentence is a point in the vectorial space, and the distance between two sentences represents how strongly the two sentences are related. just like the relationship between two words can be measured based on different features, e.g., animacy, abstractness, and part of speech information, the relationship between two sentences can be measured in many different ways. here, we use the graded entailment score to quantify the relationship between two figure 1 : construction of a semantic space for sentences. we construct a sentence pool, collect pairwise entailment scores to build a distance matrix, and use multidimensional scaling (mds) to visualize the space. each point in the sentence space in the upper right corner represents a sentence, and its color, i.e., rgb channels, illustrates how the sentence relates to 3 basis sentences that are randomly selected from the sentence pool. a portion of the sentence space is zoomed in in the lower panel, in which the font size is proportional to the shortest distance to a basis sentence -larger fonts for sentences that are close to a basis sentence. for this illustration, the 3 basis sentences are: (r) making a marker is a complicated task, even with modern computer assistance; (g) i also like to read things about the civil war; (b) he appointed a new financial manager, whom his followers saw as miserly. semantic entailment, also referred to as the natural language inference (nli) task, is widely used to evaluate the sentence comprehension ability of models @xcite . previous studies, however, mostly focus on the entailment relationship between pairs of sentences that are constructed either manually @xcite @xcite or based on templates @xcite . for example, large datasets such as mnli @xcite and snli @xcite have been constructed by collecting premises from corpora, and asking annotators to construct hypotheses that have the required relationship with the premise (i.e., entailment, contradiction, or neutral). transformers-based language models have achieved high accuracy on mainstream nli datasets @xcite @xcite , but their performance is sensitive to data corruption, distribution shift, and data manipulation @xcite @xcite . consequently, it is of significant interest to design more comprehensive methods to evaluate model performance on the nli task @xcite @xcite @xcite . the ssm proposed here differs from c c l 2 0 2 4 premise hypothesis making a marker is a complicated task, even with modern computer assistance. producing a marker is complex, even with the help of computers. (0.93, 0.83) making a marker is a complicated task, even with modern computer assistance. producing a marker is even trickier without computers. (0.80, 0.39) a fisherman with his friend is setting up his pole. a fisherman and his friend are preparing his fishing rod. (0.7, 0.76) a fisherman with his friend is setting up his pole. the fisherman would like to catch fish. (0.76, 0.33) the procedure to construct a sentence space is illustrated in figure 1 . we constructed a sentence pool in which sentences fall into 50 semantically related clusters (6-11 sentences per cluster), calculated the distance between these sentences, i.e., the graded entailment score, and analyzed the space defined by the distance matrix. an example sentence space is visualized in figure 1 . we applied the method to test 20 language models, as well as human participants for comparison. for a model that well captures sentence meaning, we expect the following three properties. first, short distance should be sparse since most sentence pairs are unrelated. second, semantically related sentences should form clusters. third, the model sentence space should align with the human sentence space. therefore, we use (1) sparsity, (2) clustering, and (3) similarity with human to evaluate a sentence space. ## summary we construct a sentence space to evaluate the sentence interpretation ability of models. the underlying philosophy of the method is that the meaning of a sentence is well described by its relationship with other sentences. here, we use the graded entailment relationship between sentences as a distance measure to construct the sentence space, but the method can be readily used for other sentence similarity or relatedness measures. the evaluation results show that current models, including chatgpt, still do not resemble the human ability to make language-based inference, even when the testing datasets do not involve challenging world knowledge or multi-media information. in sum, by constructing a sentence space and calculating metrics based on the space, we propose a new method that can more comprehensively evaluate sentence processing abilities of language models.