Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations (2024)

Abstract: While large language models have significantly enhanced the effectiveness of discourse relation classifications, it remains unclear whether their comprehension is faithful and reliable. We provide DiSQ, a new method for evaluating the faithfulness of understanding discourse based on question answering. We first employ in-context learning to annotate the reasoning for discourse comprehension, based on the connections among key events within the discourse. Following this, DiSQ interrogates the model with a sequence of questions to assess its grasp of core event relations, its resilience to counterfactual queries, as well as its consistency to its previous responses. then evaluate language models with different architectural designs using DiSQ, finding: (1) DiSQ presents a significant challenge for all models, with the top-performing GPT model attaining only 41% of the ideal performance in PDTB; (2) DiSQ is robust to domain shifts and paraphrase variations; (3) Open-source models generally lag behind their closed-source GPT counterparts, with notable exceptions being those enhanced with chat and code/math features; (4) Our analysis validates the effectiveness of explicitly signalled discourse connectives, the role of contextual information, and the benefits of using historical QA data.

## introduction while language models can generate coherent and seemingly human-like text, their true grasp of discourse relations remains unclear. traditionally, discourse relation prediction has been evaluated using accuracy scores from classification tasks. however, task accuracy may not reflect a reliable understanding, as a high score might not reflect sound reasoning or consistent comprehension of discourse semantics. drawing inspiration from socrates' method of examining his students' understanding through a series of questions, we introduce discursive socratic questioning (disq), a new method discourse relation: contingency.cause.result is "they keep changing their prices" a reason for "it's very frustrating"? model's answer: true is "they keep changing their prices" contrasted with "it's very frustrating"? is "it's very frustrating" the result of "they keep changing their prices"? arg2: it's very frustrating. arg1: when i want to buy, they run from you --they keep changing their prices. that assesses a model's understanding of discourse relations by requiring systematic accuracy over multiple questions, rather than just a single accurate prediction (figure 1 ). while qa-based evaluation is well-researched @xcite , disq addresses unique, discourse-centric challenges: what to ask: while many discourse spans can form questions, not all provide salient insights into discourse understanding. while previous research like e-snli @xcite rely on labor-intensive human annotations to extract relevant signals for natural language inference, we advocate the use of in-context learning (icl). this harnesses the power of large language models to annotate salient discourse signals efficiently. how to ask: the manner in which questions are framed is essential for assessing three key attributes of a model's faithfulness: (1) responsiveness to targets: we generate questions centered on key spans with ground-truth semantics, to which the model should respond affirmatively (e.g., affirming a cause in a contingency relation). (2) robustness to counterfactuals: we design questions based on counterfactual semantics, expecting the model to negate the query (e.g., dismissing contrast in a contingency relation). (3) logical consistency: we formulate converse questions with equivalent semantics, anticipating the model to deliver consistent responses (e.g., aligning answers to a result question with its corresponding reason). @xcite find that llms struggle with the "reversal curse" (finding it hard to infer "b is a" from "a is b"). we introduce this facet as a discourse-centric test to the broader llm research on models' logical consistency. we base our evaluation on the widely-recognized pdtb corpus @xcite . initially, we select 11 second-level discourse senses and employ in-context learning to select salient evidence for questioning. subsequently, we invite human experts to validate our chosen evidence, underscoring the soundness of our questions. we apply disq to a range of models encompassing various architectures and sizes. notably, many models demonstrate zero-shot capabilities, even without training on discourse-specific data. this suggests that the prevailing training paradigms yield emergent ability to understand discourse semantics. we find that while larger, closed-source models excel in responsiveness, they also struggle with the "reversal curse", indicating a probabilistic approach to discourse semantics without full logical consistency. we further demonstrate that disq's measure is robust against domain shifts (ted-mdb corpus @xcite ) and question paraphrasing. we highlight the benefits and limits of using linguistic features like discourse connectives, context, and historical qa to enhance comprehension faithfulness foot_0 . ## question bank for disq we detail "what to ask" in disq by identifying key events and