Analog circuit topology synthesis is integral to Electronic Design Automation (EDA), enabling the automated creation of circuit structures tailored to specific design requirements. However, the vast design search space and strict constraint adherence make efficient synthesis challenging. Leveraging the versatility of Large Language Models (LLMs), we propose AUTOCIRCUIT-RL, a novel reinforcement learning (RL)-based framework for automated analog circuit synthesis. The framework operates in two phases: instruction tuning, where an LLM learns to generate circuit topologies from structured prompts encoding design constraints, and RL refinement, which further improves the instruction-tuned model using reward models that evaluate validity, efficiency, and output voltage. The refined model is then used directly to generate topologies that satisfy the design constraints. Empirical results show that AUTOCIRCUIT-RL generates ~12% more valid circuits and improves efficiency by ~14% compared to the best baselines, while reducing duplicate generation rates by ~38%. It achieves over 60% success in synthesizing valid circuits with limited training data, demonstrating strong generalization. These findings highlight the framework's effectiveness in scaling to complex circuits while maintaining efficiency and constraint adherence, marking a significant advancement in AI-driven circuit design.

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite their impressive capabilities, LLMs have been shown to reflect and perpetuate harmful societal biases, including those based on ethnicity, gender, and religion. A critical and underexplored issue is the reinforcement of caste-based biases, particularly towards India's marginalized caste groups such as Dalits and Shudras. In this paper, we address this gap by proposing DECASTE, a novel, multi-dimensional framework designed to detect and assess both implicit and explicit caste biases in LLMs. Our approach evaluates caste fairness across four dimensions: socio-cultural, economic, educational, and political, using a range of customized prompting strategies. By benchmarking several state-of-the-art LLMs, we reveal that these models systematically reinforce caste biases, with significant disparities observed in the treatment of oppressed versus dominant caste groups. For example, bias scores are notably elevated when comparing Dalits and Shudras with dominant caste groups, reflecting societal prejudices that persist in model outputs. These results expose the subtle yet pervasive caste biases in LLMs and emphasize the need for more comprehensive and inclusive bias evaluation methodologies that assess the potential risks of deploying such models in real-world contexts.

Narrative is a ubiquitous component of human communication. Understanding its structure plays a critical role in a wide variety of applications, ranging from simple comparative analyses to enhanced narrative retrieval, comprehension, or reasoning capabilities. Prior research in narratology has highlighted the importance of studying the links between cognitive and linguistic aspects of narratives for effective comprehension. This interdependence is related to the textual semantics and mental language in narratives, referring to characters' motivations, feelings or emotions, and beliefs. However, this interdependence is hardly explored for modeling narratives. In this work, we propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state along with linguistic information at the syntactic and semantic levels. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. To this end, we implement a computational model that leverages the protagonist's mental state information obtained from a pre-trained model trained on social commonsense knowledge and integrates their representations with contextual semantic embed-dings using a multi-feature fusion approach. Evaluating against prior zero-shot and supervised baselines, we find that our model is able to achieve significant improvements in the task of identifying climax and resolution.

Recently, several studies on propaganda detection have involved document and fragmentlevel analyses of news articles.However, there are significant data and modeling challenges dealing with fine-grained detection of propaganda on social media.In this work, we present TWEETSPIN, a dataset containing tweets that are weakly annotated with different fine-grained propaganda techniques, and propose a neural approach to detect and categorize propaganda tweets across those fine-grained categories.These categories include specific rhetorical and psychological techniques, ranging from leveraging emotions to using logical fallacies.Our model relies on multi-view representations of the input tweet data to ( a) extract different aspects of the input text including the context, entities, their relationships, and external knowledge; (b) model their mutual interplay; and (c) effectively speed up the learning process by requiring fewer training examples.Our method allows for representation enrichment leading to better detection and categorization of propaganda on social media.We verify the effectiveness of our proposed method on TWEETSPIN and further probe how the implicit relations between the views impact the performance.Our experiments show that our model is able to outperform several benchmark methods and transfer the knowledge to relatively low-resource news domains.

The ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life.Several studies in cognitive psychology have established the role of mental state attributions in effectively representing variable aspects of these social events.In the past, NLP research on learning event representations often focuses on construing syntactic and semantic information from language.However, they fail to consider the importance of pragmatic aspects and the need to consistently update new social situational information without forgetting the accumulated experiences.In this work, we propose a representation learning framework to directly address these shortcomings by integrating social commonsense knowledge with recent advancements in the space of lifelong language learning.First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge.Next, we introduce continual learning strategies that allow for incremental consolidation of new knowledge while retaining and promoting efficient usage of prior knowledge.Experimental results on event similarity, reasoning, and paraphrase detection tasks prove the efficacy of our social event embeddings.

The ability to automatically understand and infer characters’ motivations and emotional states is key to better narrative comprehension. In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about the mental states of people related to social events and employ dynamic state tracking of entities using an augmented memory module. Our model learns to produce contextual embeddings and explanations of characters’ mental states by integrating external knowledge along with prior narrative context and mental state encodings. We leverage weakly-annotated personal narratives and knowledge data to train our model and demonstrate its effectiveness on publicly available dataset containing annotations for character mental states. Further, we show that the learned mental state embeddings can be applied in downstream tasks such as empathetic response generation.

The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation.We introduce neural models to learn persona embeddings in a supervised character trope classification task.The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters.The bestperforming models use a multi-level attention mechanism over a set of utterances.We also utilize prior knowledge in the form of textual descriptions of the different tropes.We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings.The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.

Affective virtual spaces are of interest in many virtual reality applications such as education, wellbeing, rehabilitation, and entertainment. In this paper we present Auris, a system that attempts to generate affective virtual environments from music. We use music as input because it inherently encodes emotions that listeners readily recognize and respond to. Creating virtual environments is a time consuming and labor-intensive task involving various skills like design, 3D modeling, texturing, animation, and coding. Auris helps make this easier by automating the virtual world generation task using mood and content extracted from song audio and lyrics data respectively. Our user study results indicate virtual spaces created by Auris successfully convey the mood of the songs used to create them and achieve high presence scores with the potential to provide novel experiences of listening to music.

We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages.