Evaluating the Effectiveness of Large Language Models in Establishing Conversational Grounding (2024)

Abstract: Conversational grounding, vital for building dependable dialog systems, involves ensuring a mutual understanding of shared information. Despite its importance, there has been limited research on this aspect of conversation in recent years, especially after the advent of Large Language Models (LLMs). Previous studies have highlighted the shortcomings of pre-trained language models in conversational grounding. However, most testing for conversational grounding capabilities involves human evaluations that are costly and time-consuming. This has led to a lack of testing across multiple models of varying sizes, a critical need given the rapid rate of new model releases. This gap in research becomes more significant considering recent advances in language models, which have led to new emergent capabilities. In this paper, we aim to evaluate the performance of LLMs in various aspects of conversational grounding and analyze why some models perform better than others. We demonstrate a direct correlation between the size of the pre-training data and conversational grounding abilities, meaning that they have independently acquired a specific form of pragmatic capabilities from larger pre-training datasets. Finally, we propose ways to enhance the capabilities of the models that lag in this aspect.

## introduction the concept of "common ground" in linguistics, introduced by @xcite , refers to the collective body of knowledge and assumptions that conversation participants build together. this shared understanding is not solely formed through words; it also incorporates other modalities, as highlighted by nakano et al. @xcite , such as gestures, nods, and eye contact. these non-verbal cues are crucial in creating and maintaining the common ground in face-to-face dialogues. conversational grounding is this process of building common ground, and involves continuous negotiation, and resolution of uncertainty. these uncertainties may be addressed by providing additional context -for example, specifying the object "the small gate next to the bakery" or through the listener seeking clarification, like asking, "you mean the white gate?". since these agreements are not always explicitly expressed, participants must be adept at recognizing cues of (mis-)understanding from their interlocutors. conversational grounding is complex due to the dynamic nature of spontaneous dialogues, which demand more than just grammatical correctness, but also pragmatics -how language is used. an effective grounding mechanism is vital to dialogue systems in order to reduce ambiguity both when the system is the speaker, by adding more information if it senses confusion from the listener, and as listener, by asking for clarifications when necessary. @xcite have previously shown that state-of-the-art language models pre-trained on large amounts of conversational data like blenderbot 1 @xcite frequently fall short in ensuring that information has been adequately grounded. while they identified these deficiencies, their work didn't provide a comprehensive framework for evaluating multiple different models on their grounding capabilities. in addition, their findings were based on limited interactions with smaller models such as blender-bot 1, trained on smaller amount of data compared to more recent and advanced llms like llama @xcite and gpt4 @xcite . while conversational grounding can be a multimodal phenomenon, in this paper, we start by evaluating the performance of state-of-the-art language models on textual inputs, since current dialog systems convert speech to text before sending it as input to language models for nlu, nlg, and in some cases dialog management itself. the out-puts are later converted into speech using separate modules. because of their growing significance in the field of dialogue systems, we aim here to assess and enhance pre-trained llms' capabilities in the various kinds of conversational grounding. this will lay the groundwork for more advanced modular spoken dialog systems with multimodal input and output in the future. to this end, we have devised a series of tests to evaluate llms. we evaluate how they handle grounding both as speaker and listener. when the models act as a listener, we test if they can ground information that was repaired or cancelled by the speaker, and we test if they can ask questions in case of important uncertainties. when the models act as speaker, we test if they can recognize and provide correct information to reduce ambiguity. our approach involves analyzing the model perplexity of two hand-crafted responses for a given context: one being contextually appropriate and the other deceptively fitting but contextually incorrect. by comparing the perplexities of these responses, we gauge the model's proficiency in specific grounding scenarios. our findings indicate a correlation between model performance, its size in terms of parameters and the amount of data that it was pretrained on. consequently, we conduct novel tests to explore the reasons behind the under-performance of smaller models, focusing specifically on their embedding vectors. the insights gained from these investigations are then utilized to explore methods for enhancing the performance of these smaller models in conversational grounding tasks. ## related work in the field of linguistics, @xcite explored the inherent uncertainty present in dialogue, which interlocutors negotiate and resolve, to the extent necessary for that stage of the dialogue. clark identified four distinct states of uncertainty: 1) b didn't notice that a uttered any utterance u. the field of conversational systems has seen various efforts aimed at addressing the challenges of grounding, particularly in the context of rulebased modular dialog systems. the pioneering work originated with @xcite , which introduced the concept of grounding acts (gas). it serves as a framework for breaking down the grounding process into its fundamental units. within this framework, they define 7 categories of gas: initiate, continue, acknowledge, repair, request-repair, request-acknowledge, and cancel. the framework also introduced the concept of 'grounding unit', a group of utterances providing a piece of information starting with an 'initiate' and then adding to the common ground with an 'acknowledgment'. other gas help in negotiating this information before adding to the common ground. while this concept offers a solid foundation for understanding and modeling conversational grounding, its application has been limited in the context of contemporary llms. @xcite have recently expanded on the challenges associated with annotating and modeling grounding acts within dialogue systems. subsequent approaches, such as centering theory @xcite and domain reference theory @xcite , introduced techniques for representing and managing grounded information. their applicability, however, was largely limited to closed domains, primarily owing to their reliance on rule-based approaches. @xcite focused on reference-centric models by leveraging the success of recent artificial neural networks. however, it was designed for the specific multimodal reference task in the onecommon dataset (udagawa and aizawa, 2019) using an end-to-end neural-network based model. recent research on generative agents has highlighted the potential of llms in interactive settings. @xcite studied the creation of multiple agents, each assigned an initial identity. these agents were equipped with a memory module and relied on llms to assess the significance of various memories. the study demonstrated their ability to plan relevant events and execute them through human-like interactions. however, it's important to note that this research was conducted in a virtual environment with artificial agents, which does not fully replicate the scenario of human-human or human-agent spontaneous conversations. while this work sheds light on the potential capabilities of llms, and is contrary to the results of previous work like @xcite , further investigation is warranted in assessing their effectiveness in handling various grounding phenomena in natural conversations. ## dataset several datasets have been curated to support research on conversational grounding. talk the walk (de vries et al., 2018) created a virtual 2d grid en-vironment, while the hcrc maptask @xcite had participants discuss directions using a map that, in fact, differed between the two participants, forcing them to negotiate what they were referring to at any given moment. these conversations helped in the development of early theories and models for grounding. after assessing the existing datasets, we opted to employ the meetup dataset @xcite to generate our test cases. this choice was made due to the nature of the task that required participants to negotiate instances of referring expressions. the meetup dataset introduced a scenario wherein two participants are placed on a 2d grid, with each vertex representing a room. the objective is for the two participants to converge in the same room, despite only being able to see their own rooms. navigational actions (east, west, north, or south) move participants to new rooms, unveiling the image of the newly entered room to them. achieving the goal necessitates the articulation of room descriptions, formulation and communication of a converging strategy, retention of room descriptions shared by the counterpart, and mental modeling of the other participant's room configurations. although the dataset is text-based, it serves as a great resource for exploring and developing grounding models. unlike many tasks that attribute a leading role to one participant, in this task, both participants can assume initiator or responder roles interchangeably. these features made the dataset ideal for our purposes. the ga annotated version of the meetup dataset provided by @xcite was used for creating testing cases for model evaluation. ## models in order to examine llms of varying sizes and pre-trained on varying amounts of data, we decided to test t5-large @xcite , godel-large @xcite , llama-1(7 & 13 billion) @xcite , gpt 3.5(openai, 2022) and gpt 4 (openai, 2023). t5 is an encoder-decoder-based transformer model, while godel, builds upon t5 with additional fine-tuning for conversational applications. both models possess 770 million parameters and are not trained on a lot of pre-trained data. llama and the gpt models, in contrast, are decoder-based transformer models trained on vast amount of data. for t5, godel, llama(7b, 13b), access to the models allowed for additional fine-tuning using causal language mod-instructions : here is a conversation between two participants ........ to both participants. following is the dialog history along with image descriptions: <image @xmath0............. is located near the table. here, w represents the sequence of words w 1 , . . . , w n , n is the length of the word sequence, and p (w i |w 1 , . . . , w i-1 ) is the probability of each word. a lower perplexity indicates a higher chance of the model accurately generating the sequence. we evaluate the models' ability to ground the conversations in two contexts -when they act as a listener and when as a speaker. to assess the llm's grounding ability as a listener to consider modifications from the speaker, we test it on situations containing the gas -repair and cancel. we also test if it can detect ambiguities in the speaker's utterances and respond accordingly. while acting as a speaker, we test them on another type of ga -request repair, where they have to provide further relevant information when requested by the listener, in order to help in grounding. we also look at request-repairs that are of the yes-no question type where the models tend to make contextual mistakes. finally, we examined instances of complex anaphoric references made by the listener while asking for clarification. these references have to be disambiguated before responding to the requests for clarification. to evaluate each phenomenon, we use the grounding act annotations of the meetup corpus provided by @xcite . then we pick 20 instances of each phenomenon and introduce slight modifications to create test cases. we pick only 20 instances because some scenarios, such as cancel, occur only 20 times, and we want equal numbers of instances for each phenomenon. as described below, the results nevertheless show a clear trend in the performance of different models. after identifying test cases, we create a correct and incorrect response for the context and analyze the model perplexity for those examples, as illustrated in figure 2 . ideally, the perplexity of the correct response should be lower than the incorrect response. here we provide a detailed discussion of the test case creation process for each category -1. reference ambiguity refers to instances where the referring expression is ambiguous. we test if the model is able to ask for clarifications in such cases of uncertainty, as seen in figure 2 . examples of remaining categories can be found in section a.1 of appendix 2. repair is the correction of previously uttered material or the addition of omitted material that will change the listener's interpretation of the speaker's intention. we check if the model can correctly take the repair from the speaker into account. the correct response accounts for the repaired information from the speaker while the wrong response contains the older information. the model's ability to correctly take into account the repaired information shows its grounding abilities. ## results however, based on the performance of the models, there was a question that was not yet answered -is the performance increase solely because of the increasing parameters or does the amount of pretraining data also play a role in their performance? to address this, we tested llama 3.1-8b model which is similar in size to llama-7b but pre-trained on much more data. as tables 2 and 3 show, llama 3.1-8b outperforms llama-7b in almost all the categories while competing with llama-13b which has significantly higher parameters. this is in line with the latest findings @xcite . however, it does not surpass llama-13b despite being trained on a much larger amount of data, highlighting the importance of both the pre-training data volume and the number of parameters in enhancing the model's understanding of pragmatics. d1 with repair user a: it is overlooking the garden, with yellow seat user b: yellow seat? user a: sorry yellow table user a: do you want me to find you or you to find me? user b: i'll look for you d2 (paraphrased from d1 without repair) user a: it is overlooking the garden, with yellow table user a: do you want me to find you or you to find me? user b: i'll look for you d3 (paraphrased from d2) user a: with a garden view, there is a yellow table user a: do you want me to search for you or for you to search for me? user b: i will search for you. d4 (with wrong information) user a: it is overlooking the garden, with yellow seat user a: do you want me to search for you or for you to search for me? user b: i will search for you. responses indicated by the perplexity. however, the models show similar trends on both the perplexity and prompt testing i.e. the smaller models perform better on categories like anaphoric references and reference ambiguities as compared to repair, request-repair and cancel. similarly, larger models tend to outperform smaller models with llama-13b being an exception over gpt 3.5 due to the amount of pre-training data used to train llama models over an older gpt 3.5. while the result suggests that bigger models trained on larger pre-training data outperform other models, the utilization of these large models may not be optimal for every dialogue system, given their increased latency, higher memory constraints and higher cost per inference. consequently, this prompted an investigation into the reasons behind the less effective performance of smaller and medium-sized models. ## embedding study to gain deeper insights, we developed a novel method to analyze how these models process utterances at the embedding level. for this purpose, four instances of the same dialogue were generated. figure 4 illustrates a test case encompassing d1, d2, d3, and d4. owing to the inherent characteristics of reference uncertainty, they