Contrastive Learning for Inference in Dialogue (2023)

Abstract: Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker. While recent large language models show remarkable advances in inference tasks, their performance in inductive reasoning, where not all information is present in the context, is far behind deductive reasoning. In this paper, we analyze the behavior of the models based on the task difficulty defined by the semantic information gap – which distinguishes inductive and deductive reasoning. Our analysis reveals that the information gap between dialogue contexts and desired inferences renders the inductive inference process more challenging. To mitigate this information gap, we investigate a contrastive learning approach by feeding negative samples. Our experiments suggest negative samples help models understand what is wrong and improve their inference generations.

## introduction in conversations, inference is essential to uncover what the speaker intended to deliver, which often goes beyond the information explicitly expressed @xcite . inferences can be made by an explicit or implicit logical reasoning based on utterances and common ground among speakers @xcite . by reading between the lines, these inferences enable appropriate responses in dialogues. this inference process has been intensely discussed in the early age of research at dialogues (e.g., @xcite . however, research in dialogue systems nowadays often overlook such an aspect and instead rely solely on the capabilities of large language models (llms) to understand and comprehend dialogues. dial. user a: i'm hungry, let's order up something to eat. user b: ok, maybe we can order a soup and a salad from the restaurant down the street. user a: i was thinking of getting a hamburger, fries, and a chocolate sundae. user b: you eat too much junk food. that sort of stuff clogs up your arteries and is very high in cholesterol. user a: well, i never seem to gain weight, so i don't mind. user b: it's not only about getting fat or not, it's about being healthy. you could really have some health problems later on. @xcite user a: how about pizza or maybe some fried chicken? better yet , let's order some hot dogs! user b: you are a lost cause. what is or could be the prerequisite of the target? ## information gap in inference while existing work focuses on improving the model performance on inference tasks with various methods, there is still a lack of in-depth investigation on the task itself and how the model behavior is changed with the improved results. to fill this gap, we first propose to connect task difficulty with the "information gap" between contexts and target inferences and classify the inference task difficulty into three levels. then, we focus on the generative inference in dialogues with the cicero dataset @xcite . we collect additional annotations to assess the task difficulty of a subset of samples for further analysis. ## methodology we primarily train our model f θ by minimizing the negative log-likelihood: ## conclusion in this paper, we conduct an analysis of inferences in dialogue, focusing on the availability of semantic information between inputs and outputs. as expected, the models perform worse on the samples with larger information gaps. we investigate a contrastive learning approach to teach what is wrong in inference. our experimental results suggest the effectiveness of our approach, showing the promising direction to bridge the information gap, especially for smaller models @xmath0.