Can probing classifiers reveal the learning by contact center large language models?: No, it doesn’t! (2024)

Abstract: Fine-tuning large language models (LLMs) with domain-specific instruction dataset has emerged as an effective method to enhance their domain-specific understanding. Yet, there is limited work that examines the core characteristics acquired during this process. In this study, we benchmark the fundamental characteristics learned by contact-center (CC) domain specific instruction fine-tuned LLMs with out-of-the-box (OOB) LLMs via probing tasks encompassing conversational, channel, and automatic speech recognition (ASR) properties. We explore different LLM architectures (Flan-T5 and Llama) and sizes (3B, 7B, 11B, 13B). Our findings reveal remarkable effectiveness of CC-LLMs on the in-domain downstream tasks, with improvement in response acceptability by over 48% compared to OOB-LLMs. However, we observe that the performance of probing classifiers are relatively similar and does not reflect the performance of in-domain downstream tasks. A similar observation is also noted on SentEval dataset that assess capabilities of models in terms of surface, syntactic, and semantic information through probing tasks. Our study challenges the premise that probing classifiers can reveal the fundamental characteristics learned by large language models and is reflective of the downstream task performance, via a case-study of LLMs tuned for contact center domain.

## introduction and related works large language models (llms) have made significant strides in recent years, with their ability to generate fluent text on variety of inputs @xcite . the strategy of fine-tuning the general-purpose models with domain-specific data has led to performance improvements in domains with llms such as biogpt @xcite and med-palm @xcite in biomedical research, codet5 @xcite , codellama in coding @xcite , and bloomberg-gpt @xcite in finance, demonstrating the need and advantage of domain specific fine-tuning of llms. however, one domain that has received relatively little attention is the contact center industry. contact centers play a crucial role in customer service and support for various businesses. they address a broad spectrum of customer queries, from technical issues to billing concerns. incorporating llms into contact center workflows have a potential to transform the sector. however, noisy queries, spontaneous conversational dynamics and domain specific understanding pose significant challenges for llms. adapting to these nuances is crucial for llms to enhance their effectiveness in contact center. instruction fine-tuning @xcite ) has emerged as one the promising approaches to develop domain-specific llms. assessing effectiveness of llms often involves evaluating their performance on specific downstream tasks. however, probing the representations of the models on different probing tasks provide a deeper insight into the fundamental aspects of what language models capture and learn @xcite . these tasks have been instrumental in understanding the underlying characteristic of language models. @xcite introduced probing tasks in senteval to assess sentence embedding representations of language models. following this, studies like those by @xcite and @xcite have applied layer-wise probing to bert, shedding light on its semantic and hierarchical processing capabilities. while the majority of probing studies have concentrated on general lms, work by @xcite delved into the representation capabilities of roberta in contact center domain. building on this foundation, our study seeks to further understand the intricacies of instruction-fine-tuned llms in contact centers through specific research questions, aiming to uncover how these llms adapt and learn within this specialized context: • rq1: how effective is instruction fine-tuning in enhancing llms' performance on downstream tasks within the contact-center domain? in this work, we train a contact center-specific large language model (cc-llm) using a proprietary dataset of asr transcripts 1 from various sectors. through instruction fine-tuning, we adapt out-ofbox (oob) llms to the contact center conversations, characterized by multi-party interactions, disfluencies, and asr errors. our training methodology involves generating diverse instructions for a wide array of tasks, such as call summarization, dialog question answering etc., to tailor the model's capabilities for contact center applications. more details is mentioned in section a.1. ## probing tasks probing tasks tailored to the contact center domain provide valuable insights into the capabilities and 1 we cannot release the dataset due to proprietary reasons. ## implementation details we compare two model classes, namely flan @xcite and llama @xcite gorizing the responses generated by each of them into one among following seven classes: extremely good, very good, good, acceptable, bad, very bad, and extremely bad. the annotation process in detail is mentioned in section a.3. we analyze the responses generated by the llm groups, and observe significant difference in the distribution of quality of responses (refer figure 1 ). specifically, responses generated by oob-t5 (11b) (raffel et al., 2020), oob-flan-t5 (11b), oob-llama (13b) and oob-llama-instruct (13b) models are consistently skewed towards the lower end of the quality spectrum. a majority of these responses fall within the bad to extremely bad categories, indicating that without specific fine-tuning, oob models struggle to generate satisfactory responses for contact center specific instructions. conversely, responses generated by cc-flan-t5 (11b) and cc-llama (13b) models exhibit a notable shift towards higher quality categories. a substantial portion of responses generated by these models lands in the acceptable to extremely good range, demonstrating their ability to comprehend and generate contextually relevant responses for contact center interactions. specifically, 91% of responses from cc-flan-t5 and 87% of responses from cc-llama has @xmath0. this improvement in performance can be attributed to the fine-tuning process with contact center data. ## conclusion our study contributes to the growing body of research on fine-tuning llms with domain-specific instructions. in this work, we demonstrate that cc-llms, cc-flan-t5 and cc-llama, exhibit superior performance on downstream tasks within the contact center domain. this finding reinforces the effectiveness of fine-tuning llms with domainspecific instructions, as expected. however, our comparison between oob and cc models on the probing task reveals intriguing and unexpected observations. while the performance of cc-llms are much superior to the oob-llms on downstream tasks, the performance of probing classifiers across the models shows no substantial differences. this questions the efficacy of traditional probing mechanisms and probing tasks in understanding the llms. we also observe that the decoder model (llama-13b) consistently underperforms compared to the lower sized encoder-decoder model (flan-11b) in all experiments this prompts more research into the learning dynamics of these architectures.