were not examined at the embedding level in the current investigation. for more information on how d2 test cases were created from d1 for each phenomenon, please look at the appendix. the study focused on the spatial distance between the embeddings of different instances of the dialogues. ideally, the first three dialogues (d1, d2, d3) would have close proximity in the embedding space, while d4 should be distinctly separated. this was assessed by analyzing the distances between the hidden representations of the final encoding layer of each model for each instance. essentially, this evaluated whether the dialogue d1 containing the grounding phenomenon, bore more similarity to d3 or d4, in terms of their respective distances from d2. if the model exhibits capabilities of understanding these grounding phenomena, the distance between d1 and d2 should be akin to that between d2 and d3; otherwise, if it considers it to be not similar to d2 and d3 then it would more closely resemble the distance between d2 and d4 i.e. it will treat it more like a random dialog as depicted by figure 3 . we calculate a score v of the embeddings based on the formula in equation 2. here, v1,v2,v3 and v4 are the mean final layer embeddings of d1,d2,d3 and d4. the score calculates the distance (dist) between v1,v2 and compares it with the distance of v3,v2 and v4,v2. if d1 is considered closer to d3 then the dist(v1, v2) should be similar to the dist(v3,v2) and much smaller than the dist(v4, v2) leading to a larger score. otherwise, if the d1 is considered more like d4 then the numerator will be smaller and the denominator will be larger leading to a smaller score. ideally, we would like our models to provide higher scores for each category. the data presented in ## positive and negative reward training based on the embedding testing analysis, we realized that the models need to be able to distinguish at the embedding level between dialogs that sound the same but mean very different. hence, we decided to create additional cases for each categories and finetune the models using positive and negative reward training (sutton and barto, 2018). as seen in equation 3 , this approach involved rewarding the model for correctly identifying suitable responses i.e. reduce the loss of correct response (loss_correct), while penalizing it for selecting incorrect utterances in the same context i.e. increase the loss of the wrong response (loss_wrong). both the correct and wrong losses are obtained using cross-entropy loss with the entire context as input and the correct and wrong responses as outputs. here w1 and w2 are hyper-parameters empirically set as @xmath1.5. recognizing gpt 4's superior performance in our evaluations, and the need for more diverse category instances in our dataset, we utilized gpt 4 to generate 100 additional training data by feeding it examples from every category. however, it was noted that gpt 4 had limitations in creating complex cases, necessitating manual modifications to improve their quality. tables 6 and 7 show the improvement in the performance of llama-7b, llama-13b and their clm fine-tuned version after the positive-negative reward training over their previous performance in tables 2 and 3 . llama 3.1-8b also showed a slight improvement in performance. however, the smaller models t5 and godel were not able to improve their performance indicating a role of model size and pre-training on extensive data that leads to their ability to learn newer concepts. this indicates that a complex concept like grounding is difficult to achieve with models like t5-large even after fine-tuning. conversely, a model akin to llama's size pre-trained on large data can be trained for better grounding performance, though it may not match the proficiency of a larger model like gpt-4 leading to a trade-off between performance and computations performed. ## conclusion and future work in this study, we developed a benchmark aimed at assessing the effectiveness of llms in natural conversations, with a focus on conversational grounding. our observations revealed a direct correlation between model size, the size of their pre-training data and performance, highlighting the possibility of emergent properties in llms leading to the addressing of conversational grounding in dialogs, unlike the previous findings of @xcite . additionally, we introduced a method to investigate the limited performance of smaller models by examining the embeddings from four altered versions of the same dialogue indicating the emphasis on lexical content by smaller models over pragmatics. building on these insights, we generated new training data to employ positivenegative reward techniques resulting in improved performance of medium-sized models. while they still do not match the performance of larger models, the findings indicate that, with specific training, medium-sized models could potentially replace larger models in real-time systems where there is a need to balance performance and computation(causing latency). our observations indicated that, while, fine-tuning with causal language modeling over the meetup dataset did not improve the performance, using positive-negative rewards significantly enhanced the pragmatic capabilities of the model. future work will further investigate techniques like dpo @xcite and rlhf @xcite for this purpose. it will also focus on integrating multimodal inputs and outputs in language models, recognizing their vital role in grounding.