Extracting information from customer feedback is a key capability required for identifying current drawbacks and scope for further improvement. Online shoppers routinely provide feedback on their experience with the purchased product that are not just important for the other potential customers but also a critical feedback to the product manufacturers for the next cycle of iteration. Similar feedbacks are available in various other domains ranging from Manufacturing to Healthcare where granular opinions (sentiments) about various dimensions (aspects) of the used product (or service) are available in textual form but need to be understood. Due to the presence of multiple aspects (and the corresponding sentiments) extraction of these aspect-sentiment pairs is a challenging task and since its introduction in 2014 (SemEval-2014 Task-4, Pontiki et al.) Aspect Based Sentiment Analysis (ABSA) attracted various different approaches and is still under active consideration. ABSA demands semantic understanding of the sentence where it is necessary to identify the aspect terms (defining "what") and the opinion terms (defining "why") and the connection between each related pairs (resulting in a positive, neutral or negative sentiment, i.e., "how").

Knowledge graphs (KGs) are large datasets with specific structures representing large knowledge bases (KB) where each node represents a key entity and relations amongst them are typed edges. Natural language queries formed to extract information from a KB entail starting from specific nodes and reasoning over multiple edges of the corresponding KG to arrive at the correct set of answer nodes. Traditional approaches of question answering on KG are based on (a) semantic parsing (SP), where a logical form (e.g., S-expression, SPARQL query, etc.) is generated using node and edge embeddings and then reasoning over these representations or tuning language models to generate the final answer directly, or (b) information-retrieval based that works by extracting entities and relations sequentially. In this work, we evaluate the capability of (LLMs) to answer questions over KG that involve multiple hops. We show that depending upon the size and nature of the KG we need different approaches to extract and feed the relevant information to an LLM since every LLM comes with a fixed context window. We evaluate our approach on six KGs with and without the availability of example-specific sub-graphs and show that both the IR and SP-based methods can be adopted by LLMs resulting in an extremely competitive performance.

In this work, we review research studies that combine Reinforcement Learning (RL) and Large Language Models (LLMs), two areas that owe their momentum to the development of deep neural networks. We propose a novel taxonomy of three main classes based on the way that the two model types interact with each other. The first class, RL4LLM, includes studies where RL is leveraged to improve the performance of LLMs on tasks related to Natural Language Processing. L4LLM is divided into two sub-categories depending on whether RL is used to directly fine-tune an existing LLM or to improve the prompt of the LLM. In the second class, LLM4RL, an LLM assists the training of an RL model that performs a task that is not inherently related to natural language. We further break down LLM4RL based on the component of the RL training framework that the LLM assists or replaces, namely reward shaping, goal generation, and policy function. Finally, in the third class, RL+LLM, an LLM and an RL agent are embedded in a common planning framework without either of them contributing to training or fine-tuning of the other. We further branch this class to distinguish between studies with and without natural language feedback. We use this taxonomy to explore the motivations behind the synergy of LLMs and RL and explain the reasons for its success, while pinpointing potential shortcomings and areas where further research is needed, as well as alternative methodologies that serve the same goal.