use in-context learning to identify salient evidence. we then perform human verification to guarantee the quality of these questions. ## discursive socratic questioning for evaluation having confirmed the validity of salient events in discourse comprehension, we now consider our measure's core as established. now we outline our systematic approach to "how to ask": generating questions, querying models with these questions, and subsequently computing the scores. ## evaluations we guide our evaluation with following research questions (rqs): rq1: how do models perform on disq's three scores overall? rq2: are disq's scores consistent in different datasets and variations in question phrasing? rq3: what impact do different discourse relations have on model performance? rq4: what linguistic structures can help models improve their performance on disq? datasets: besides pdtb, we also use english sections of the ted-multilingual discourse bank (ted-mdb) dataset, with pdtb-style annotations from ted talks. after preprocessing it in a manner similar to our treatment of the pdtb, we obtain a question bank consisting of 448 discourse instances and 8,376 questions (appendix c.1). models: while any language model can be assessed, our evaluation targets two of the current strongest large language models (llms): (1) closed-sourced models: gpt-4 and gpt-3.5turbo. to manage costs, we limit evaluations to 20% of our test samples (appendix c.5). despite this constraint, we noted stable performance throughout our experiment and relation distributions similar to those of the entire dataset. implementation details: we use a zero-shot approach in our evaluations to mirror real-world conditions. recognizing that smaller models may falter with different instruction templates, we experimented with various templates and report the optimal performances. this method allows us to focus on evaluating the models' ability to answer questions about discourse relation, minimizing the influence of their instruction comprehension skills. respond to a true-or-false question derived from a two-sentence discourse, comprising sentence 1 (sent1) and sentence 2 (sent2), linked by a relationship type like causal, temporal, expansion, contrasting, etc. the question targets two events within this discourse, and your task is to evaluate if these events exhibit the specified relationship. answer with 'true' or 'false' based on your analysis. sent1: "when i want to buy, they run from youthey keep changing their prices." sent2: "it's very frustrating." question: is "it's very frustrating. (event 2)" the result of "hey keep changing their prices (event 1)"? true or false? answer: following zhao et al. (2021), we prompt models with concise instructions, as shown in the example above, followed by the given discourse context and questions. we determine predictions based on the probability of the first token (see appendix c.4). ## related work evaluation methods in nlp: recent approaches for evaluating and interpreting lms in-clude: (1) the probing paradigm takes out the representation of lms and train a model to predict whether one linguistic property is captured by the representation @xcite @xcite . (2) behavior analysis and post-hoc interpretation produce fine-grained interpretation of model's output. the common practice is to perturb the text to reveal the decision boundary or unwanted bias of the model @xcite @xcite . but the creation of the perturbation usually requires manual efforts. discourse modeling and evaluation: (1) discourse modeling: language models (lms) serve as the core for custom neural networks to predict discourse relations @xcite @xcite @xcite . these approaches show improvements over traditional feature-based methods @xcite but lack in interpretability. additionally, lms are applied in coherence modeling @xcite and hierarchical discourse parsing @xcite ), yet they often overlook robustness evaluation, a key contribution of disq. (2) discourse evaluations: recent benchmarks have moved beyond traditional treebanks like pdtb @xcite . discoeval by @xcite assesses sentence embeddings across various discourse tasks. @xcite analyze chatgpt's capabilities in diverse discourse tasks. however, none of these studies focus on the faithfulness aspect of lms. ## conclusion and future work in this paper, we contribute discursive socratic questioning (disq), the first systematic evaluation for faithful discourse comprehension. to ensure the reliability of disq's assessment, we employ both intrinsic verification via human annotation and extrinsic evaluation to demonstrate its resilience against domain shifts and paraphrase variations. our extensive experiments reveal that even leading models like gpt-4 have their shortcomings in disq, and that open-source models -despite trailing gpt-4 -can close this gap with fine-tuning on chat and code/math data. to advance llms' understanding, we suggest incorporating linguistic features such as discourse connectives, contextual information, and historical qa data. in the future, we aim to extend our analysis to longer-range discourse, incorporate additional discourse annotation frameworks beyond pdtb, and distilling knowledge from larger models to benefit smaller models.