Reflective listening is a fundamental communication skill in behavioral health counseling. It enables counselors to demonstrate an understanding of and empathy for clients’ experiences and concerns. Training to acquire and refine reflective listening skills is essential for counseling proficiency. Yet, it faces significant barriers, notably the need for specialized and timely feedback to improve counseling skills. In this work, we evaluate and compare several computational models, including transformer-based architectures, for their ability to assess the quality of counselors’ reflective listening skills. We explore a spectrum of neural-based models, ranging from compact, specialized RoBERTa models to advanced large-scale language models such as Flan, Mistral, and GPT-3.5, to score psychotherapy reflections. We introduce a psychotherapy dataset that encompasses three basic levels of reflective listening skills. Through comparative experiments, we show that a fine-tuned small RoBERTa model with a custom learning objective (Prompt-Aware margIn Ranking (PAIR)) effectively provides constructive feedback to counselors in training. This study also highlights the potential of machine learning in enhancing the training process for motivational interviewing (MI) by offering scalable and effective feedback alternatives for counseling training.

One common approach for question answering over speech data is to first transcribe speech using automatic speech recognition (ASR) and then employ text-based retrieval-augmented generation (RAG) on the transcriptions. While this cascaded pipeline has proven effective in many practical settings, ASR errors can propagate to the retrieval and generation steps. To overcome this limitation, we introduce SpeechRAG, a novel framework designed for open-question answering over spoken data. Our proposed approach fine-tunes a pre-trained speech encoder into a speech adapter fed into a frozen large language model (LLM)–based retrieval model. By aligning the embedding spaces of text and speech, our speech retriever directly retrieves audio passages from text-based queries, leveraging the retrieval capacity of the frozen text retriever. Our retrieval experiments on spoken question answering datasets show that direct speech retrieval does not degrade over the text-based baseline, and outperforms the cascaded systems using ASR. For generation, we use a speech language model (SLM) as a generator, conditioned on audio passages rather than transcripts. Without fine-tuning of the SLM, this approach outperforms cascaded text-based models when there is high WER in the transcripts.

Computational models of psychotherapy often ignore vocal cues by relying solely on text. To address this, we propose MISQ, a framework that integrates speech features directly into language models using a speech encoder and lightweight adapter. MISQ improves behavioral analysis in counseling conversations, achieving ~5% relative gains over text-only or indirect speech methods—underscoring the value of vocal signals like tone and prosody.

Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that “it’s all been solved.” Not surprisingly, this has, in turn, made many NLP researchers – especially those at the beginning of their careers – worry about what NLP research area they should focus on. Has it all been solved, or what remaining questions can we work on regardless of LLMs? To address this question, this paper compiles NLP research directions rich for exploration. We identify fourteen different research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. While we identify many research areas, many others exist; we do not cover areas currently addressed by LLMs, but where LLMs lag behind in performance or those focused on LLM development. We welcome suggestions for other research directions to include: https://bit.ly/nlp-era-llm.

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.

Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is common practice to utilize costly grid-search to find the best configuration for an endpointing model. In this paper, we aim to provide a solution for adaptive endpointing by proposing an efficient method for choosing an optimal endpointing configuration given utterance-level audio features in an online setting, while avoiding hyperparameter grid-search. Our method does not require ground truth labels, and uses only online learning from reward signals. Specifically, we propose a deep contextual multi-armed bandit-based approach, combining the representational power of neural networks with the action exploration behavior of Thomp-son modeling algorithms. We compare our approach to several baselines, and show that our deep bandit models also succeed in reducing early cutoff errors while maintaining low latency.

Reflections are a core verbal skill used by mental health counselors to express understanding and acknowledgement of the client's experience and concerns.In this paper, we propose a system for the automatic evaluation of counselor reflections.Specifically, our system takes as input one dialog turn containing a client prompt likely leading to a reflection and a counselor response to it, and outputs a numeric score indicating the quality of the reflection made by the counselor.We compile a dataset consisting of reflections portraying different levels of reflective listening skills, and propose Prompt-Aware margIn Ranking (PAIR), a novel framework for reflection scoring that contrasts positive and negative prompt and response pairs using adhoc multi-gap and prompt-aware margin ranking losses.Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our scoring model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.